wip

This package provides a way to convert JSON schemas to equivalent EBNF.
It is intended to be a replacement to llama.cpp's schema_to_grammar.

This is still an early version and does not yet support all JSON schema
features. The to-do list includes:

- minumum/maximum constraints on integer types
- minLength/maxLength constraints on string types
- defs and refs

CMakeLists.txt

@@ -23,6 +23,7 @@ set(GGML_SCHED_MAX_COPIES 4)
 set(GGML_LLAMAFILE ON)
 set(GGML_CUDA_PEER_MAX_BATCH_SIZE 128)
 set(GGML_CUDA_GRAPHS ON)
+set(GGML_CUDA_FA ON)
 
 if((CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
     OR (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_SYSTEM_PROCESSOR MATCHES "arm|aarch64|ARM64|ARMv[0-9]+"))
@@ -105,9 +106,11 @@ if(CMAKE_HIP_COMPILER)
         add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-hip)
 
         if (WIN32)
-            target_compile_definitions(ggml-hip PRIVATE GGML_CUDA_NO_PEER_COPY=1)
+            target_compile_definitions(ggml-hip PRIVATE GGML_CUDA_NO_PEER_COPY)
         endif()
 
+        target_compile_definitions(ggml-hip PRIVATE GGML_HIP_NO_VMM)
+
         set(OLLAMA_HIP_INSTALL_DIR ${OLLAMA_INSTALL_DIR}/rocm)
         install(TARGETS ggml-hip
             RUNTIME_DEPENDENCIES

CMakePresets.json

@@ -28,7 +28,7 @@
       "name": "CUDA 12",
       "inherits": [ "CUDA" ],
       "cacheVariables": {
-        "CMAKE_CUDA_ARCHITECTURES": "50;60;61;70;75;80;86;87;89;90;90a;100"
+        "CMAKE_CUDA_ARCHITECTURES": "50;60;61;70;75;80;86;87;89;90;90a;120"
       }
     },
     {
@@ -56,7 +56,7 @@
       "name": "ROCm 6",
       "inherits": [ "ROCm" ],
       "cacheVariables": {
-        "AMDGPU_TARGETS": "gfx900;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-"
+        "AMDGPU_TARGETS": "gfx900;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102;gfx1151;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-"
       }
     }
   ],

CONTRIBUTING.md

@@ -6,8 +6,6 @@ Thank you for your interest in contributing to Ollama! Here are a few guidelines
 
 See the [development documentation](./docs/development.md) for instructions on how to build and run Ollama locally.
 
-## Pull requests
-
 ### Ideal issues
 
 * [Bugs](https://github.com/ollama/ollama/issues?q=is%3Aissue+is%3Aopen+label%3Abug): issues where Ollama stops working or where it results in an unexpected error.
@@ -26,11 +24,64 @@ See the [development documentation](./docs/development.md) for instructions on h
 * Changes that add significant friction to the user experience
 * Changes that create a large future maintenance burden for maintainers and contributors
 
-### Best practices
+## Proposing a (non-trivial) change
 
-* Commit messages: please leave both a title and a description in your commit messages. The title should be a short summary of the changes, with a leading word that explains the section of the code being changed (e.g. `api: fix parsing of prompt field`) . In the description, leave a short 2-3 sentences that explain more about the change and its impact.
-* Tests: please add test coverage to changes where possible.
-* Minimize dependencies: avoid adding new dependencies unless absolutely necessary.
+> By "non-trivial", we mean a change that is not a bug fix or small
+> documentation update. If you are unsure, please ask us on our [Discord
+> server](https://discord.gg/ollama).
+
+Before opening a non-trivial Pull Request, please open an issue to discuss the change and
+get feedback from the maintainers. This helps us understand the context of the
+change and how it fits into Ollama's roadmap and prevents us from duplicating
+work or you from spending time on a change that we may not be able to accept.
+
+Tips for proposals:
+
+* Explain the problem you are trying to solve, not what you are trying to do.
+* Explain why the change is important.
+* Explain how the change will be used.
+* Explain how the change will be tested.
+
+Additionally, for bonus points: Provide draft documentation you would expect to
+see if the change were accepted.
+
+## Pull requests
+
+**Commit messages**
+
+The title should look like:
+
+   <package>: <short description>
+
+The package is the most affected Go package. If the change does not affect Go
+code, then use the directory name instead. Changes to a single well-known
+file in the root directory may use the file name.
+
+The short description should start with a lowercase letter and be a
+continuation of the sentence:
+
+      "This changes Ollama to..."
+
+Examples:
+
+      llm/backend/mlx: support the llama architecture
+      CONTRIBUTING: provide clairity on good commit messages, and bad
+
+Bad Examples:
+
+      feat: add more emoji
+      fix: was not using famous web framework
+      chore: generify code
+
+**Tests**
+
+Please include tests. Strive to test behavior, not implementation.
+
+**New dependencies**
+
+Dependencies should be added sparingly. If you are adding a new dependency,
+please explain why it is necessary and what other ways you attempted that
+did not work without it.
 
 ## Need help?
 

Dockerfile

@@ -12,7 +12,7 @@ FROM --platform=linux/amd64 rocm/dev-almalinux-8:${ROCMVERSION}-complete AS base
 RUN yum install -y yum-utils \
     && yum-config-manager --add-repo https://dl.rockylinux.org/vault/rocky/8.5/AppStream/\$basearch/os/ \
     && rpm --import https://dl.rockylinux.org/pub/rocky/RPM-GPG-KEY-Rocky-8 \
-    && dnf install -y yum-utils ccache gcc-toolset-10-gcc-10.2.1-8.2.el8 gcc-toolset-10-gcc-c++-10.2.1-8.2.el8 \
+    && dnf install -y yum-utils ccache gcc-toolset-10-gcc-10.2.1-8.2.el8 gcc-toolset-10-gcc-c++-10.2.1-8.2.el8 gcc-toolset-10-binutils-2.35-11.el8 \
     && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
 ENV PATH=/opt/rh/gcc-toolset-10/root/usr/bin:$PATH
 
@@ -86,10 +86,11 @@ RUN --mount=type=cache,target=/root/.ccache \
         && cmake --install build --component CUDA --strip --parallel 8
 
 FROM base AS build
-ARG GOVERSION=1.23.4
-RUN curl -fsSL https://golang.org/dl/go${GOVERSION}.linux-$(case $(uname -m) in x86_64) echo amd64 ;; aarch64) echo arm64 ;; esac).tar.gz | tar xz -C /usr/local
-ENV PATH=/usr/local/go/bin:$PATH
 WORKDIR /go/src/github.com/ollama/ollama
+COPY go.mod go.sum .
+RUN curl -fsSL https://golang.org/dl/go$(awk '/^go/ { print $2 }' go.mod).linux-$(case $(uname -m) in x86_64) echo amd64 ;; aarch64) echo arm64 ;; esac).tar.gz | tar xz -C /usr/local
+ENV PATH=/usr/local/go/bin:$PATH
+RUN go mod download
 COPY . .
 ARG GOFLAGS="'-ldflags=-w -s'"
 ENV CGO_ENABLED=1

README.md

@@ -1,5 +1,5 @@
 <div align="center">
-  <a href="https://ollama.com" />
+  <a href="https://ollama.com">
     <img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
   </a>
 </div>
@@ -54,6 +54,11 @@ Here are some example models that can be downloaded:
 
 | Model              | Parameters | Size  | Download                         |
 | ------------------ | ---------- | ----- | -------------------------------- |
+| Gemma 3            | 1B         | 815MB | `ollama run gemma3:1b`           |
+| Gemma 3            | 4B         | 3.3GB | `ollama run gemma3`              |
+| Gemma 3            | 12B        | 8.1GB | `ollama run gemma3:12b`          |
+| Gemma 3            | 27B        | 17GB  | `ollama run gemma3:27b`          |
+| QwQ                | 32B        | 20GB  | `ollama run qwq`                 |
 | DeepSeek-R1        | 7B         | 4.7GB | `ollama run deepseek-r1`         |
 | DeepSeek-R1        | 671B       | 404GB | `ollama run deepseek-r1:671b`    |
 | Llama 3.3          | 70B        | 43GB  | `ollama run llama3.3`            |
@@ -64,10 +69,7 @@ Here are some example models that can be downloaded:
 | Llama 3.1          | 8B         | 4.7GB | `ollama run llama3.1`            |
 | Llama 3.1          | 405B       | 231GB | `ollama run llama3.1:405b`       |
 | Phi 4              | 14B        | 9.1GB | `ollama run phi4`                |
-| Phi 3 Mini         | 3.8B       | 2.3GB | `ollama run phi3`                |
-| Gemma 2            | 2B         | 1.6GB | `ollama run gemma2:2b`           |
-| Gemma 2            | 9B         | 5.5GB | `ollama run gemma2`              |
-| Gemma 2            | 27B        | 16GB  | `ollama run gemma2:27b`          |
+| Phi 4 Mini         | 3.8B       | 2.5GB | `ollama run phi4-mini`           |
 | Mistral            | 7B         | 4.1GB | `ollama run mistral`             |
 | Moondream 2        | 1.4B       | 829MB | `ollama run moondream`           |
 | Neural Chat        | 7B         | 4.1GB | `ollama run neural-chat`         |
@@ -75,7 +77,7 @@ Here are some example models that can be downloaded:
 | Code Llama         | 7B         | 3.8GB | `ollama run codellama`           |
 | Llama 2 Uncensored | 7B         | 3.8GB | `ollama run llama2-uncensored`   |
 | LLaVA              | 7B         | 4.5GB | `ollama run llava`               |
-| Solar              | 10.7B      | 6.1GB | `ollama run solar`               |
+| Granite-3.2         | 8B         | 4.9GB | `ollama run granite3.2`          |
 
 > [!NOTE]
 > You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
@@ -275,6 +277,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 ### Web & Desktop
 
 - [Open WebUI](https://github.com/open-webui/open-webui)
+- [SwiftChat (macOS with ReactNative)](https://github.com/aws-samples/swift-chat)
 - [Enchanted (macOS native)](https://github.com/AugustDev/enchanted)
 - [Hollama](https://github.com/fmaclen/hollama)
 - [Lollms-Webui](https://github.com/ParisNeo/lollms-webui)
@@ -386,6 +389,11 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [MaxKB](https://github.com/1Panel-dev/MaxKB/) (Ready-to-use & flexible RAG Chatbot)
 - [yla](https://github.com/danielekp/yla) (Web interface to freely interact with your customized models)
 - [LangBot](https://github.com/RockChinQ/LangBot) (LLM-based instant messaging bots platform, with Agents, RAG features, supports multiple platforms)
+- [1Panel](https://github.com/1Panel-dev/1Panel/) (Web-based Linux Server Management Tool)
+- [AstrBot](https://github.com/Soulter/AstrBot/) (User-friendly LLM-based multi-platform chatbot with a WebUI, supporting RAG, LLM agents, and plugins integration)
+- [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
+- [Ellama](https://github.com/zeozeozeo/ellama) (Friendly native app to chat with an Ollama instance)
+- [screenpipe](https://github.com/mediar-ai/screenpipe) Build agents powered by your screen history
 
 ### Cloud
 
@@ -429,6 +437,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 
 ### Apple Vision Pro
 
+- [SwiftChat](https://github.com/aws-samples/swift-chat) (Cross-platform AI chat app supporting Apple Vision Pro via "Designed for iPad")
 - [Enchanted](https://github.com/AugustDev/enchanted)
 
 ### Database
@@ -503,13 +512,17 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Ollama for Zig](https://github.com/dravenk/ollama-zig)
 - [Abso](https://github.com/lunary-ai/abso) (OpenAI-compatible TypeScript SDK for any LLM provider)
 - [Nichey](https://github.com/goodreasonai/nichey) is a Python package for generating custom wikis for your research topic
+- [Ollama for D](https://github.com/kassane/ollama-d)
 
 ### Mobile
 
+- [SwiftChat](https://github.com/aws-samples/swift-chat) (Lightning-fast Cross-platform AI chat app with native UI for Android, iOS and iPad)
 - [Enchanted](https://github.com/AugustDev/enchanted)
 - [Maid](https://github.com/Mobile-Artificial-Intelligence/maid)
 - [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
 - [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
+- [Ollama Android Chat](https://github.com/sunshine0523/OllamaServer) (No need for Termux, start the Ollama service with one click on an Android device)
+- [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
 
 ### Extensions & Plugins
 
@@ -555,12 +568,14 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [TextLLaMA](https://github.com/adarshM84/TextLLaMA) A Chrome Extension that helps you write emails, correct grammar, and translate into any language
 - [Simple-Discord-AI](https://github.com/zyphixor/simple-discord-ai)
 - [LLM Telegram Bot](https://github.com/innightwolfsleep/llm_telegram_bot) (telegram bot, primary for RP. Oobabooga-like buttons, [A1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) API integration e.t.c)
+- [mcp-llm](https://github.com/sammcj/mcp-llm) (MCP Server to allow LLMs to call other LLMs)
 
 ### Supported backends
 
 - [llama.cpp](https://github.com/ggerganov/llama.cpp) project founded by Georgi Gerganov.
 
 ### Observability
+- [Opik](https://www.comet.com/docs/opik/cookbook/ollama) is an open-source platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. Opik supports native intergration to Ollama.
 - [Lunary](https://lunary.ai/docs/integrations/ollama) is the leading open-source LLM observability platform. It provides a variety of enterprise-grade features such as real-time analytics, prompt templates management, PII masking, and comprehensive agent tracing.
 - [OpenLIT](https://github.com/openlit/openlit) is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
 - [HoneyHive](https://docs.honeyhive.ai/integrations/ollama) is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production.

api/client.go

@@ -10,7 +10,7 @@
 // repository].
 //
 // [the API documentation]: https://github.com/ollama/ollama/blob/main/docs/api.md
-// [in the GitHub repository]: https://github.com/ollama/ollama/tree/main/examples
+// [in the GitHub repository]: https://github.com/ollama/ollama/tree/main/api/examples
 package api
 
 import (

api/types.go

@@ -349,6 +349,7 @@ type ShowResponse struct {
 	Messages      []Message      `json:"messages,omitempty"`
 	ModelInfo     map[string]any `json:"model_info,omitempty"`
 	ProjectorInfo map[string]any `json:"projector_info,omitempty"`
+	Tensors       []Tensor       `json:"tensors,omitempty"`
 	ModifiedAt    time.Time      `json:"modified_at,omitempty"`
 }
 
@@ -361,9 +362,9 @@ type CopyRequest struct {
 // PullRequest is the request passed to [Client.Pull].
 type PullRequest struct {
 	Model    string `json:"model"`
-	Insecure bool   `json:"insecure,omitempty"`
-	Username string `json:"username"`
-	Password string `json:"password"`
+	Insecure bool   `json:"insecure,omitempty"` // Deprecated: ignored
+	Username string `json:"username"`           // Deprecated: ignored
+	Password string `json:"password"`           // Deprecated: ignored
 	Stream   *bool  `json:"stream,omitempty"`
 
 	// Deprecated: set the model name with Model instead
@@ -467,6 +468,13 @@ type ModelDetails struct {
 	QuantizationLevel string   `json:"quantization_level"`
 }
 
+// Tensor describes the metadata for a given tensor.
+type Tensor struct {
+	Name  string   `json:"name"`
+	Type  string   `json:"type"`
+	Shape []uint64 `json:"shape"`
+}
+
 func (m *Metrics) Summary() {
 	if m.TotalDuration > 0 {
 		fmt.Fprintf(os.Stderr, "total duration:       %v\n", m.TotalDuration)

benchmark/server_benchmark_test.go

@@ -0,0 +1,178 @@
+package benchmark
+
+import (
+	"context"
+	"flag"
+	"fmt"
+	"testing"
+	"time"
+
+	"github.com/ollama/ollama/api"
+)
+
+// Command line flags
+var modelFlag string
+
+func init() {
+	flag.StringVar(&modelFlag, "m", "", "Name of the model to benchmark")
+	flag.Lookup("m").DefValue = "model"
+}
+
+// modelName returns the model name from flags, failing the test if not set
+func modelName(b *testing.B) string {
+	if modelFlag == "" {
+		b.Fatal("Error: -m flag is required for benchmark tests")
+	}
+	return modelFlag
+}
+
+type TestCase struct {
+	name      string
+	prompt    string
+	maxTokens int
+}
+
+// runGenerateBenchmark contains the common generate and metrics logic
+func runGenerateBenchmark(b *testing.B, ctx context.Context, client *api.Client, req *api.GenerateRequest) {
+	start := time.Now()
+	var ttft time.Duration
+	var metrics api.Metrics
+
+	err := client.Generate(ctx, req, func(resp api.GenerateResponse) error {
+		if ttft == 0 && resp.Response != "" {
+			ttft = time.Since(start)
+		}
+		if resp.Done {
+			metrics = resp.Metrics
+		}
+		return nil
+	})
+
+	// Report custom metrics as part of the benchmark results
+	b.ReportMetric(float64(ttft.Milliseconds()), "ttft_ms")
+	b.ReportMetric(float64(metrics.LoadDuration.Milliseconds()), "load_ms")
+
+	// Token throughput metrics
+	promptThroughput := float64(metrics.PromptEvalCount) / metrics.PromptEvalDuration.Seconds()
+	genThroughput := float64(metrics.EvalCount) / metrics.EvalDuration.Seconds()
+	b.ReportMetric(promptThroughput, "prompt_tok/s")
+	b.ReportMetric(genThroughput, "gen_tok/s")
+
+	// Token counts
+	b.ReportMetric(float64(metrics.PromptEvalCount), "prompt_tokens")
+	b.ReportMetric(float64(metrics.EvalCount), "gen_tokens")
+	if err != nil {
+		b.Fatal(err)
+	}
+}
+
+// BenchmarkColdStart runs benchmarks with model loading from cold state
+func BenchmarkColdStart(b *testing.B) {
+	client := setup(b)
+	tests := []TestCase{
+		{"short_prompt", "Write a long story", 100},
+		{"medium_prompt", "Write a detailed economic analysis", 500},
+		{"long_prompt", "Write a comprehensive AI research paper", 1000},
+	}
+	m := modelName(b)
+
+	for _, tt := range tests {
+		b.Run(fmt.Sprintf("%s/cold/%s", m, tt.name), func(b *testing.B) {
+			ctx := context.Background()
+
+			// Set number of tokens as our throughput metric
+			b.SetBytes(int64(tt.maxTokens))
+
+			for b.Loop() {
+				b.StopTimer()
+				// Ensure model is unloaded before each iteration
+				unload(client, m, b)
+				b.StartTimer()
+
+				req := &api.GenerateRequest{
+					Model:   m,
+					Prompt:  tt.prompt,
+					Options: map[string]interface{}{"num_predict": tt.maxTokens, "temperature": 0.1},
+				}
+
+				runGenerateBenchmark(b, ctx, client, req)
+			}
+		})
+	}
+}
+
+// BenchmarkWarmStart runs benchmarks with pre-loaded model
+func BenchmarkWarmStart(b *testing.B) {
+	client := setup(b)
+	tests := []TestCase{
+		{"short_prompt", "Write a long story", 100},
+		{"medium_prompt", "Write a detailed economic analysis", 500},
+		{"long_prompt", "Write a comprehensive AI research paper", 1000},
+	}
+	m := modelName(b)
+
+	for _, tt := range tests {
+		b.Run(fmt.Sprintf("%s/warm/%s", m, tt.name), func(b *testing.B) {
+			ctx := context.Background()
+
+			// Pre-warm the model
+			warmup(client, m, tt.prompt, b)
+
+			// Set number of tokens as our throughput metric
+			b.SetBytes(int64(tt.maxTokens))
+
+			for b.Loop() {
+				req := &api.GenerateRequest{
+					Model:   m,
+					Prompt:  tt.prompt,
+					Options: map[string]any{"num_predict": tt.maxTokens, "temperature": 0.1},
+				}
+
+				runGenerateBenchmark(b, ctx, client, req)
+			}
+		})
+	}
+}
+
+// setup verifies server and model availability
+func setup(b *testing.B) *api.Client {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		b.Fatal(err)
+	}
+	if _, err := client.Show(context.Background(), &api.ShowRequest{Model: modelName(b)}); err != nil {
+		b.Fatalf("Model unavailable: %v", err)
+	}
+
+	return client
+}
+
+// warmup ensures the model is loaded and warmed up
+func warmup(client *api.Client, model string, prompt string, b *testing.B) {
+	for range 3 {
+		err := client.Generate(
+			context.Background(),
+			&api.GenerateRequest{
+				Model:   model,
+				Prompt:  prompt,
+				Options: map[string]interface{}{"num_predict": 50, "temperature": 0.1},
+			},
+			func(api.GenerateResponse) error { return nil },
+		)
+		if err != nil {
+			b.Logf("Error during model warm-up: %v", err)
+		}
+	}
+}
+
+// unload forces model unloading using KeepAlive: 0 parameter
+func unload(client *api.Client, model string, b *testing.B) {
+	req := &api.GenerateRequest{
+		Model:     model,
+		KeepAlive: &api.Duration{Duration: 0},
+	}
+	if err := client.Generate(context.Background(), req, func(api.GenerateResponse) error { return nil }); err != nil {
+		b.Logf("Unload error: %v", err)
+	}
+	time.Sleep(1 * time.Second)
+}

cmd/cmd.go

@@ -18,6 +18,7 @@ import (
 	"os/signal"
 	"path/filepath"
 	"runtime"
+	"sort"
 	"strconv"
 	"strings"
 	"sync/atomic"
@@ -34,7 +35,6 @@ import (
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
-	"github.com/ollama/ollama/llama"
 	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/progress"
 	"github.com/ollama/ollama/runner"
@@ -256,6 +256,7 @@ func StopHandler(cmd *cobra.Command, args []string) error {
 		if strings.Contains(err.Error(), "not found") {
 			return fmt.Errorf("couldn't find model \"%s\" to stop", args[0])
 		}
+		return err
 	}
 	return nil
 }
@@ -338,10 +339,16 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	// TODO(jessegross): We should either find another way to know if this is
-	// a vision model or remove the logic. Also consider that other modalities will
-	// need different behavior anyways.
-	opts.MultiModal = len(info.ProjectorInfo) != 0 || envconfig.NewEngine()
+	if len(info.ProjectorInfo) != 0 {
+		opts.MultiModal = true
+	}
+	for k := range info.ModelInfo {
+		if strings.Contains(k, ".vision.") {
+			opts.MultiModal = true
+			break
+		}
+	}
+
 	opts.ParentModel = info.Details.ParentModel
 
 	if interactive {
@@ -562,8 +569,9 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 	parameters, errParams := cmd.Flags().GetBool("parameters")
 	system, errSystem := cmd.Flags().GetBool("system")
 	template, errTemplate := cmd.Flags().GetBool("template")
+	verbose, errVerbose := cmd.Flags().GetBool("verbose")
 
-	for _, boolErr := range []error{errLicense, errModelfile, errParams, errSystem, errTemplate} {
+	for _, boolErr := range []error{errLicense, errModelfile, errParams, errSystem, errTemplate, errVerbose} {
 		if boolErr != nil {
 			return errors.New("error retrieving flags")
 		}
@@ -601,7 +609,7 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		return errors.New("only one of '--license', '--modelfile', '--parameters', '--system', or '--template' can be specified")
 	}
 
-	req := api.ShowRequest{Name: args[0]}
+	req := api.ShowRequest{Name: args[0], Verbose: verbose}
 	resp, err := client.Show(cmd.Context(), &req)
 	if err != nil {
 		return err
@@ -624,10 +632,10 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		return nil
 	}
 
-	return showInfo(resp, os.Stdout)
+	return showInfo(resp, verbose, os.Stdout)
 }
 
-func showInfo(resp *api.ShowResponse, w io.Writer) error {
+func showInfo(resp *api.ShowResponse, verbose bool, w io.Writer) error {
 	tableRender := func(header string, rows func() [][]string) {
 		fmt.Fprintln(w, " ", header)
 		table := tablewriter.NewWriter(w)
@@ -684,6 +692,47 @@ func showInfo(resp *api.ShowResponse, w io.Writer) error {
 		})
 	}
 
+	if resp.ModelInfo != nil && verbose {
+		tableRender("Metadata", func() (rows [][]string) {
+			keys := make([]string, 0, len(resp.ModelInfo))
+			for k := range resp.ModelInfo {
+				keys = append(keys, k)
+			}
+			sort.Strings(keys)
+
+			for _, k := range keys {
+				var v string
+				switch vData := resp.ModelInfo[k].(type) {
+				case bool:
+					v = fmt.Sprintf("%t", vData)
+				case string:
+					v = vData
+				case float64:
+					v = fmt.Sprintf("%g", vData)
+				case []any:
+					n := 3
+					if len(vData) < n {
+						n = len(vData)
+					}
+					v = fmt.Sprintf("%v", vData[:n])
+				default:
+					v = fmt.Sprintf("%T", vData)
+				}
+				rows = append(rows, []string{"", k, v})
+			}
+			return
+		})
+	}
+
+	if len(resp.Tensors) > 0 && verbose {
+		tableRender("Tensors", func() (rows [][]string) {
+			for _, t := range resp.Tensors {
+				rows = append(rows, []string{"", t.Name, t.Type, fmt.Sprint(t.Shape)})
+			}
+			return
+		})
+	}
+
 	head := func(s string, n int) (rows [][]string) {
 		scanner := bufio.NewScanner(strings.NewReader(s))
 		for scanner.Scan() && (len(rows) < n || n < 0) {
@@ -1190,6 +1239,7 @@ func NewCLI() *cobra.Command {
 	showCmd.Flags().Bool("parameters", false, "Show parameters of a model")
 	showCmd.Flags().Bool("template", false, "Show template of a model")
 	showCmd.Flags().Bool("system", false, "Show system message of a model")
+	showCmd.Flags().BoolP("verbose", "v", false, "Show detailed model information")
 
 	runCmd := &cobra.Command{
 		Use:     "run MODEL [PROMPT]",
@@ -1274,7 +1324,6 @@ func NewCLI() *cobra.Command {
 
 	runnerCmd := &cobra.Command{
 		Use:    "runner",
-		Short:  llama.PrintSystemInfo(),
 		Hidden: true,
 		RunE: func(cmd *cobra.Command, args []string) error {
 			return runner.Execute(os.Args[1:])

cmd/cmd_test.go

@@ -27,7 +27,7 @@ func TestShowInfo(t *testing.T) {
 				ParameterSize:     "7B",
 				QuantizationLevel: "FP16",
 			},
-		}, &b); err != nil {
+		}, false, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -57,7 +57,7 @@ func TestShowInfo(t *testing.T) {
 				ParameterSize:     "7B",
 				QuantizationLevel: "FP16",
 			},
-		}, &b); err != nil {
+		}, false, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -68,6 +68,60 @@ func TestShowInfo(t *testing.T) {
     embedding length    0       
     quantization        FP16    
 
+`
+		if diff := cmp.Diff(expect, b.String()); diff != "" {
+			t.Errorf("unexpected output (-want +got):\n%s", diff)
+		}
+	})
+
+	t.Run("verbose model", func(t *testing.T) {
+		var b bytes.Buffer
+		if err := showInfo(&api.ShowResponse{
+			Details: api.ModelDetails{
+				Family:            "test",
+				ParameterSize:     "8B",
+				QuantizationLevel: "FP16",
+			},
+			Parameters: `
+			stop up`,
+			ModelInfo: map[string]any{
+				"general.architecture":    "test",
+				"general.parameter_count": float64(8_000_000_000),
+				"some.true_bool":          true,
+				"some.false_bool":         false,
+				"test.context_length":     float64(1000),
+				"test.embedding_length":   float64(11434),
+			},
+			Tensors: []api.Tensor{
+				{Name: "blk.0.attn_k.weight", Type: "BF16", Shape: []uint64{42, 3117}},
+				{Name: "blk.0.attn_q.weight", Type: "FP16", Shape: []uint64{3117, 42}},
+			},
+		}, true, &b); err != nil {
+			t.Fatal(err)
+		}
+
+		expect := `  Model
+    architecture        test     
+    parameters          8B       
+    context length      1000     
+    embedding length    11434    
+    quantization        FP16     
+
+  Parameters
+    stop    up    
+
+  Metadata
+    general.architecture       test     
+    general.parameter_count    8e+09    
+    some.false_bool            false    
+    some.true_bool             true     
+    test.context_length        1000     
+    test.embedding_length      11434    
+
+  Tensors
+    blk.0.attn_k.weight    BF16    [42 3117]    
+    blk.0.attn_q.weight    FP16    [3117 42]    
+
 `
 		if diff := cmp.Diff(expect, b.String()); diff != "" {
 			t.Errorf("unexpected output (-want +got):\n%s", diff)
@@ -89,7 +143,7 @@ func TestShowInfo(t *testing.T) {
 			stop you
 			stop up
 			temperature 99`,
-		}, &b); err != nil {
+		}, false, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -126,7 +180,7 @@ func TestShowInfo(t *testing.T) {
 				"clip.vision.embedding_length": float64(0),
 				"clip.vision.projection_dim":   float64(0),
 			},
-		}, &b); err != nil {
+		}, false, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -159,7 +213,7 @@ func TestShowInfo(t *testing.T) {
 Ahoy, matey!
 Weigh anchor!
 			`,
-		}, &b); err != nil {
+		}, false, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -188,7 +242,7 @@ Weigh anchor!
 				QuantizationLevel: "FP16",
 			},
 			License: license,
-		}, &b); err != nil {
+		}, false, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -707,3 +761,132 @@ func TestCreateHandler(t *testing.T) {
 		})
 	}
 }
+
+func TestNewCreateRequest(t *testing.T) {
+	tests := []struct {
+		name     string
+		from     string
+		opts     runOptions
+		expected *api.CreateRequest
+	}{
+		{
+			"basic test",
+			"newmodel",
+			runOptions{
+				Model:       "mymodel",
+				ParentModel: "",
+				Prompt:      "You are a fun AI agent",
+				Messages:    []api.Message{},
+				WordWrap:    true,
+			},
+			&api.CreateRequest{
+				From:  "mymodel",
+				Model: "newmodel",
+			},
+		},
+		{
+			"parent model test",
+			"newmodel",
+			runOptions{
+				Model:       "mymodel",
+				ParentModel: "parentmodel",
+				Messages:    []api.Message{},
+				WordWrap:    true,
+			},
+			&api.CreateRequest{
+				From:  "parentmodel",
+				Model: "newmodel",
+			},
+		},
+		{
+			"parent model as filepath test",
+			"newmodel",
+			runOptions{
+				Model:       "mymodel",
+				ParentModel: "/some/file/like/etc/passwd",
+				Messages:    []api.Message{},
+				WordWrap:    true,
+			},
+			&api.CreateRequest{
+				From:  "mymodel",
+				Model: "newmodel",
+			},
+		},
+		{
+			"parent model as windows filepath test",
+			"newmodel",
+			runOptions{
+				Model:       "mymodel",
+				ParentModel: "D:\\some\\file\\like\\etc\\passwd",
+				Messages:    []api.Message{},
+				WordWrap:    true,
+			},
+			&api.CreateRequest{
+				From:  "mymodel",
+				Model: "newmodel",
+			},
+		},
+		{
+			"options test",
+			"newmodel",
+			runOptions{
+				Model:       "mymodel",
+				ParentModel: "parentmodel",
+				Options: map[string]any{
+					"temperature": 1.0,
+				},
+			},
+			&api.CreateRequest{
+				From:  "parentmodel",
+				Model: "newmodel",
+				Parameters: map[string]any{
+					"temperature": 1.0,
+				},
+			},
+		},
+		{
+			"messages test",
+			"newmodel",
+			runOptions{
+				Model:       "mymodel",
+				ParentModel: "parentmodel",
+				System:      "You are a fun AI agent",
+				Messages: []api.Message{
+					{
+						Role:    "user",
+						Content: "hello there!",
+					},
+					{
+						Role:    "assistant",
+						Content: "hello to you!",
+					},
+				},
+				WordWrap: true,
+			},
+			&api.CreateRequest{
+				From:   "parentmodel",
+				Model:  "newmodel",
+				System: "You are a fun AI agent",
+				Messages: []api.Message{
+					{
+						Role:    "user",
+						Content: "hello there!",
+					},
+					{
+						Role:    "assistant",
+						Content: "hello to you!",
+					},
+				},
+			},
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			actual := NewCreateRequest(tt.from, tt.opts)
+			if !cmp.Equal(actual, tt.expected) {
+				t.Errorf("expected output %#v, got %#v", tt.expected, actual)
+			}
+		})
+	}
+}

cmd/interactive.go

@@ -18,6 +18,7 @@ import (
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/readline"
 	"github.com/ollama/ollama/types/errtypes"
+	"github.com/ollama/ollama/types/model"
 )
 
 type MultilineState int
@@ -195,6 +196,10 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 			opts.Messages = []api.Message{}
 			fmt.Printf("Loading model '%s'\n", opts.Model)
 			if err := loadOrUnloadModel(cmd, &opts); err != nil {
+				if strings.Contains(err.Error(), "not found") {
+					fmt.Printf("error: %v\n", err)
+					continue
+				}
 				return err
 			}
 			continue
@@ -343,7 +348,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 
 				switch args[1] {
 				case "info":
-					_ = showInfo(resp, os.Stderr)
+					_ = showInfo(resp, false, os.Stderr)
 				case "license":
 					if resp.License == "" {
 						fmt.Println("No license was specified for this model.")
@@ -455,9 +460,16 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 }
 
 func NewCreateRequest(name string, opts runOptions) *api.CreateRequest {
+	parentModel := opts.ParentModel
+
+	modelName := model.ParseName(parentModel)
+	if !modelName.IsValid() {
+		parentModel = ""
+	}
+
 	req := &api.CreateRequest{
-		Name: name,
-		From: cmp.Or(opts.ParentModel, opts.Model),
+		Model: name,
+		From:  cmp.Or(parentModel, opts.Model),
 	}
 
 	if opts.System != "" {

convert/convert.go

@@ -13,8 +13,13 @@ import (
 )
 
 type ModelParameters struct {
-	Architectures []string `json:"architectures"`
-	VocabSize     uint32   `json:"vocab_size"`
+	Architectures []string       `json:"architectures"`
+	VocabSize     uint32         `json:"vocab_size"`
+	TextModel     TextParameters `json:"text_config"`
+}
+
+type TextParameters struct {
+	VocabSize uint32 `json:"vocab_size"`
 }
 
 type AdapterParameters struct {
@@ -185,6 +190,8 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 		conv = &gemmaModel{}
 	case "Gemma2ForCausalLM":
 		conv = &gemma2Model{}
+	case "Gemma3ForCausalLM", "Gemma3ForConditionalGeneration":
+		conv = &gemma3Model{Architecture: p.Architectures[0]}
 	case "Phi3ForCausalLM":
 		conv = &phi3Model{}
 	case "Qwen2ForCausalLM":
@@ -194,7 +201,7 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 	case "CohereForCausalLM":
 		conv = &commandrModel{}
 	default:
-		return errors.New("unsupported architecture")
+		return fmt.Errorf("unsupported architecture %q", p.Architectures[0])
 	}
 
 	if err := json.Unmarshal(bts, conv); err != nil {
@@ -213,7 +220,14 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 	}
 
 	vocabSize := int(p.VocabSize)
+	if vocabSize == 0 {
+		tVocabSize := int(p.TextModel.VocabSize)
+		vocabSize = tVocabSize
+	}
+
 	switch {
+	case vocabSize == 0:
+		slog.Warn("vocabulary size was not explicitly set by the model", "default size", len(t.Vocabulary.Tokens))
 	case vocabSize > len(t.Vocabulary.Tokens):
 		slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", vocabSize, "actual", len(t.Vocabulary.Tokens))
 		for i := range vocabSize - len(t.Vocabulary.Tokens) {

convert/convert_gemma.go

@@ -45,7 +45,7 @@ func (p *gemmaModel) KV(t *Tokenizer) ggml.KV {
 func (p *gemmaModel) Tensors(ts []Tensor) []ggml.Tensor {
 	var out []ggml.Tensor
 	for _, t := range ts {
-		if strings.HasSuffix(t.Name(), "_norm.weight") {
+		if !strings.HasPrefix(t.Name(), "v.") && strings.HasSuffix(t.Name(), "_norm.weight") {
 			t.SetRepacker(p.addOne)
 		}
 

convert/convert_gemma3.go

@@ -0,0 +1,142 @@
+package convert
+
+import (
+	"cmp"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type gemma3Model struct {
+	gemmaModel
+	Architecture string
+	TextModel    struct {
+		HeadDim          uint32 `json:"head_dim"`
+		HiddenSize       uint32 `json:"hidden_size"`
+		HiddenLayers     uint32 `json:"num_hidden_layers"`
+		IntermediateSize uint32 `json:"intermediate_size"`
+		SlidingWindow    uint32 `json:"sliding_window"`
+	} `json:"text_config"`
+	VisionModel struct {
+		NumAttentionHeads uint32  `json:"num_attention_heads"` // attention.head_count 16
+		LayerNormEpsilon  float32 `json:"layer_norm_eps"`      // attention.layer_norm_epsilon 1e-05
+		NumHiddenLayers   uint32  `json:"num_hidden_layers"`   // block_count 32
+		HiddenSize        uint32  `json:"hidden_size"`         // embedding_length 1280
+		IntermediateSize  uint32  `json:"intermediate_size"`   // feed_forward_length 5120
+		ImageSize         uint32  `json:"image_size"`          // image_size 560
+		NumChannels       uint32  `json:"num_channels"`        // num_channels 3
+		PatchSize         uint32  `json:"patch_size"`          // patch_size 14
+	} `json:"vision_config"`
+	MaxPositionEmbeddings    uint32  `json:"max_position_embeddings"`
+	NumAttentionHeads        uint32  `json:"num_attention_heads"`
+	NumKeyValueHeads         uint32  `json:"num_key_value_heads"`
+	RMSNormEPS               float32 `json:"rms_norm_eps"`
+	HeadDim                  uint32  `json:"head_dim"`
+	FinalLogitSoftcap        float32 `json:"final_logit_softcapping"`
+	RopeLocalTheta           float32 `json:"rope_local_base_freq"`
+	RopeGlobalTheta          float32 `json:"rope_global_base_freq"`
+	SlidingWindow            uint32  `json:"sliding_window"`
+	MultiModalTokensPerImage uint32  `json:"mm_tokens_per_image"`
+}
+
+const (
+	gemma4BLayerCount  = 34
+	gemma12BLayerCount = 48
+	gemma27BLayerCount = 62
+)
+
+func (p *gemma3Model) KV(t *Tokenizer) ggml.KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "gemma3"
+
+	numBlocks := cmp.Or(p.HiddenLayers, p.TextModel.HiddenLayers)
+	kv["gemma3.block_count"] = numBlocks
+
+	var (
+		numHeads   uint32
+		numKVHeads uint32
+	)
+
+	switch numBlocks {
+	case gemma4BLayerCount:
+		numHeads = 8
+		numKVHeads = 4
+	case gemma12BLayerCount:
+		numHeads = 16
+		numKVHeads = 8
+	case gemma27BLayerCount:
+		numHeads = 32
+		numKVHeads = 16
+	default:
+		numHeads = p.NumAttentionHeads
+		numKVHeads = p.NumKeyValueHeads
+	}
+
+	kv["gemma3.attention.head_count"] = numHeads
+	kv["gemma3.attention.head_count_kv"] = numKVHeads
+
+	switch p.Architecture {
+	case "Gemma3ForCausalLM":
+		kv["gemma3.context_length"] = p.MaxPositionEmbeddings
+		kv["gemma3.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+		kv["gemma3.attention.key_length"] = p.HeadDim
+		kv["gemma3.attention.value_length"] = p.HeadDim
+		kv["gemma3.attention.sliding_window"] = p.SlidingWindow
+		kv["gemma3.final_logit_softcapping"] = cmp.Or(p.FinalLogitSoftcap, 30)
+		kv["gemma3.rope.local.freq_base"] = cmp.Or(p.RopeLocalTheta, 10000.0)
+		kv["gemma3.rope.global.freq_base"] = cmp.Or(p.RopeGlobalTheta, 1000000.0)
+		kv["gemma3.embedding_length"] = p.HiddenSize
+		kv["gemma3.feed_forward_length"] = p.IntermediateSize
+	default:
+		kv["gemma3.context_length"] = cmp.Or(p.MaxPositionEmbeddings, 131072)
+		kv["gemma3.embedding_length"] = p.TextModel.HiddenSize
+		kv["gemma3.feed_forward_length"] = p.TextModel.IntermediateSize
+		kv["gemma3.attention.sliding_window"] = p.TextModel.SlidingWindow
+		kv["gemma3.vision.block_count"] = p.VisionModel.NumHiddenLayers
+		kv["gemma3.vision.embedding_length"] = p.VisionModel.HiddenSize
+		kv["gemma3.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
+		kv["gemma3.vision.image_size"] = p.VisionModel.ImageSize
+		kv["gemma3.vision.patch_size"] = p.VisionModel.PatchSize
+		kv["gemma3.vision.num_channels"] = cmp.Or(p.VisionModel.NumChannels, 3)
+		kv["gemma3.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
+		kv["gemma3.vision.attention.layer_norm_epsilon"] = cmp.Or(p.VisionModel.LayerNormEpsilon, 1e-6)
+		kv["gemma3.attention.key_length"] = cmp.Or(p.TextModel.HeadDim, 256)
+		kv["gemma3.attention.value_length"] = cmp.Or(p.TextModel.HeadDim, 256)
+	}
+
+	if p.MultiModalTokensPerImage > 0 {
+		kv["gemma3.mm.tokens_per_image"] = p.MultiModalTokensPerImage
+	}
+
+	return kv
+}
+
+func (p *gemma3Model) Replacements() []string {
+	return []string{
+		"lm_head", "output",
+		"model.embed_tokens", "token_embd",
+		"model.norm", "output_norm",
+		"vision_tower.vision_model.embeddings", "v",
+		"vision_tower.vision_model", "v",
+		"vision_model.vision_model.embeddings", "v",
+		"vision_model.vision_model", "v",
+		"language_model.", "",
+		"model.layers", "blk",
+		"encoder.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.q_norm", "attn_q_norm",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.k_norm", "attn_k_norm",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.o_proj", "attn_output",
+		"self_attn.out_proj", "attn_output",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.down_proj", "ffn_down",
+		"mlp.up_proj", "ffn_up",
+		"post_attention_layernorm", "post_attention_norm",
+		"pre_feedforward_layernorm", "ffn_norm",
+		"post_feedforward_layernorm", "post_ffw_norm",
+		"input_projection_weight", "input_projection.weight",
+		"multi_modal_projector", "mm",
+	}
+}

convert/tokenizer_spm.go

@@ -6,7 +6,9 @@ import (
 	"errors"
 	"fmt"
 	"io/fs"
+	"log/slog"
 	"os"
+	"reflect"
 	"slices"
 
 	"google.golang.org/protobuf/proto"
@@ -15,6 +17,8 @@ import (
 )
 
 func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
+	slog.Debug("using spm vocabulary")
+
 	ast, err := parseAdditionalSpecialTokens(fsys)
 	if err != nil {
 		return nil, err
@@ -43,10 +47,19 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
 			v.Types = append(v.Types, int32(t))
 		default:
 			tt := int32(sentencepiece.ModelProto_SentencePiece_NORMAL)
-			if slices.Contains(ast, piece.GetPiece()) {
+
+			// temporary fix to handle gemma3 broken configs
+			if slices.Contains([]string{"<end_of_turn>", "<start_of_turn>"}, piece.GetPiece()) {
 				tt = int32(sentencepiece.ModelProto_SentencePiece_CONTROL)
 			}
 
+			for _, t := range ast {
+				if t.Content == piece.GetPiece() {
+					tt = int32(sentencepiece.ModelProto_SentencePiece_CONTROL)
+					break
+				}
+			}
+
 			v.Types = append(v.Types, tt)
 		}
 	}
@@ -78,10 +91,16 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
 		return cmp.Compare(i.id, j.id)
 	})
 
-	n := len(v.Tokens)
-	for i, t := range ts {
-		if t.id != i+n {
-			return nil, fmt.Errorf("invalid token id: %d", t.id)
+	for _, t := range ts {
+		if t.id < len(v.Tokens) {
+			if v.Tokens[t.id] == t.content {
+				slog.Warn("tokenizer", "duplicate token", t.content, "id", t.id)
+				continue
+			}
+			return nil, fmt.Errorf("token mismatch: %s != %s at pos [%d]", t.content, v.Tokens[t.id], t.id)
+		}
+		if t.id != len(v.Tokens) {
+			return nil, fmt.Errorf("invalid token id: [%d] as pos [%d]", t.id, len(v.Tokens))
 		}
 
 		v.Tokens = append(v.Tokens, t.content)
@@ -92,7 +111,15 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
 	return &v, nil
 }
 
-func parseAdditionalSpecialTokens(fsys fs.FS) ([]string, error) {
+type specialToken struct {
+	Content    string `json:"content"`
+	Lstrip     bool   `json:"lstrip"`
+	Normalized bool   `json:"normalized"`
+	Rstrip     bool   `json:"rstrip"`
+	SingleWord bool   `json:"single_word"`
+}
+
+func parseAdditionalSpecialTokens(fsys fs.FS) ([]specialToken, error) {
 	f, err := fsys.Open("special_tokens_map.json")
 	if errors.Is(err, os.ErrNotExist) {
 		return nil, nil
@@ -102,12 +129,43 @@ func parseAdditionalSpecialTokens(fsys fs.FS) ([]string, error) {
 	defer f.Close()
 
 	var m struct {
-		AdditionalSpecialTokens []string `json:"additional_special_tokens"`
+		AdditionalSpecialTokens any `json:"additional_special_tokens"`
 	}
 
 	if err := json.NewDecoder(f).Decode(&m); err != nil {
 		return nil, err
 	}
 
-	return m.AdditionalSpecialTokens, nil
+	var ast []specialToken
+
+	switch st := m.AdditionalSpecialTokens.(type) {
+	case []string:
+		for _, s := range st {
+			ast = append(ast, specialToken{Content: s})
+		}
+	case []any:
+		for _, s := range st {
+			// marshal and unmarshal the object to get the special token
+			tMap := s.(map[string]any)
+			data, err := json.Marshal(tMap)
+			if err != nil {
+				return nil, err
+			}
+
+			var token specialToken
+			err = json.Unmarshal(data, &token)
+			if err != nil {
+				return nil, err
+			}
+
+			ast = append(ast, token)
+		}
+
+	default:
+		slog.Warn("special token", "unknown token", reflect.TypeOf(st))
+	}
+
+	slog.Debug("spm tokenizer", "additional tokens", ast)
+
+	return ast, nil
 }

docs/api.md

@@ -558,6 +558,10 @@ Final response:
 {
   "model": "llama3.2",
   "created_at": "2023-08-04T19:22:45.499127Z",
+  "message": {
+    "role": "assistant",
+    "content": ""
+  },
   "done": true,
   "total_duration": 4883583458,
   "load_duration": 1334875,

docs/benchmark.md

@@ -0,0 +1,59 @@
+# Benchmark
+
+Go benchmark tests that measure end-to-end performance of a running Ollama server. Run these tests to evaluate model inference performance on your hardware and measure the impact of code changes.
+
+## When to use
+
+Run these benchmarks when:
+- Making changes to the model inference engine
+- Modifying model loading/unloading logic
+- Changing prompt processing or token generation code
+- Implementing a new model architecture
+- Testing performance across different hardware setups
+
+## Prerequisites
+- Ollama server running locally with `ollama serve` on `127.0.0.1:11434`
+## Usage and Examples
+
+>[!NOTE]
+>All commands must be run from the root directory of the Ollama project.
+
+Basic syntax:
+```bash
+go test -bench=. ./benchmark/... -m $MODEL_NAME
+```
+
+Required flags:
+- `-bench=.`: Run all benchmarks
+- `-m`: Model name to benchmark
+
+Optional flags:
+- `-count N`: Number of times to run the benchmark (useful for statistical analysis)
+- `-timeout T`: Maximum time for the benchmark to run (e.g. "10m" for 10 minutes)
+
+Common usage patterns:
+
+Single benchmark run with a model specified:
+```bash
+go test -bench=. ./benchmark/... -m llama3.3
+```
+
+## Output metrics
+
+The benchmark reports several key metrics:
+
+- `gen_tok/s`: Generated tokens per second
+- `prompt_tok/s`: Prompt processing tokens per second
+- `ttft_ms`: Time to first token in milliseconds
+- `load_ms`: Model load time in milliseconds
+- `gen_tokens`: Total tokens generated
+- `prompt_tokens`: Total prompt tokens processed
+
+Each benchmark runs two scenarios:
+- Cold start: Model is loaded from disk for each test
+- Warm start: Model is pre-loaded in memory
+
+Three prompt lengths are tested for each scenario:
+- Short prompt (100 tokens)
+- Medium prompt (500 tokens)
+- Long prompt (1000 tokens)

docs/development.md

@@ -118,6 +118,35 @@ To run tests, use `go test`:
 go test ./...
 ```
 
+> NOTE: In rare cirumstances, you may nedd to change a package using the new
+> "synctest" package in go1.24.
+>
+> If you do not have the "synctest" package enabled, you will not see build or
+> test failures resulting from your change(s), if any, locally, but CI will
+> break.
+>
+> If you see failures in CI, you can either keep pushing changes to see if the
+> CI build passes, or you can enable the "synctest" package locally to see the
+> failures before pushing.
+>
+> To enable the "synctest" package for testing, run the following command:
+>
+> ```shell
+> GOEXPERIMENT=synctest go test ./...
+> ```
+>
+> If you wish to enable synctest for all go commands, you can set the
+> `GOEXPERIMENT` environment variable in your shell profile or by using:
+>
+> ```shell
+> go env -w GOEXPERIMENT=synctest
+> ```
+>
+> Which will enable the "synctest" package for all go commands without needing
+> to set it for all shell sessions.
+>
+> The synctest package is not required for production builds.
+
 ## Library detection
 
 Ollama looks for acceleration libraries in the following paths relative to the `ollama` executable:

docs/faq.md

@@ -20,7 +20,7 @@ Please refer to the [GPU docs](./gpu.md).
 
 ## How can I specify the context window size?
 
-By default, Ollama uses a context window size of 2048 tokens.
+By default, Ollama uses a context window size of 2048 tokens. This can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context length to 8K, use: `OLLAMA_CONTEXT_LENGTH=8192 ollama serve`.
 
 To change this when using `ollama run`, use `/set parameter`:
 
@@ -187,6 +187,13 @@ cloudflared tunnel --url http://localhost:11434 --http-host-header="localhost:11
 
 Ollama allows cross-origin requests from `127.0.0.1` and `0.0.0.0` by default. Additional origins can be configured with `OLLAMA_ORIGINS`.
 
+For browser extensions, you'll need to explicitly allow the extension's origin pattern. Set `OLLAMA_ORIGINS` to include `chrome-extension://*`, `moz-extension://*`, and `safari-web-extension://*` if you wish to allow all browser extensions access, or specific extensions as needed:
+
+```
+# Allow all Chrome, Firefox, and Safari extensions
+OLLAMA_ORIGINS=chrome-extension://*,moz-extension://*,safari-web-extension://* ollama serve
+```
+
 Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
 
 ## Where are models stored?

docs/linux.md

@@ -75,7 +75,7 @@ RestartSec=3
 Environment="PATH=$PATH"
 
 [Install]
-WantedBy=default.target
+WantedBy=multi-user.target
 ```
 
 Then start the service:

docs/windows.md

@@ -81,9 +81,11 @@ help you keep up to date.
 
 If you'd like to install or integrate Ollama as a service, a standalone
 `ollama-windows-amd64.zip` zip file is available containing only the Ollama CLI
-and GPU library dependencies for Nvidia and AMD. This allows for embedding
-Ollama in existing applications, or running it as a system service via `ollama
-serve` with tools such as [NSSM](https://nssm.cc/).
+and GPU library dependencies for Nvidia.  If you have an AMD GPU, also download
+and extract the additional ROCm package `ollama-windows-amd64-rocm.zip` into the
+same directory.  This allows for embedding Ollama in existing applications, or
+running it as a system service via `ollama serve` with tools such as
+[NSSM](https://nssm.cc/). 
 
 > [!NOTE]  
 > If you are upgrading from a prior version, you should remove the old directories first.

envconfig/config.go

@@ -73,6 +73,7 @@ func AllowedOrigins() (origins []string) {
 		"file://*",
 		"tauri://*",
 		"vscode-webview://*",
+		"vscode-file://*",
 	)
 
 	return origins

envconfig/config_test.go

@@ -69,6 +69,7 @@ func TestOrigins(t *testing.T) {
 			"file://*",
 			"tauri://*",
 			"vscode-webview://*",
+			"vscode-file://*",
 		}},
 		{"http://10.0.0.1", []string{
 			"http://10.0.0.1",
@@ -88,6 +89,7 @@ func TestOrigins(t *testing.T) {
 			"file://*",
 			"tauri://*",
 			"vscode-webview://*",
+			"vscode-file://*",
 		}},
 		{"http://172.16.0.1,https://192.168.0.1", []string{
 			"http://172.16.0.1",
@@ -108,6 +110,7 @@ func TestOrigins(t *testing.T) {
 			"file://*",
 			"tauri://*",
 			"vscode-webview://*",
+			"vscode-file://*",
 		}},
 		{"http://totally.safe,http://definitely.legit", []string{
 			"http://totally.safe",
@@ -128,6 +131,7 @@ func TestOrigins(t *testing.T) {
 			"file://*",
 			"tauri://*",
 			"vscode-webview://*",
+			"vscode-file://*",
 		}},
 	}
 	for _, tt := range cases {

fs/ggml/ggml.go

@@ -100,6 +100,10 @@ func (kv KV) Float(key string, defaultValue ...float32) float32 {
 	return keyValue(kv, key, append(defaultValue, 0)...)
 }
 
+func (kv KV) Bool(key string, defaultValue ...bool) bool {
+	return keyValue(kv, key, append(defaultValue, false)...)
+}
+
 func (kv KV) Strings(key string, defaultValue ...[]string) []string {
 	r := keyValue(kv, key, &array{})
 	s := make([]string, r.size)
@@ -120,7 +124,20 @@ func (kv KV) Uints(key string, defaultValue ...[]uint32) []uint32 {
 	return s
 }
 
-func keyValue[T string | uint32 | uint64 | float32 | *array](kv KV, key string, defaultValue ...T) T {
+func (kv KV) Floats(key string, defaultValue ...[]float32) []float32 {
+	r := keyValue(kv, key, &array{})
+	s := make([]float32, r.size)
+	for i := range r.size {
+		s[i] = float32(r.values[i].(float32))
+	}
+	return s
+}
+
+func (kv KV) OllamaEngineRequired() bool {
+	return kv.Architecture() == "gemma3"
+}
+
+func keyValue[T string | uint32 | uint64 | float32 | *array | bool](kv KV, key string, defaultValue ...T) T {
 	if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") {
 		key = kv.Architecture() + "." + key
 	}
@@ -310,6 +327,10 @@ func (t Tensor) Size() uint64 {
 	return t.parameters() * t.typeSize() / t.blockSize()
 }
 
+func (t Tensor) Type() string {
+	return fileType(t.Kind).String()
+}
+
 type container interface {
 	Name() string
 	Decode(io.ReadSeeker) (model, error)
@@ -472,7 +493,7 @@ func (f GGML) GraphSize(context, batch uint64, kvCacheType string) (kv, partialO
 			// vocab graph
 			4*batch*(embedding+vocab)+embedding*vocab*105/128,
 		)
-	case "gemma", "gemma2":
+	case "gemma", "gemma2", "gemma3":
 		fullOffload = max(
 			4*batch*(embedding+vocab),
 			4*batch*(2+context+context*heads+2*embedding+2*embeddingHeadsK*heads),
@@ -561,6 +582,56 @@ func (f GGML) GraphSize(context, batch uint64, kvCacheType string) (kv, partialO
 	return
 }
 
+func (llm GGML) VisionGraphSize() (weights, graphSize uint64) {
+	if llm.KV().Uint("vision.block_count") == 0 {
+		return
+	}
+
+	for name, layer := range llm.Tensors().GroupLayers() {
+		if name == "v" || strings.HasPrefix(name, "v.") {
+			for _, tensor := range layer {
+				weights += tensor.Size()
+			}
+		}
+	}
+
+	imageSize := uint64(llm.KV().Uint("vision.image_size"))
+	patchSize := uint64(llm.KV().Uint("vision.patch_size"))
+	if patchSize == 0 {
+		slog.Warn("unknown patch size for vision model")
+		return
+	}
+
+	numChannels := uint64(llm.KV().Uint("vision.num_channels"))
+
+	numPatches := (imageSize / patchSize) * (imageSize / patchSize)
+	if _, ok := llm.Tensors().GroupLayers()["v"]["class_embd"]; ok {
+		numPatches++
+	}
+
+	headCount := uint64(llm.KV().Uint("vision.attention.head_count"))
+	embeddingLength := uint64(llm.KV().Uint("vision.embedding_length"))
+
+	switch llm.KV().Architecture() {
+	case "mllama":
+		numPaddedPatches := numPatches + 8 - (numPatches%8)%8
+
+		maxNumTiles := uint64(llm.KV().Uint("vision.max_num_tiles"))
+
+		graphSize = 4 * (8 +
+			imageSize*imageSize*numChannels*maxNumTiles +
+			embeddingLength*numPatches*maxNumTiles +
+			9*embeddingLength*numPaddedPatches*maxNumTiles +
+			numPaddedPatches*maxNumTiles*numPaddedPatches*maxNumTiles*headCount)
+	case "gemma3":
+		graphSize = 4 * (imageSize*imageSize*numChannels +
+			embeddingLength*patchSize +
+			numPatches*numPatches*headCount)
+	}
+
+	return weights, graphSize
+}
+
 // SupportsKVCacheType checks if the requested cache type is supported
 func (f GGML) SupportsKVCacheType(cacheType string) bool {
 	return slices.Contains([]string{"f16", "q8_0", "q4_0"}, cacheType)

go.mod

@@ -1,6 +1,6 @@
 module github.com/ollama/ollama
 
-go 1.24
+go 1.24.0
 
 require (
 	github.com/containerd/console v1.0.3
@@ -11,7 +11,7 @@ require (
 	github.com/spf13/cobra v1.7.0
 	github.com/stretchr/testify v1.9.0
 	github.com/x448/float16 v0.8.4
-	golang.org/x/sync v0.10.0
+	golang.org/x/sync v0.11.0
 )
 
 require (
@@ -24,7 +24,7 @@ require (
 	github.com/nlpodyssey/gopickle v0.3.0
 	github.com/pdevine/tensor v0.0.0-20240510204454-f88f4562727c
 	golang.org/x/image v0.22.0
-	gonum.org/v1/gonum v0.15.0
+	golang.org/x/tools v0.30.0
 )
 
 require (
@@ -44,6 +44,7 @@ require (
 	github.com/xtgo/set v1.0.0 // indirect
 	go4.org/unsafe/assume-no-moving-gc v0.0.0-20231121144256-b99613f794b6 // indirect
 	golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1 // indirect
+	gonum.org/v1/gonum v0.15.0 // indirect
 	gorgonia.org/vecf32 v0.9.0 // indirect
 	gorgonia.org/vecf64 v0.9.0 // indirect
 )
@@ -69,12 +70,12 @@ require (
 	github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
 	github.com/ugorji/go/codec v1.2.12 // indirect
 	golang.org/x/arch v0.8.0 // indirect
-	golang.org/x/crypto v0.31.0
-	golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa
-	golang.org/x/net v0.25.0 // indirect
-	golang.org/x/sys v0.28.0
-	golang.org/x/term v0.27.0
-	golang.org/x/text v0.21.0
+	golang.org/x/crypto v0.33.0
+	golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa
+	golang.org/x/net v0.35.0 // indirect
+	golang.org/x/sys v0.30.0
+	golang.org/x/term v0.29.0
+	golang.org/x/text v0.22.0
 	google.golang.org/protobuf v1.34.1
 	gopkg.in/yaml.v3 v3.0.1 // indirect
 )

go.sum

@@ -214,16 +214,16 @@ golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACk
 golang.org/x/crypto v0.0.0-20190510104115-cbcb75029529/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
 golang.org/x/crypto v0.0.0-20191011191535-87dc89f01550/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
 golang.org/x/crypto v0.0.0-20200622213623-75b288015ac9/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
-golang.org/x/crypto v0.31.0 h1:ihbySMvVjLAeSH1IbfcRTkD/iNscyz8rGzjF/E5hV6U=
-golang.org/x/crypto v0.31.0/go.mod h1:kDsLvtWBEx7MV9tJOj9bnXsPbxwJQ6csT/x4KIN4Ssk=
+golang.org/x/crypto v0.33.0 h1:IOBPskki6Lysi0lo9qQvbxiQ+FvsCC/YWOecCHAixus=
+golang.org/x/crypto v0.33.0/go.mod h1:bVdXmD7IV/4GdElGPozy6U7lWdRXA4qyRVGJV57uQ5M=
 golang.org/x/exp v0.0.0-20180321215751-8460e604b9de/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20180807140117-3d87b88a115f/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20190121172915-509febef88a4/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20190125153040-c74c464bbbf2/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20190306152737-a1d7652674e8/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20191002040644-a1355ae1e2c3/go.mod h1:NOZ3BPKG0ec/BKJQgnvsSFpcKLM5xXVWnvZS97DWHgE=
-golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa h1:FRnLl4eNAQl8hwxVVC17teOw8kdjVDVAiFMtgUdTSRQ=
-golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa/go.mod h1:zk2irFbV9DP96SEBUUAy67IdHUaZuSnrz1n472HUCLE=
+golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa h1:t2QcU6V556bFjYgu4L6C+6VrCPyJZ+eyRsABUPs1mz4=
+golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa/go.mod h1:BHOTPb3L19zxehTsLoJXVaTktb06DFgmdW6Wb9s8jqk=
 golang.org/x/image v0.0.0-20180708004352-c73c2afc3b81/go.mod h1:ux5Hcp/YLpHSI86hEcLt0YII63i6oz57MZXIpbrjZUs=
 golang.org/x/image v0.0.0-20190227222117-0694c2d4d067/go.mod h1:kZ7UVZpmo3dzQBMxlp+ypCbDeSB+sBbTgSJuh5dn5js=
 golang.org/x/image v0.0.0-20190802002840-cff245a6509b/go.mod h1:FeLwcggjj3mMvU+oOTbSwawSJRM1uh48EjtB4UJZlP0=
@@ -257,8 +257,8 @@ golang.org/x/net v0.0.0-20200822124328-c89045814202/go.mod h1:/O7V0waA8r7cgGh81R
 golang.org/x/net v0.0.0-20201021035429-f5854403a974/go.mod h1:sp8m0HH+o8qH0wwXwYZr8TS3Oi6o0r6Gce1SSxlDquU=
 golang.org/x/net v0.0.0-20210405180319-a5a99cb37ef4/go.mod h1:p54w0d4576C0XHj96bSt6lcn1PtDYWL6XObtHCRCNQM=
 golang.org/x/net v0.0.0-20210614182718-04defd469f4e/go.mod h1:9nx3DQGgdP8bBQD5qxJ1jj9UTztislL4KSBs9R2vV5Y=
-golang.org/x/net v0.25.0 h1:d/OCCoBEUq33pjydKrGQhw7IlUPI2Oylr+8qLx49kac=
-golang.org/x/net v0.25.0/go.mod h1:JkAGAh7GEvH74S6FOH42FLoXpXbE/aqXSrIQjXgsiwM=
+golang.org/x/net v0.35.0 h1:T5GQRQb2y08kTAByq9L4/bz8cipCdA8FbRTXewonqY8=
+golang.org/x/net v0.35.0/go.mod h1:EglIi67kWsHKlRzzVMUD93VMSWGFOMSZgxFjparz1Qk=
 golang.org/x/oauth2 v0.0.0-20180821212333-d2e6202438be/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=
 golang.org/x/oauth2 v0.0.0-20200107190931-bf48bf16ab8d/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw=
 golang.org/x/sync v0.0.0-20180314180146-1d60e4601c6f/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
@@ -268,8 +268,8 @@ golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJ
 golang.org/x/sync v0.0.0-20190911185100-cd5d95a43a6e/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.0.0-20201020160332-67f06af15bc9/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.0.0-20210220032951-036812b2e83c/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
-golang.org/x/sync v0.10.0 h1:3NQrjDixjgGwUOCaF8w2+VYHv0Ve/vGYSbdkTa98gmQ=
-golang.org/x/sync v0.10.0/go.mod h1:Czt+wKu1gCyEFDUtn0jG5QVvpJ6rzVqr5aXyt9drQfk=
+golang.org/x/sync v0.11.0 h1:GGz8+XQP4FvTTrjZPzNKTMFtSXH80RAzG+5ghFPgK9w=
+golang.org/x/sync v0.11.0/go.mod h1:Czt+wKu1gCyEFDUtn0jG5QVvpJ6rzVqr5aXyt9drQfk=
 golang.org/x/sys v0.0.0-20180830151530-49385e6e1522/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
 golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
 golang.org/x/sys v0.0.0-20190312061237-fead79001313/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
@@ -285,17 +285,17 @@ golang.org/x/sys v0.0.0-20210510120138-977fb7262007/go.mod h1:oPkhp1MJrh7nUepCBc
 golang.org/x/sys v0.0.0-20210630005230-0f9fa26af87c/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.5.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
-golang.org/x/sys v0.28.0 h1:Fksou7UEQUWlKvIdsqzJmUmCX3cZuD2+P3XyyzwMhlA=
-golang.org/x/sys v0.28.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
+golang.org/x/sys v0.30.0 h1:QjkSwP/36a20jFYWkSue1YwXzLmsV5Gfq7Eiy72C1uc=
+golang.org/x/sys v0.30.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
 golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
-golang.org/x/term v0.27.0 h1:WP60Sv1nlK1T6SupCHbXzSaN0b9wUmsPoRS9b61A23Q=
-golang.org/x/term v0.27.0/go.mod h1:iMsnZpn0cago0GOrHO2+Y7u7JPn5AylBrcoWkElMTSM=
+golang.org/x/term v0.29.0 h1:L6pJp37ocefwRRtYPKSWOWzOtWSxVajvz2ldH/xi3iU=
+golang.org/x/term v0.29.0/go.mod h1:6bl4lRlvVuDgSf3179VpIxBF0o10JUpXWOnI7nErv7s=
 golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
 golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
 golang.org/x/text v0.3.5/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
 golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
-golang.org/x/text v0.21.0 h1:zyQAAkrwaneQ066sspRyJaG9VNi/YJ1NfzcGB3hZ/qo=
-golang.org/x/text v0.21.0/go.mod h1:4IBbMaMmOPCJ8SecivzSH54+73PCFmPWxNTLm+vZkEQ=
+golang.org/x/text v0.22.0 h1:bofq7m3/HAFvbF51jz3Q9wLg3jkvSPuiZu/pD1XwgtM=
+golang.org/x/text v0.22.0/go.mod h1:YRoo4H8PVmsu+E3Ou7cqLVH8oXWIHVoX0jqUWALQhfY=
 golang.org/x/tools v0.0.0-20180525024113-a5b4c53f6e8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20190114222345-bf090417da8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
@@ -309,6 +309,8 @@ golang.org/x/tools v0.0.0-20200130002326-2f3ba24bd6e7/go.mod h1:TB2adYChydJhpapK
 golang.org/x/tools v0.0.0-20200619180055-7c47624df98f/go.mod h1:EkVYQZoAsY45+roYkvgYkIh4xh/qjgUK9TdY2XT94GE=
 golang.org/x/tools v0.0.0-20210106214847-113979e3529a/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
 golang.org/x/tools v0.1.4/go.mod h1:o0xws9oXOQQZyjljx8fwUC0k7L1pTE6eaCbjGeHmOkk=
+golang.org/x/tools v0.30.0 h1:BgcpHewrV5AUp2G9MebG4XPFI1E2W41zU1SaqVA9vJY=
+golang.org/x/tools v0.30.0/go.mod h1:c347cR/OJfw5TI+GfX7RUPNMdDRRbjvYTS0jPyvsVtY=
 golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20191011141410-1b5146add898/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=

grammar/bench_test.go

@@ -0,0 +1,22 @@
+//go:build go1.24
+
+package grammar
+
+import "testing"
+
+func BenchmarkFromSchema(b *testing.B) {
+	for tt := range testCases(b) {
+		b.Run("", func(b *testing.B) {
+			s := []byte(tt.schema)
+
+			b.ReportAllocs()
+			for b.Loop() {
+				_, err := FromSchema(nil, s)
+				if err != nil {
+					b.Fatalf("GrammarFromSchema: %v", err)
+				}
+			}
+		})
+		return
+	}
+}

grammar/grammar.go

@@ -0,0 +1,227 @@
+package grammar
+
+import (
+	"bytes"
+	"encoding/json"
+	"fmt"
+	"iter"
+	"strconv"
+
+	"github.com/ollama/ollama/grammar/jsonschema"
+)
+
+const jsonTerms = `
+# Unicode
+#
+# Unicode characters can be specified directly in the grammar, for example
+# hiragana ::= [ぁ-ゟ], or with escapes: 8-bit (\xXX), 16-bit (\uXXXX) or 32-bit
+# (\UXXXXXXXX).
+unicode ::= \x{hex}{2} | \u{hex}{4} | \U{hex}{8}
+
+# JSON grammar from RFC 7159
+null    ::= "null"
+object  ::= "{" (kv ("," kv)*)? "}"
+array   ::= "[" (value ("," value)*)? "]"
+kv      ::= string ":" value
+integer ::= "0" | [1-9] [0-9]*
+number  ::= "-"? integer frac? exp?
+frac    ::= "." [0-9]+
+exp     ::= ("e" | "E") ("+" | "-") [0-9]+
+string  ::= "\"" char* "\""
+escape  ::= ["/" | "b" | "f" | "n" | "r" | "t" | unicode]
+char    ::= [^"\\] | escape
+space   ::= (" " | "\t" | "\n" | "\r")*
+hex     ::= [0-9] | [a-f] | [A-F]
+boolean ::= "true" | "false"
+value   ::= object | array | string | number | boolean | "null"
+
+# User-defined
+`
+
+// FromSchema generates a grammar from a JSON schema.
+func FromSchema(buf []byte, jsonSchema []byte) ([]byte, error) {
+	var s *jsonschema.Schema
+	if err := json.Unmarshal(jsonSchema, &s); err != nil {
+		return nil, err
+	}
+
+	var g builder
+
+	// "root" is the only rule that is guaranteed to exist, so we start
+	// with its length for padding, and then adjust it as we go.
+	g.pad = len("root")
+	for id := range dependencies("root", s) {
+		g.pad = max(g.pad, len(id))
+	}
+
+	g.b.WriteString(jsonTerms)
+
+	ids := make(map[*jsonschema.Schema]string)
+	for id, s := range dependencies("root", s) {
+		ids[s] = id
+		g.define(id)
+		if err := fromSchema(&g, ids, s); err != nil {
+			return nil, err
+		}
+	}
+	g.define("root")
+	if err := fromSchema(&g, ids, s); err != nil {
+		return nil, err
+	}
+	g.define("") // finalize the last rule
+	return g.b.Bytes(), nil
+}
+
+func fromSchema(g *builder, ids map[*jsonschema.Schema]string, s *jsonschema.Schema) error {
+	switch typ := s.EffectiveType(); typ {
+	case "array":
+		if len(s.PrefixItems) == 0 && s.Items == nil {
+			g.u("array")
+		} else {
+			g.q("[")
+			for i, s := range s.PrefixItems {
+				if i > 0 {
+					g.q(",")
+				}
+				g.u(ids[s])
+			}
+			if s.Items != nil {
+				g.u("(")
+				if len(s.PrefixItems) > 0 {
+					g.q(",")
+				}
+				g.u(ids[s.Items])
+				g.u(")*")
+			}
+			g.q("]")
+		}
+	case "object":
+		if len(s.Properties) == 0 {
+			g.u("object")
+		} else {
+			g.q("{")
+			for i, p := range s.Properties {
+				name := ids[p]
+				if i > 0 {
+					g.q(",")
+				}
+				g.q(p.Name)
+				g.q(":")
+				g.u(name)
+			}
+			g.q("}")
+		}
+	case "number":
+		buildConstrainedNumber(g, s)
+	case "string":
+		if len(s.Enum) == 0 {
+			g.u("string")
+		} else {
+			g.u("(")
+			for i, e := range s.Enum {
+				if i > 0 {
+					g.q("|")
+				}
+				g.q(string(e))
+			}
+			g.u(")")
+		}
+	case "boolean", "value", "null", "integer":
+		g.u(typ)
+	default:
+		return fmt.Errorf("%s: unsupported type %q", s.Name, typ)
+	}
+	return nil
+}
+
+// dependencies returns a sequence of all child dependencies of the schema in
+// post-order.
+//
+// The first value is the id/pointer to the dependency, and the second value
+// is the schema.
+func dependencies(id string, s *jsonschema.Schema) iter.Seq2[string, *jsonschema.Schema] {
+	return func(yield func(string, *jsonschema.Schema) bool) {
+		for i, p := range s.Properties {
+			id := fmt.Sprintf("%s_%d", id, i)
+			for did, d := range dependencies(id, p) {
+				if !yield(did, d) {
+					return
+				}
+			}
+			if !yield(id, p) {
+				return
+			}
+		}
+		for i, p := range s.PrefixItems {
+			id := fmt.Sprintf("tuple_%d", i)
+			for did, d := range dependencies(id, p) {
+				id := fmt.Sprintf("%s_%s", id, did)
+				if !yield(id, d) {
+					return
+				}
+			}
+			if !yield(id, p) {
+				return
+			}
+		}
+		if s.Items != nil {
+			id := fmt.Sprintf("%s_tuple_%d", id, len(s.PrefixItems))
+			for did, d := range dependencies(id, s.Items) {
+				if !yield(did, d) {
+					return
+				}
+			}
+			if !yield(id, s.Items) {
+				return
+			}
+		}
+	}
+}
+
+type builder struct {
+	b     bytes.Buffer
+	pad   int
+	rules int
+	items int
+}
+
+// define terminates the current rule, if any, and then either starts a new
+// rule or does nothing else if the name is empty.
+func (b *builder) define(name string) {
+	if b.rules > 0 {
+		b.b.WriteString(";\n")
+	}
+	if name == "" {
+		return
+	}
+	fmt.Fprintf(&b.b, "% -*s", b.pad, name)
+	b.b.WriteString(" ::=")
+	b.rules++
+	b.items = 0
+}
+
+// quote appends a terminal to the current rule.
+func (b *builder) q(s string) {
+	if b.items > 0 {
+		b.b.WriteString(" ")
+	}
+	b.b.WriteString(" ")
+	b.b.WriteString(strconv.Quote(s))
+}
+
+// u appends a non-terminal to the current rule.
+func (b *builder) u(s string) {
+	if b.items > 0 {
+		b.b.WriteString(" ")
+	}
+	b.b.WriteString(" ")
+	b.b.WriteString(s)
+}
+
+func buildConstrainedNumber(b *builder, s *jsonschema.Schema) {
+	if s.Minimum == 0 && s.Maximum == 0 {
+		b.u("TODO")
+	} else {
+		b.u("number")
+	}
+}

grammar/grammar_test.go

@@ -0,0 +1,75 @@
+package grammar
+
+import (
+	"bufio"
+	"cmp"
+	"iter"
+	"strings"
+	"testing"
+
+	_ "embed"
+
+	"github.com/ollama/ollama/grammar/internal/diff"
+)
+
+func TestFromSchema(t *testing.T) {
+	for tt := range testCases(t) {
+		t.Run(tt.name, func(t *testing.T) {
+			g, err := FromSchema(nil, []byte(tt.schema))
+			if err != nil {
+				t.Fatalf("FromSchema: %v", err)
+			}
+			got := string(g)
+			got = strings.TrimPrefix(got, jsonTerms)
+			if got != tt.want {
+				t.Logf("schema:\n%s", tt.schema)
+				t.Fatal(string(diff.Diff("got", []byte(got), "want", []byte(tt.want))))
+			}
+		})
+	}
+}
+
+type testCase struct {
+	name   string
+	schema string
+	want   string
+}
+
+//go:embed testdata/schemas.txt
+var tests string
+
+func testCases(t testing.TB) iter.Seq[testCase] {
+	t.Helper()
+	return func(yield func(testCase) bool) {
+		t.Helper()
+		sc := bufio.NewScanner(strings.NewReader(tests))
+		name := ""
+		for sc.Scan() {
+			line := strings.TrimSpace(sc.Text())
+			if line == "" {
+				name = ""
+				continue
+			}
+			if line[0] == '#' {
+				name = cmp.Or(name, strings.TrimSpace(line[1:]))
+				continue
+			}
+			s := sc.Text()
+			g := ""
+			for sc.Scan() {
+				line = strings.TrimSpace(sc.Text())
+				if line == "" || line[0] == '#' {
+					break
+				}
+				g += sc.Text() + "\n"
+			}
+			if !yield(testCase{name, s, g}) {
+				return
+			}
+			name = strings.TrimSpace(strings.TrimPrefix(line, "#"))
+		}
+		if err := sc.Err(); err != nil {
+			t.Fatalf("error reading tests: %v", err)
+		}
+	}
+}

grammar/internal/diff/diff.go

@@ -0,0 +1,261 @@
+// Copyright 2022 The Go Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style
+// license that can be found in the LICENSE file.
+
+package diff
+
+import (
+	"bytes"
+	"fmt"
+	"sort"
+	"strings"
+)
+
+// A pair is a pair of values tracked for both the x and y side of a diff.
+// It is typically a pair of line indexes.
+type pair struct{ x, y int }
+
+// Diff returns an anchored diff of the two texts old and new
+// in the “unified diff” format. If old and new are identical,
+// Diff returns a nil slice (no output).
+//
+// Unix diff implementations typically look for a diff with
+// the smallest number of lines inserted and removed,
+// which can in the worst case take time quadratic in the
+// number of lines in the texts. As a result, many implementations
+// either can be made to run for a long time or cut off the search
+// after a predetermined amount of work.
+//
+// In contrast, this implementation looks for a diff with the
+// smallest number of “unique” lines inserted and removed,
+// where unique means a line that appears just once in both old and new.
+// We call this an “anchored diff” because the unique lines anchor
+// the chosen matching regions. An anchored diff is usually clearer
+// than a standard diff, because the algorithm does not try to
+// reuse unrelated blank lines or closing braces.
+// The algorithm also guarantees to run in O(n log n) time
+// instead of the standard O(n²) time.
+//
+// Some systems call this approach a “patience diff,” named for
+// the “patience sorting” algorithm, itself named for a solitaire card game.
+// We avoid that name for two reasons. First, the name has been used
+// for a few different variants of the algorithm, so it is imprecise.
+// Second, the name is frequently interpreted as meaning that you have
+// to wait longer (to be patient) for the diff, meaning that it is a slower algorithm,
+// when in fact the algorithm is faster than the standard one.
+func Diff(oldName string, old []byte, newName string, new []byte) []byte {
+	if bytes.Equal(old, new) {
+		return nil
+	}
+	x := lines(old)
+	y := lines(new)
+
+	// Print diff header.
+	var out bytes.Buffer
+	fmt.Fprintf(&out, "diff %s %s\n", oldName, newName)
+	fmt.Fprintf(&out, "--- %s\n", oldName)
+	fmt.Fprintf(&out, "+++ %s\n", newName)
+
+	// Loop over matches to consider,
+	// expanding each match to include surrounding lines,
+	// and then printing diff chunks.
+	// To avoid setup/teardown cases outside the loop,
+	// tgs returns a leading {0,0} and trailing {len(x), len(y)} pair
+	// in the sequence of matches.
+	var (
+		done  pair     // printed up to x[:done.x] and y[:done.y]
+		chunk pair     // start lines of current chunk
+		count pair     // number of lines from each side in current chunk
+		ctext []string // lines for current chunk
+	)
+	for _, m := range tgs(x, y) {
+		if m.x < done.x {
+			// Already handled scanning forward from earlier match.
+			continue
+		}
+
+		// Expand matching lines as far as possible,
+		// establishing that x[start.x:end.x] == y[start.y:end.y].
+		// Note that on the first (or last) iteration we may (or definitely do)
+		// have an empty match: start.x==end.x and start.y==end.y.
+		start := m
+		for start.x > done.x && start.y > done.y && x[start.x-1] == y[start.y-1] {
+			start.x--
+			start.y--
+		}
+		end := m
+		for end.x < len(x) && end.y < len(y) && x[end.x] == y[end.y] {
+			end.x++
+			end.y++
+		}
+
+		// Emit the mismatched lines before start into this chunk.
+		// (No effect on first sentinel iteration, when start = {0,0}.)
+		for _, s := range x[done.x:start.x] {
+			ctext = append(ctext, "-"+s)
+			count.x++
+		}
+		for _, s := range y[done.y:start.y] {
+			ctext = append(ctext, "+"+s)
+			count.y++
+		}
+
+		// If we're not at EOF and have too few common lines,
+		// the chunk includes all the common lines and continues.
+		const C = 3 // number of context lines
+		if (end.x < len(x) || end.y < len(y)) &&
+			(end.x-start.x < C || (len(ctext) > 0 && end.x-start.x < 2*C)) {
+			for _, s := range x[start.x:end.x] {
+				ctext = append(ctext, " "+s)
+				count.x++
+				count.y++
+			}
+			done = end
+			continue
+		}
+
+		// End chunk with common lines for context.
+		if len(ctext) > 0 {
+			n := end.x - start.x
+			if n > C {
+				n = C
+			}
+			for _, s := range x[start.x : start.x+n] {
+				ctext = append(ctext, " "+s)
+				count.x++
+				count.y++
+			}
+			done = pair{start.x + n, start.y + n}
+
+			// Format and emit chunk.
+			// Convert line numbers to 1-indexed.
+			// Special case: empty file shows up as 0,0 not 1,0.
+			if count.x > 0 {
+				chunk.x++
+			}
+			if count.y > 0 {
+				chunk.y++
+			}
+			fmt.Fprintf(&out, "@@ -%d,%d +%d,%d @@\n", chunk.x, count.x, chunk.y, count.y)
+			for _, s := range ctext {
+				out.WriteString(s)
+			}
+			count.x = 0
+			count.y = 0
+			ctext = ctext[:0]
+		}
+
+		// If we reached EOF, we're done.
+		if end.x >= len(x) && end.y >= len(y) {
+			break
+		}
+
+		// Otherwise start a new chunk.
+		chunk = pair{end.x - C, end.y - C}
+		for _, s := range x[chunk.x:end.x] {
+			ctext = append(ctext, " "+s)
+			count.x++
+			count.y++
+		}
+		done = end
+	}
+
+	return out.Bytes()
+}
+
+// lines returns the lines in the file x, including newlines.
+// If the file does not end in a newline, one is supplied
+// along with a warning about the missing newline.
+func lines(x []byte) []string {
+	l := strings.SplitAfter(string(x), "\n")
+	if l[len(l)-1] == "" {
+		l = l[:len(l)-1]
+	} else {
+		// Treat last line as having a message about the missing newline attached,
+		// using the same text as BSD/GNU diff (including the leading backslash).
+		l[len(l)-1] += "\n\\ No newline at end of file\n"
+	}
+	return l
+}
+
+// tgs returns the pairs of indexes of the longest common subsequence
+// of unique lines in x and y, where a unique line is one that appears
+// once in x and once in y.
+//
+// The longest common subsequence algorithm is as described in
+// Thomas G. Szymanski, “A Special Case of the Maximal Common
+// Subsequence Problem,” Princeton TR #170 (January 1975),
+// available at https://research.swtch.com/tgs170.pdf.
+func tgs(x, y []string) []pair {
+	// Count the number of times each string appears in a and b.
+	// We only care about 0, 1, many, counted as 0, -1, -2
+	// for the x side and 0, -4, -8 for the y side.
+	// Using negative numbers now lets us distinguish positive line numbers later.
+	m := make(map[string]int)
+	for _, s := range x {
+		if c := m[s]; c > -2 {
+			m[s] = c - 1
+		}
+	}
+	for _, s := range y {
+		if c := m[s]; c > -8 {
+			m[s] = c - 4
+		}
+	}
+
+	// Now unique strings can be identified by m[s] = -1+-4.
+	//
+	// Gather the indexes of those strings in x and y, building:
+	//	xi[i] = increasing indexes of unique strings in x.
+	//	yi[i] = increasing indexes of unique strings in y.
+	//	inv[i] = index j such that x[xi[i]] = y[yi[j]].
+	var xi, yi, inv []int
+	for i, s := range y {
+		if m[s] == -1+-4 {
+			m[s] = len(yi)
+			yi = append(yi, i)
+		}
+	}
+	for i, s := range x {
+		if j, ok := m[s]; ok && j >= 0 {
+			xi = append(xi, i)
+			inv = append(inv, j)
+		}
+	}
+
+	// Apply Algorithm A from Szymanski's paper.
+	// In those terms, A = J = inv and B = [0, n).
+	// We add sentinel pairs {0,0}, and {len(x),len(y)}
+	// to the returned sequence, to help the processing loop.
+	J := inv
+	n := len(xi)
+	T := make([]int, n)
+	L := make([]int, n)
+	for i := range T {
+		T[i] = n + 1
+	}
+	for i := range n {
+		k := sort.Search(n, func(k int) bool {
+			return T[k] >= J[i]
+		})
+		T[k] = J[i]
+		L[i] = k + 1
+	}
+	k := 0
+	for _, v := range L {
+		if k < v {
+			k = v
+		}
+	}
+	seq := make([]pair, 2+k)
+	seq[1+k] = pair{len(x), len(y)} // sentinel at end
+	lastj := n
+	for i := n - 1; i >= 0; i-- {
+		if L[i] == k && J[i] < lastj {
+			seq[k] = pair{xi[i], yi[J[i]]}
+			k--
+		}
+	}
+	seq[0] = pair{0, 0} // sentinel at start
+	return seq
+}

grammar/internal/diff/diff_test.go

@@ -0,0 +1,44 @@
+// Copyright 2022 The Go Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style
+// license that can be found in the LICENSE file.
+
+package diff
+
+import (
+	"bytes"
+	"path/filepath"
+	"testing"
+
+	"golang.org/x/tools/txtar"
+)
+
+func clean(text []byte) []byte {
+	text = bytes.ReplaceAll(text, []byte("$\n"), []byte("\n"))
+	text = bytes.TrimSuffix(text, []byte("^D\n"))
+	return text
+}
+
+func Test(t *testing.T) {
+	files, _ := filepath.Glob("testdata/*.txt")
+	if len(files) == 0 {
+		t.Fatalf("no testdata")
+	}
+
+	for _, file := range files {
+		t.Run(filepath.Base(file), func(t *testing.T) {
+			a, err := txtar.ParseFile(file)
+			if err != nil {
+				t.Fatal(err)
+			}
+			if len(a.Files) != 3 || a.Files[2].Name != "diff" {
+				t.Fatalf("%s: want three files, third named \"diff\"", file)
+			}
+			diffs := Diff(a.Files[0].Name, clean(a.Files[0].Data), a.Files[1].Name, clean(a.Files[1].Data))
+			want := clean(a.Files[2].Data)
+			if !bytes.Equal(diffs, want) {
+				t.Fatalf("%s: have:\n%s\nwant:\n%s\n%s", file,
+					diffs, want, Diff("have", diffs, "want", want))
+			}
+		})
+	}
+}

grammar/internal/diff/testdata/allnew.txt

@@ -0,0 +1,13 @@
+-- old --
+-- new --
+a
+b
+c
+-- diff --
+diff old new
+--- old
++++ new
+@@ -0,0 +1,3 @@
++a
++b
++c

grammar/internal/diff/testdata/allold.txt

@@ -0,0 +1,13 @@
+-- old --
+a
+b
+c
+-- new --
+-- diff --
+diff old new
+--- old
++++ new
+@@ -1,3 +0,0 @@
+-a
+-b
+-c

grammar/internal/diff/testdata/basic.txt

@@ -0,0 +1,35 @@
+Example from Hunt and McIlroy, “An Algorithm for Differential File Comparison.”
+https://www.cs.dartmouth.edu/~doug/diff.pdf
+
+-- old --
+a
+b
+c
+d
+e
+f
+g
+-- new --
+w
+a
+b
+x
+y
+z
+e
+-- diff --
+diff old new
+--- old
++++ new
+@@ -1,7 +1,7 @@
++w
+ a
+ b
+-c
+-d
++x
++y
++z
+ e
+-f
+-g

grammar/internal/diff/testdata/dups.txt

@@ -0,0 +1,40 @@
+-- old --
+a
+
+b
+
+c
+
+d
+
+e
+
+f
+-- new --
+a
+
+B
+
+C
+
+d
+
+e
+
+f
+-- diff --
+diff old new
+--- old
++++ new
+@@ -1,8 +1,8 @@
+ a
+ $
+-b
+-
+-c
++B
++
++C
+ $
+ d
+ $

grammar/internal/diff/testdata/end.txt

@@ -0,0 +1,38 @@
+-- old --
+1
+2
+3
+4
+5
+6
+7
+eight
+nine
+ten
+eleven
+-- new --
+1
+2
+3
+4
+5
+6
+7
+8
+9
+10
+-- diff --
+diff old new
+--- old
++++ new
+@@ -5,7 +5,6 @@
+ 5
+ 6
+ 7
+-eight
+-nine
+-ten
+-eleven
++8
++9
++10

grammar/internal/diff/testdata/eof.txt

@@ -0,0 +1,9 @@
+-- old --
+a
+b
+c^D
+-- new --
+a
+b
+c^D
+-- diff --

grammar/internal/diff/testdata/eof1.txt

@@ -0,0 +1,18 @@
+-- old --
+a
+b
+c
+-- new --
+a
+b
+c^D
+-- diff --
+diff old new
+--- old
++++ new
+@@ -1,3 +1,3 @@
+ a
+ b
+-c
++c
+\ No newline at end of file

grammar/internal/diff/testdata/eof2.txt

@@ -0,0 +1,18 @@
+-- old --
+a
+b
+c^D
+-- new --
+a
+b
+c
+-- diff --
+diff old new
+--- old
++++ new
+@@ -1,3 +1,3 @@
+ a
+ b
+-c
+\ No newline at end of file
++c

grammar/internal/diff/testdata/long.txt

@@ -0,0 +1,62 @@
+-- old --
+1
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11
+12
+13
+14
+14½
+15
+16
+17
+18
+19
+20
+-- new --
+1
+2
+3
+4
+5
+6
+8
+9
+10
+11
+12
+13
+14
+17
+18
+19
+20
+-- diff --
+diff old new
+--- old
++++ new
+@@ -4,7 +4,6 @@
+ 4
+ 5
+ 6
+-7
+ 8
+ 9
+ 10
+@@ -12,9 +11,6 @@
+ 12
+ 13
+ 14
+-14½
+-15
+-16
+ 17
+ 18
+ 19

grammar/internal/diff/testdata/same.txt

@@ -0,0 +1,5 @@
+-- old --
+hello world
+-- new --
+hello world
+-- diff --

grammar/internal/diff/testdata/start.txt

@@ -0,0 +1,34 @@
+-- old --
+e
+pi
+4
+5
+6
+7
+8
+9
+10
+-- new --
+1
+2
+3
+4
+5
+6
+7
+8
+9
+10
+-- diff --
+diff old new
+--- old
++++ new
+@@ -1,5 +1,6 @@
+-e
+-pi
++1
++2
++3
+ 4
+ 5
+ 6

grammar/internal/diff/testdata/triv.txt

@@ -0,0 +1,40 @@
+Another example from Hunt and McIlroy,
+“An Algorithm for Differential File Comparison.”
+https://www.cs.dartmouth.edu/~doug/diff.pdf
+
+Anchored diff gives up on finding anything,
+since there are no unique lines.
+
+-- old --
+a
+b
+c
+a
+b
+b
+a
+-- new --
+c
+a
+b
+a
+b
+c
+-- diff --
+diff old new
+--- old
++++ new
+@@ -1,7 +1,6 @@
+-a
+-b
+-c
+-a
+-b
+-b
+-a
++c
++a
++b
++a
++b
++c

grammar/jsonschema/decode.go

@@ -0,0 +1,171 @@
+package jsonschema
+
+import (
+	"bytes"
+	"encoding/json"
+	"errors"
+)
+
+// Schema holds a JSON schema.
+type Schema struct {
+	// Name is the name of the property. For the parent/root property, this
+	// is "root". For child properties, this is the name of the property.
+	Name string `json:"-"`
+
+	// Type is the type of the property.
+	//
+	// TODO: Union types (e.g. make this a []string).
+	Type string
+
+	// PrefixItems is a list of schemas for each item in a tuple. By
+	// default, the tuple is "closed." unless Items is set to true or a
+	// valid Schema.
+	PrefixItems []*Schema
+
+	// Items is the schema for each item in a list.
+	//
+	// If it is missing, or its JSON value is "null" or "false", it is nil.
+	// If the JSON value is "true", it is set to the empty Schema. If the
+	// JSON value is an object, it will be decoded as a Schema.
+	Items *Schema
+
+	// MinItems specifies the minimum number of items allowed in a list.
+	MinItems int
+
+	// MaxItems specifies the maximum number of items allowed in a list.
+	MaxItems int
+
+	// Properties is the schema for each property of an object.
+	Properties []*Schema
+
+	// Format is the format of the property. This is used to validate the
+	// property against a specific format.
+	//
+	// It is the callers responsibility to validate the property against
+	// the format.
+	Format string
+
+	// Minimum specifies the minimum value for numeric properties.
+	Minimum float64
+
+	// Maximum specifies the maximum value for numeric properties.
+	Maximum float64
+
+	// Enum is a list of valid values for the property.
+	Enum []json.RawMessage
+}
+
+func (s *Schema) UnmarshalJSON(data []byte) error {
+	type S Schema
+	w := struct {
+		Properties props
+		Items      items
+		*S
+	}{
+		S: (*S)(s),
+	}
+	if err := json.Unmarshal(data, &w); err != nil {
+		return err
+	}
+	if w.Items.set {
+		s.Items = &w.Items.Schema
+	}
+	s.Properties = w.Properties
+	return nil
+}
+
+type items struct {
+	Schema
+	set bool
+}
+
+func (s *items) UnmarshalJSON(data []byte) error {
+	switch b := data[0]; b {
+	case 't':
+		*s = items{set: true}
+	case '{':
+		type I items
+		if err := json.Unmarshal(data, (*I)(s)); err != nil {
+			return err
+		}
+		s.set = true
+	case 'n', 'f':
+	default:
+		return errors.New("invalid Items")
+	}
+	return nil
+}
+
+// EffectiveType returns the effective type of the schema. If the Type field is
+// not empty, it is returned; otherwise:
+//
+//   - If the schema has both Properties and Items, it returns an empty string.
+//   - If the schema has Properties, it returns "object".
+//   - If the schema has Items, it returns "array".
+//   - If the schema has neither Properties nor Items, it returns "value".
+//
+// The returned string is never empty.
+func (d *Schema) EffectiveType() string {
+	if d.Type == "" {
+		if len(d.Properties) > 0 {
+			return "object"
+		}
+		if len(d.PrefixItems) > 0 || d.Items != nil {
+			return "array"
+		}
+		return "value"
+	}
+	return d.Type
+}
+
+// props is an ordered list of properties. The order of the properties
+// is the order in which they were defined in the schema.
+type props []*Schema
+
+var _ json.Unmarshaler = (*props)(nil)
+
+func (v *props) UnmarshalJSON(data []byte) error {
+	if len(data) == 0 {
+		return nil
+	}
+	if data[0] != '{' {
+		return errors.New("expected object")
+	}
+
+	d := json.NewDecoder(bytes.NewReader(data))
+
+	// TODO(bmizerany): Consider DisallowUnknownFields. Currently, we, like
+	// llama.cpp, ignore unknown fields, which could be lead to unexpected
+	// behavior for clients of this package, since they may not be aware
+	// that "additionalFields", "itemsPrefix", etc, are being ignored.
+	//
+	// For now, just do what llama.cpp does.
+
+	t, err := d.Token()
+	if err != nil {
+		return err
+	}
+	if t != json.Delim('{') {
+		return errors.New("expected object")
+	}
+	for d.More() {
+		// Use the first token (map key) as the property name, then
+		// decode the rest of the object fields into a Schema and
+		// append.
+		t, err := d.Token()
+		if err != nil {
+			return err
+		}
+		if t == json.Delim('}') {
+			return nil
+		}
+		s := &Schema{
+			Name: t.(string),
+		}
+		if err := d.Decode(s); err != nil {
+			return err
+		}
+		*v = append(*v, s)
+	}
+	return nil
+}

grammar/jsonschema/decode_test.go

@@ -0,0 +1,104 @@
+package jsonschema
+
+import (
+	"encoding/json"
+	"reflect"
+	"strings"
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+)
+
+const testSchemaBasic = `
+{
+  "properties": {
+    "tupleClosedEmpty":   { "prefixItems": [] },
+    "tupleClosedMissing": { "prefixItems": [{}] },
+    "tupleClosedNull":    { "prefixItems": [{}], "items": null },
+    "tupleClosedFalse":   { "prefixItems": [{}], "items": false },
+    "tupleOpenTrue":      { "prefixItems": [{}], "items": true },
+    "tupleOpenEmpty":     { "prefixItems": [{}], "items": {} },
+    "tupleOpenTyped":     { "prefixItems": [{}], "items": {"type": "boolean"} },
+    "tupleOpenMax":       { "prefixItems": [{}], "items": true, "maxItems": 3},
+
+    "array": { "items": {"type": "number"} },
+
+    "null": { "type": "null" },
+    "string": { "type": "string" },
+    "boolean": { "type": "boolean" }
+  }
+}
+`
+
+func TestSchemaUnmarshal(t *testing.T) {
+	var got *Schema
+	if err := json.Unmarshal([]byte(testSchemaBasic), &got); err != nil {
+		t.Fatalf("Unmarshal: %v", err)
+	}
+	want := &Schema{
+		Properties: []*Schema{
+			{Name: "tupleClosedEmpty", PrefixItems: []*Schema{}, Items: nil},
+			{Name: "tupleClosedMissing", PrefixItems: []*Schema{{}}, Items: nil},
+			{Name: "tupleClosedNull", PrefixItems: []*Schema{{}}, Items: nil},
+			{Name: "tupleClosedFalse", PrefixItems: []*Schema{{}}, Items: nil},
+
+			{Name: "tupleOpenTrue", PrefixItems: []*Schema{{}}, Items: &Schema{}},
+			{Name: "tupleOpenEmpty", PrefixItems: []*Schema{{}}, Items: &Schema{}},
+			{Name: "tupleOpenTyped", PrefixItems: []*Schema{{}}, Items: &Schema{Type: "boolean"}},
+			{Name: "tupleOpenMax", PrefixItems: []*Schema{{}}, Items: &Schema{}, MaxItems: 3},
+
+			{Name: "array", Items: &Schema{Type: "number"}},
+
+			{Name: "null", Type: "null"},
+			{Name: "string", Type: "string"},
+			{Name: "boolean", Type: "boolean"},
+		},
+	}
+
+	if diff := cmp.Diff(want, got); diff != "" {
+		t.Errorf("(-want, +got)\n%s", diff)
+	}
+}
+
+func TestEffectiveType(t *testing.T) {
+	const schema = `
+		{"properties": {
+			"o": {"type": "object"},
+			"a": {"type": "array"},
+			"n": {"type": "number"},
+			"s": {"type": "string"},
+			"z": {"type": "null"},
+			"b": {"type": "boolean"},
+
+			"t0": {"prefixItems": [{}], "items": {"type": "number"}},
+			"t1": {"items": {"type": "number"}, "maxItems": 3},
+
+			"v": {"maxItems": 3}
+		}}
+	`
+
+	var s *Schema
+	if err := json.Unmarshal([]byte(schema), &s); err != nil {
+		t.Fatalf("json.Unmarshal: %v", err)
+	}
+
+	var got []string
+	for _, p := range s.Properties {
+		got = append(got, p.EffectiveType())
+	}
+
+	want := strings.Fields(`
+		object
+		array
+		number
+		string
+		null
+		boolean
+		array
+		array
+		value
+	`)
+	if !reflect.DeepEqual(want, got) {
+		t.Errorf("\ngot:\n\t%v\nwant:\n\t%v", got, want)
+	}
+}

grammar/testdata/schemas.txt

@@ -0,0 +1,76 @@
+# This file holds tests for JSON schema to EBNF grammar conversions.
+#
+# The format is a JSON schema, followed by the expected EBNF grammar. Each test
+# MAY be preceded by a comment that describes the test (e.g. the test name), followed by
+# the JSON schema and the expected EBNF grammar. If no comment is present, the test
+# name the tests number in the file (e.g. "#0", "#1", etc.)
+#
+# Blank lines signify the end or start of a new test. Comments can be added
+# anywhere in the file, but they must be preceded by a '#' character and start at
+# the beginning of the line.
+
+# default
+{}
+root ::= value;
+
+{"properties": {}}
+root ::= value;
+
+# array
+{"properties": {"a": {"type": "array", "items": {"type": "string"}}}}
+root_0_tuple_0 ::= string;
+root_0         ::= "[" ( root_0_tuple_0 )* "]";
+root           ::= "{" "a" ":" root_0 "}";
+
+# array with nested array
+{"type": "array", "items": {"type": "array", "items": {"type": "string"}}}
+root_tuple_0_tuple_0 ::= string;
+root_tuple_0         ::= "[" ( root_tuple_0_tuple_0 )* "]";
+root                 ::= "[" ( root_tuple_0 )* "]";
+
+# object
+{"properties": {"e": {}}}
+root_0 ::= value;
+root   ::= "{" "e" ":" root_0 "}";
+
+# object with nested object
+{"properties": {"o": {"type": "object", "properties": {"e": {}}}}}
+root_0_0 ::= value;
+root_0   ::= "{" "e" ":" root_0_0 "}";
+root     ::= "{" "o" ":" root_0 "}";
+
+# boolean
+{"type": "boolean"}
+root ::= boolean;
+
+# number
+{"properties": {"n": {"type": "number", "minimum": 123, "maximum": 4567}}}
+root_0 ::= number;
+root   ::= "{" "n" ":" root_0 "}";
+
+# string
+{"type": "string"}
+root ::= string;
+
+# string with enum
+{"type": "string", "enum": ["a", "b", "c"]}
+root ::= ( "\"a\"" "|" "\"b\"" "|" "\"c\"" );
+
+# spaces in key
+{"properties": {"a b": {}}}
+root_0 ::= value;
+root   ::= "{" "a b" ":" root_0 "}";
+
+# issue7978
+{ "type": "object", "properties": { "steps": { "type": "array", "items": { "type": "object", "properties": { "explanation": { "type": "string" }, "output": { "type": "string" } }, "required": [ "explanation", "output" ], "additionalProperties": false } }, "final_answer": { "type": "string" } }, "required": [ "steps", "final_answer" ], "additionalProperties": false }
+root_0_tuple_0_0 ::= string;
+root_0_tuple_0_1 ::= string;
+root_0_tuple_0   ::= "{" "explanation" ":" root_0_tuple_0_0 "," "output" ":" root_0_tuple_0_1 "}";
+root_0           ::= "[" ( root_0_tuple_0 )* "]";
+root_1           ::= string;
+root             ::= "{" "steps" ":" root_0 "," "final_answer" ":" root_1 "}";
+
+# !! # special characters in key
+# !! {"properties": {"a!b": {}}}
+# !! !invalid character '!' in key
+# !! 

integration/llm_image_test.go

@@ -66,6 +66,35 @@ func TestIntegrationMllama(t *testing.T) {
 	DoGenerate(ctx, t, client, req, []string{resp}, 240*time.Second, 30*time.Second)
 }
 
+func TestIntegrationSplitBatch(t *testing.T) {
+	image, err := base64.StdEncoding.DecodeString(imageEncoding)
+	require.NoError(t, err)
+	req := api.GenerateRequest{
+		Model: "gemma3:4b",
+		// Fill up a chunk of the batch so the image will partially spill over into the next one
+		System: "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed aliquet, justo in malesuada lobortis, odio ligula volutpat quam, quis faucibus ipsum magna quis sapien. Aliquam in venenatis diam, eu viverra magna. Phasellus imperdiet hendrerit volutpat. Vivamus sem ex, facilisis placerat felis non, dictum elementum est. Phasellus aliquam imperdiet lacus, eget placerat ligula sodales vel. Pellentesque nec auctor mi. Curabitur arcu nisi, faucibus eget nunc id, viverra interdum mi. Curabitur ornare ipsum ex, ac euismod ex aliquam in. Vestibulum id magna at purus accumsan fermentum. Proin scelerisque posuere nunc quis interdum. Maecenas sed mollis nisl. Etiam vitae ipsum interdum, placerat est quis, tincidunt velit. Nullam tempor nibh non lorem volutpat efficitur. Cras laoreet diam imperdiet ipsum auctor bibendum. Suspendisse ultrices urna sed metus sagittis suscipit. Quisque ullamcorper aliquam nibh ut mollis. Aenean dapibus mauris pharetra, venenatis elit ac, hendrerit odio. Cras vestibulum erat tempor, lobortis justo eu, lobortis ipsum. Nam laoreet dapibus sem. Proin vel diam ultrices, elementum ante et, ornare lectus. Proin eu accumsan nisl. Praesent ac ex vitae ipsum vulputate tristique facilisis sit amet lacus. Nullam faucibus magna a pellentesque pretium. Nunc lacinia ullamcorper sollicitudin. Donec vitae accumsan turpis, sed porttitor est. Donec porttitor mi vitae augue faucibus, vel mollis diam tincidunt.",
+		Prompt: "what does the text in this image say?",
+		Stream: &stream,
+		Options: map[string]interface{}{
+			"seed":        42,
+			"temperature": 0.0,
+		},
+		Images: []api.ImageData{
+			image,
+		},
+	}
+
+	// Note: sometimes it returns "the ollamas" sometimes "the ollams"
+	resp := "the ollam"
+	ctx, cancel := context.WithTimeout(context.Background(), 3*time.Minute)
+	defer cancel()
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
+	require.NoError(t, PullIfMissing(ctx, client, req.Model))
+	// llava models on CPU can be quite slow to start,
+	DoGenerate(ctx, t, client, req, []string{resp}, 120*time.Second, 30*time.Second)
+}
+
 const imageEncoding = `iVBORw0KGgoAAAANSUhEUgAAANIAAAB4CAYAAACHHqzKAAAAAXNSR0IArs4c6QAAAIRlWElmTU0AKgAAAAgABQESAAMAAAABAAEAAAEaAAUAAAABAAAASgEb
 AAUAAAABAAAAUgEoAAMAAAABAAIAAIdpAAQAAAABAAAAWgAAAAAAAABIAAAAAQAAAEgAAAABAAOgAQADAAAAAQABAACgAgAEAAAAAQAAANKgAwAEAAAAAQAA
 AHgAAAAAXdsepgAAAAlwSFlzAAALEwAACxMBAJqcGAAAAVlpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6

kvcache/cache.go

@@ -4,6 +4,7 @@ import (
 	"errors"
 
 	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model/input"
 )
 
 var (
@@ -29,10 +30,26 @@ type Cache interface {
 	// cache implementation used.
 	Put(ctx ml.Context, key, value ml.Tensor)
 
+	// SetConfig controls optimizations (mostly backend-specific) that may transform
+	// the output of the cache to work better with specific kernels. If not called,
+	// the backend settings will be used. This works well when calling Attention.
+	//
+	// The config can be overridden by models, especially if they require vanilla
+	// output when implementing their own version of attention. To do this, pass
+	// an empty ml.CacheConfig.
+	//
+	// Most models will not need to use this.
+	SetConfig(ml.CacheConfig)
+
 	// ** cache management **
 
-	// Init sets up runtime parameters
-	Init(backend ml.Backend, dtype ml.DType, capacity int32)
+	// Init sets up runtime parameters.
+	// backend: Used to allocate cache data storage and execute management operations (such as defrag)
+	// dtype: The data type for storing cache entries
+	// maxSequences: The maximum number of sequences stored in the cache - across all batches
+	// capacity: The number of cache entries to store, per sequence
+	// maxBatch: The maximum number of tokens that can occur in a single batch
+	Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int)
 
 	// Close closes the cache and frees resources associated with it
 	Close()
@@ -40,7 +57,7 @@ type Cache interface {
 	// StartForward is called before the start of the model's forward pass.
 	// For each token in the coming batch, there must be a corresponding
 	// entry in positions and seqs.
-	StartForward(ctx ml.Context, positions []int32, seqs []int) error
+	StartForward(ctx ml.Context, batch input.Batch) error
 
 	// CopyPrefix copies tokens in the range [0, len) from srcSeq to dstSeq
 	CopyPrefix(srcSeq, dstSeq int, len int32)

kvcache/causal.go

@@ -8,6 +8,7 @@ import (
 	"slices"
 
 	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model/input"
 )
 
 type shiftFn func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error)
@@ -19,9 +20,13 @@ type shiftFn func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, e
 // The mask is of shape history size, batch size
 type Causal struct {
 	DType      ml.DType
-	Capacity   int32
 	windowSize int32
 
+	opts CausalOptions
+
+	// config controls mostly backend-specific optimizations
+	config *ml.CacheConfig
+
 	// ** current forward pass **
 
 	// the active layer for Get and Put
@@ -39,6 +44,12 @@ type Causal struct {
 	// locations in the cache that are needed for this batch
 	curCellRange cellRange
 
+	// curSequences is the sequences corresponding to this pass's entries in the cache
+	curSequences []int
+
+	// curPositions is the positions corresponding to this pass's entries in the cache
+	curPositions []int32
+
 	// ** cache metadata **
 
 	// for each possible location in the cache, stores the position and set of sequences
@@ -52,8 +63,8 @@ type Causal struct {
 
 	shiftFn      shiftFn
 	backend      ml.Backend
-	cacheCtx     ml.Context
-	keys, values []ml.Tensor
+	ctxs         map[int]ml.Context
+	keys, values map[int]ml.Tensor
 }
 
 type cacheCell struct {
@@ -67,28 +78,81 @@ type cellRange struct {
 }
 
 func NewCausalCache(shift shiftFn) *Causal {
-	return &Causal{windowSize: math.MaxInt32, shiftFn: shift}
+	return &Causal{
+		windowSize: math.MaxInt32,
+		shiftFn:    shift,
+		ctxs:       make(map[int]ml.Context),
+		keys:       make(map[int]ml.Tensor),
+		values:     make(map[int]ml.Tensor),
+	}
 }
 
 func NewSWACache(windowSize int32, shift shiftFn) *Causal {
-	return &Causal{windowSize: windowSize, shiftFn: shift}
+	return &Causal{
+		windowSize: windowSize,
+		shiftFn:    shift,
+		ctxs:       make(map[int]ml.Context),
+		keys:       make(map[int]ml.Tensor),
+		values:     make(map[int]ml.Tensor),
+	}
 }
 
-func (c *Causal) Init(backend ml.Backend, dtype ml.DType, capacity int32) {
+func (c *Causal) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
+	if c.config == nil {
+		var config ml.CacheConfig
+		if cc, ok := backend.(ml.BackendCacheConfig); ok {
+			config = cc.CacheConfig()
+		}
+		c.config = &config
+	}
+
+	if c.config.CachePadding == 0 {
+		c.config.CachePadding = 1
+	}
+
+	if c.config.MaskBatchPadding == 0 {
+		c.config.MaskBatchPadding = 1
+	}
+
+	if c.config.MaskDType == ml.DTypeOther {
+		c.config.MaskDType = ml.DTypeF32
+	}
+
+	var cacheSize int
+	if c.windowSize == math.MaxInt32 || capacity < int(c.windowSize)+maxBatch {
+		cacheSize = maxSequences * capacity
+	} else {
+		cacheSize = maxSequences * (int(c.windowSize) + maxBatch)
+	}
+	cacheSize = roundUp(cacheSize, c.config.CachePadding)
+	c.cells = make([]cacheCell, cacheSize)
+
 	c.DType = dtype
-	c.Capacity = capacity
-	c.cells = make([]cacheCell, capacity)
 	c.cellRanges = make(map[int]cellRange)
 	c.backend = backend
-	c.cacheCtx = backend.NewContext()
+}
+
+func (c *Causal) SetConfig(config ml.CacheConfig) {
+	if c.config != nil {
+		panic("config cannot be changed after being previously set, either by the model or backend")
+	}
+
+	c.config = &config
 }
 
 func (c *Causal) Close() {
-	c.cacheCtx.Close()
+	for _, ctx := range c.ctxs {
+		ctx.Close()
+	}
 }
 
-func (c *Causal) StartForward(ctx ml.Context, positions []int32, seqs []int) error {
-	c.curBatchSize = len(positions)
+func (c *Causal) StartForward(ctx ml.Context, batch input.Batch) error {
+	c.curBatchSize = len(batch.Positions)
+	c.curSequences = batch.Sequences
+	c.curPositions = batch.Positions
+	c.opts.Except = nil
+
+	c.updateSlidingWindow()
 
 	var err error
 	c.curLoc, err = c.findStartLoc()
@@ -101,8 +165,8 @@ func (c *Causal) StartForward(ctx ml.Context, positions []int32, seqs []int) err
 	}
 
 	c.curCellRange = newRange()
-	for i, pos := range positions {
-		seq := seqs[i]
+	for i, pos := range batch.Positions {
+		seq := batch.Sequences[i]
 
 		c.cells[c.curLoc+i] = cacheCell{pos: pos, sequences: []int{seq}}
 
@@ -127,7 +191,7 @@ func (c *Causal) StartForward(ctx ml.Context, positions []int32, seqs []int) err
 		c.cellRanges[seq] = seqRange
 	}
 
-	c.curMask, err = c.buildMask(ctx, positions, seqs)
+	c.curMask, err = c.buildMask(ctx)
 
 	return err
 }
@@ -154,39 +218,138 @@ func (c *Causal) findStartLoc() (int, error) {
 		}
 	}
 
-	return 0, fmt.Errorf("%w (length: %v)", ErrKvCacheFull, c.Capacity)
+	return 0, fmt.Errorf("%w (length: %v)", ErrKvCacheFull, len(c.cells))
+}
+
+func (c *Causal) updateSlidingWindow() {
+	if c.windowSize == math.MaxInt32 {
+		return
+	}
+
+	// create a map of unique sequences to the lowest position in that sequence
+	lowestPos := make(map[int]int32)
+	for i := range c.curPositions {
+		seq := c.curSequences[i]
+
+		pos, ok := lowestPos[seq]
+		if !ok {
+			pos = c.curPositions[i]
+		} else if c.curPositions[i] < pos {
+			pos = c.curPositions[i]
+		}
+
+		lowestPos[seq] = pos
+	}
+
+	// delete any entries that are beyond the window of the oldest position in the sequence
+	for seq, pos := range lowestPos {
+		oldRange, ok := c.cellRanges[seq]
+		if !ok {
+			continue
+		}
+
+		newRange := newRange()
+
+		for i := oldRange.min; i <= oldRange.max; i++ {
+			if slices.Contains(c.cells[i].sequences, seq) {
+				if c.cells[i].pos < pos-c.windowSize {
+					c.cells[i].sequences = slices.DeleteFunc(c.cells[i].sequences, func(s int) bool { return s == seq })
+				} else {
+					newRange.min = min(newRange.min, i)
+					newRange.max = max(newRange.max, i)
+				}
+			}
+		}
+
+		c.cellRanges[seq] = newRange
+	}
+}
+
+func roundDown(length, pad int) int {
+	return (length / pad) * pad
+}
+
+func roundUp(length, pad int) int {
+	return ((length + pad - 1) / pad) * pad
 }
 
 // Builds a mask of history x batch indicating whether for each token in the batch the
 // token in the history should apply. This is based on both the sequence and causality (the
 // position of the history is not ahead of the token in the batch).
-func (c *Causal) buildMask(ctx ml.Context, positions []int32, seqs []int) (ml.Tensor, error) {
-	// TODO(jessegross): This does not do padding, which is required for flash attention
-	len := c.curCellRange.max - c.curCellRange.min + 1
-	mask := make([]float32, c.curBatchSize*len)
+func (c *Causal) buildMask(ctx ml.Context) (ml.Tensor, error) {
+	// Align and pad the two dimensions as required by the backend
+	batchSize := roundUp(c.curBatchSize, c.config.MaskBatchPadding)
+
+	c.curCellRange.min = roundDown(c.curCellRange.min, c.config.CachePadding)
+	c.curCellRange.max = roundUp(c.curCellRange.max+1, c.config.CachePadding) - 1
+
+	length := c.curCellRange.max - c.curCellRange.min + 1
+	mask := make([]float32, batchSize*length)
 
 	for i := range c.curBatchSize {
+		enabled := !slices.Contains(c.opts.Except, i)
 		for j := c.curCellRange.min; j <= c.curCellRange.max; j++ {
-			if !slices.Contains(c.cells[j].sequences, seqs[i]) || c.cells[j].pos > positions[i] ||
-				c.cells[j].pos < positions[i]-c.windowSize {
-				mask[i*len+(j-c.curCellRange.min)] = float32(math.Inf(-1))
+			if !slices.Contains(c.cells[j].sequences, c.curSequences[i]) ||
+				(enabled && c.cells[j].pos > c.curPositions[i]) ||
+				c.cells[j].pos < c.curPositions[i]-c.windowSize {
+				mask[i*length+(j-c.curCellRange.min)] = float32(math.Inf(-1))
 			}
 		}
 	}
 
-	return ctx.FromFloatSlice(mask, len, c.curBatchSize)
+	// Mask out any padding tokens we added. For padding that we added to the cache history, this
+	// has already been masked out because the sequence doesn't match.
+	for i := c.curBatchSize * length; i < len(mask); i++ {
+		mask[i] = float32(math.Inf(-1))
+	}
+
+	maskTensor, err := ctx.Input().FromFloatSlice(mask, length, batchSize)
+	if err != nil {
+		return nil, err
+	}
+
+	if c.config.MaskDType != ml.DTypeF32 {
+		out := ctx.Input().Empty(c.config.MaskDType, maskTensor.Shape()...)
+		ctx.Forward(maskTensor.Copy(ctx, out))
+		maskTensor = out
+	}
+
+	return maskTensor, nil
 }
 
-func moveCell(ctx ml.Context, objs []ml.Tensor, src, dst, len int) {
-	for _, obj := range objs {
-		if obj == nil {
+func (c *Causal) moveCells(ctx ml.Context, src, dst, length int) {
+	for i, key := range c.keys {
+		if key == nil {
 			continue
 		}
 
-		srcView := obj.View(ctx, obj.Stride(2)*src, obj.Dim(0)*obj.Dim(1)*len)
-		dstView := obj.View(ctx, obj.Stride(2)*dst, obj.Dim(0)*obj.Dim(1)*len)
+		kHeadDim := key.Dim(0)
+		numKVHeads := key.Dim(1)
+		rowSize := key.Stride(2)
 
-		ctx.Forward(srcView.Copy(ctx, dstView))
+		kSrcView := key.View(ctx, rowSize*src, kHeadDim*numKVHeads*length)
+		kDstView := key.View(ctx, rowSize*dst, kHeadDim*numKVHeads*length)
+
+		value := c.values[i]
+		var vSrcView, vDstView ml.Tensor
+		if c.config.PermutedV {
+			vHeadDim := value.Dim(1)
+			elemSize := value.Stride(0)
+
+			vSrcView = value.View(ctx, elemSize*src, length, len(c.cells)*elemSize, vHeadDim*numKVHeads)
+			vDstView = value.View(ctx, elemSize*dst, length, len(c.cells)*elemSize, vHeadDim*numKVHeads)
+		} else {
+			vHeadDim := value.Dim(0)
+			rowSize := value.Stride(2)
+
+			vSrcView = value.View(ctx, rowSize*src, vHeadDim*numKVHeads*length)
+			vDstView = value.View(ctx, rowSize*dst, vHeadDim*numKVHeads*length)
+		}
+
+		ctx.Forward(
+			kSrcView.Copy(ctx, kDstView),
+			vSrcView.Copy(ctx, vDstView),
+		)
 	}
 }
 
@@ -210,7 +373,8 @@ func (c *Causal) defrag() {
 	ctx := c.backend.NewContext()
 
 	// For every move, 6 tensors are required per layer (2 views and a
-	// copy for each of k and v).
+	// copy for each of k and v). We also need to refer to the original
+	// k and v cache tensors - once per layer, not per move.
 	layers := 0
 	for _, key := range c.keys {
 		if key == nil {
@@ -219,7 +383,7 @@ func (c *Causal) defrag() {
 		layers++
 	}
 
-	maxMoves := ctx.MaxTensors() / (6 * layers)
+	maxMoves := (ctx.MaxGraphNodes() - 2*layers) / (6 * layers)
 	moves := 0
 
 	var pendingSrc, pendingDst, pendingLen int
@@ -238,8 +402,7 @@ func (c *Causal) defrag() {
 							pendingLen++
 							break
 						} else {
-							moveCell(ctx, c.keys, pendingSrc, pendingDst, pendingLen)
-							moveCell(ctx, c.values, pendingSrc, pendingDst, pendingLen)
+							c.moveCells(ctx, pendingSrc, pendingDst, pendingLen)
 							moves++
 						}
 					}
@@ -263,8 +426,7 @@ func (c *Causal) defrag() {
 	}
 
 	if pendingLen > 0 {
-		moveCell(ctx, c.keys, pendingSrc, pendingDst, pendingLen)
-		moveCell(ctx, c.values, pendingSrc, pendingDst, pendingLen)
+		c.moveCells(ctx, pendingSrc, pendingDst, pendingLen)
 		moves++
 	}
 
@@ -293,45 +455,107 @@ func (c *Causal) defrag() {
 }
 
 func (c *Causal) SetLayer(layer int) {
-	if layer >= len(c.keys) {
-		c.keys = append(c.keys, make([]ml.Tensor, layer-len(c.keys)+1)...)
-		c.values = append(c.values, make([]ml.Tensor, layer-len(c.values)+1)...)
-	}
-
 	c.curLayer = layer
 }
 
+type CausalOptions struct {
+	// Enabled controls whether the causal mask is generated for a particular index in a batch
+	Except []int
+}
+
+// SetCausal disables causal mask generation for a particular range of indicies in
+// the current batch for subsequent calls to Get. The state resets for the next forward pass.
+func (c *Causal) SetCausal(ctx ml.Context, opts CausalOptions) {
+	if !slices.Equal(c.opts.Except, opts.Except) {
+		c.opts = opts
+		if ctx != nil {
+			var err error
+			c.curMask, err = c.buildMask(ctx)
+			if err != nil {
+				// This error should never occur because we have previously built a mask with the same shape
+				panic(fmt.Errorf("SetCausal: %w", err))
+			}
+		}
+	}
+}
+
 func (c *Causal) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
 	key := c.keys[c.curLayer]
 	value := c.values[c.curLayer]
 
-	key = key.View(ctx, key.Stride(2)*c.curCellRange.min,
-		key.Dim(0), key.Stride(1),
-		key.Dim(1), key.Stride(2),
-		c.curMask.Dim(0),
+	kHeadDim := key.Dim(0)
+	numKVHeads := key.Dim(1)
+	rowSize := key.Stride(2)
+	cachedSize := c.curMask.Dim(0)
+
+	key = key.View(ctx, rowSize*c.curCellRange.min,
+		kHeadDim, key.Stride(1),
+		numKVHeads, key.Stride(2),
+		cachedSize,
 	)
 
-	value = value.View(ctx, key.Stride(2)*c.curCellRange.min,
-		value.Dim(0), value.Stride(1),
-		value.Dim(1), value.Stride(2),
-		c.curMask.Dim(0),
-	)
+	if c.config.PermutedV {
+		vHeadDim := value.Dim(1)
+		elemSize := value.Stride(0)
+
+		value = value.View(ctx, elemSize*c.curCellRange.min,
+			cachedSize, value.Stride(1),
+			vHeadDim, value.Stride(2),
+			numKVHeads,
+		)
+	} else {
+		vHeadDim := value.Dim(0)
+		rowSize := value.Stride(2)
+
+		value = value.View(ctx, rowSize*c.curCellRange.min,
+			vHeadDim, value.Stride(1),
+			numKVHeads, value.Stride(2),
+			cachedSize,
+		)
+	}
 
 	return key, value, c.curMask
 }
 
 func (c *Causal) Put(ctx ml.Context, key, value ml.Tensor) {
-	if c.curBatchSize != key.Dim(2) {
-		panic(fmt.Errorf("inconsistent batch sizes (layer: %v, batch size: %v layer batch size: %v)", c.curLayer, c.curBatchSize, key.Dim(2)))
+	kHeadDim := key.Dim(0)
+	vHeadDim := value.Dim(0)
+	numKVHeads := key.Dim(1)
+	batchSize := key.Dim(2)
+
+	if c.curBatchSize != batchSize {
+		panic(fmt.Errorf("inconsistent batch sizes (layer: %v, batch size: %v layer batch size: %v)", c.curLayer, c.curBatchSize, batchSize))
 	}
 
-	if c.keys[c.curLayer] == nil || c.values[c.curLayer] == nil {
-		c.keys[c.curLayer] = c.cacheCtx.Zeros(c.DType, key.Dim(0), key.Dim(1), int(c.Capacity))
-		c.values[c.curLayer] = c.cacheCtx.Zeros(c.DType, value.Dim(0), value.Dim(1), int(c.Capacity))
+	if _, ok := c.ctxs[c.curLayer]; !ok {
+		c.ctxs[c.curLayer] = c.backend.NewContextSize(2).Layer(c.curLayer)
 	}
 
-	ctx.Forward(key.Copy(ctx, c.keys[c.curLayer].View(ctx, c.keys[c.curLayer].Stride(2)*c.curLoc, key.Dim(0)*key.Dim(1)*key.Dim(2))))
-	ctx.Forward(value.Copy(ctx, c.values[c.curLayer].View(ctx, c.values[c.curLayer].Stride(2)*c.curLoc, value.Dim(0)*value.Dim(1)*value.Dim(2))))
+	if _, ok := c.keys[c.curLayer]; !ok {
+		c.keys[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, kHeadDim, numKVHeads, len(c.cells))
+	}
+
+	if _, ok := c.values[c.curLayer]; !ok {
+		if c.config.PermutedV {
+			c.values[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, len(c.cells), vHeadDim, numKVHeads)
+		} else {
+			c.values[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, vHeadDim, numKVHeads, len(c.cells))
+		}
+	}
+
+	rowSize := c.keys[c.curLayer].Stride(2)
+	ctx.Forward(key.Copy(ctx, c.keys[c.curLayer].View(ctx, rowSize*c.curLoc, kHeadDim*numKVHeads*batchSize)))
+
+	if c.config.PermutedV {
+		elemSize := c.values[c.curLayer].Stride(0)
+
+		value = value.Permute(ctx, 1, 2, 0, 3)
+		ctx.Forward(value.Copy(ctx, c.values[c.curLayer].View(ctx, elemSize*c.curLoc, batchSize, len(c.cells)*elemSize, vHeadDim*numKVHeads)))
+	} else {
+		rowSize := c.values[c.curLayer].Stride(2)
+
+		ctx.Forward(value.Copy(ctx, c.values[c.curLayer].View(ctx, rowSize*c.curLoc, vHeadDim*numKVHeads*batchSize)))
+	}
 }
 
 func (c *Causal) CopyPrefix(srcSeq, dstSeq int, len int32) {
@@ -377,7 +601,7 @@ func (c *Causal) shift(seq int, beginIndex, offset int32) error {
 		}
 	}
 
-	kShift, err := ctx.FromIntSlice(offsets, len(offsets))
+	kShift, err := ctx.Input().FromIntSlice(offsets, len(offsets))
 	if err != nil {
 		return err
 	}
@@ -387,9 +611,13 @@ func (c *Causal) shift(seq int, beginIndex, offset int32) error {
 			continue
 		}
 
-		key = key.View(ctx, key.Stride(2)*seqRange.min,
-			key.Dim(0), key.Stride(1),
-			key.Dim(1), key.Stride(2),
+		kHeadDim := key.Dim(0)
+		numKVHeads := key.Dim(1)
+		rowSize := key.Stride(2)
+
+		key = key.View(ctx, rowSize*seqRange.min,
+			kHeadDim, key.Stride(1),
+			numKVHeads, key.Stride(2),
 			size,
 		)
 

kvcache/causal_test.go

@@ -6,6 +6,7 @@ import (
 	"testing"
 
 	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model/input"
 )
 
 type testCase struct {
@@ -24,7 +25,7 @@ func TestStore(t *testing.T) {
 	cache := NewCausalCache(nil)
 	defer cache.Close()
 
-	cache.Init(backend, ml.DTypeF16, 16)
+	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
 
 	tests := []testCase{
 		{
@@ -57,11 +58,11 @@ func TestSWA(t *testing.T) {
 	cache := NewSWACache(1, nil)
 	defer cache.Close()
 
-	cache.Init(backend, ml.DTypeF32, 16)
+	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
 
 	tests := []testCase{
 		{
-			name:          "SlidingWindow",
+			name:          "FirstBatch",
 			in:            []float32{1, 2, 3, 4},
 			inShape:       []int{1, 1, 4},
 			seqs:          []int{0, 0, 0, 0},
@@ -70,6 +71,16 @@ func TestSWA(t *testing.T) {
 			expectedShape: []int{1, 1, 4},
 			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
 		},
+		{
+			name:          "SecondBatch",
+			in:            []float32{5, 6},
+			inShape:       []int{1, 1, 2},
+			seqs:          []int{0, 0},
+			pos:           []int32{4, 5},
+			expected:      []float32{5, 6, 3, 4},
+			expectedShape: []int{1, 1, 4},
+			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1))},
+		},
 	}
 
 	testCache(t, backend, cache, tests)
@@ -80,7 +91,7 @@ func TestSequences(t *testing.T) {
 	cache := NewCausalCache(nil)
 	defer cache.Close()
 
-	cache.Init(backend, ml.DTypeF16, 16)
+	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
 
 	tests := []testCase{
 		{
@@ -115,7 +126,7 @@ func TestRemove(t *testing.T) {
 	})
 	defer cache.Close()
 
-	cache.Init(backend, ml.DTypeF16, 16)
+	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
 
 	tests := []testCase{
 		{
@@ -180,7 +191,7 @@ func TestDefrag(t *testing.T) {
 	})
 	defer cache.Close()
 
-	cache.Init(backend, ml.DTypeF16, 16)
+	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
 
 	tests := []testCase{
 		{
@@ -228,7 +239,7 @@ func TestCopy(t *testing.T) {
 	cache := NewCausalCache(func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) { return key, nil })
 	defer cache.Close()
 
-	cache.Init(backend, ml.DTypeF16, 16)
+	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
 
 	tests := []testCase{
 		{
@@ -269,7 +280,7 @@ func testCache(t *testing.T, backend ml.Backend, cache Cache, tests []testCase)
 			context := backend.NewContext()
 			defer context.Close()
 
-			err := cache.StartForward(context, test.pos, test.seqs)
+			err := cache.StartForward(context, input.Batch{Positions: test.pos, Sequences: test.seqs})
 			if err != nil {
 				panic(err)
 			}
@@ -280,9 +291,7 @@ func testCache(t *testing.T, backend ml.Backend, cache Cache, tests []testCase)
 
 			out, _, mask := cache.Get(context)
 
-			context.Forward(out)
-			context.Forward(mask)
-			context.Compute(out, mask)
+			context.Forward(out, mask).Compute(out, mask)
 
 			if !slices.Equal(out.Floats(), test.expected) || !slices.Equal(out.Shape(), test.expectedShape) || !slices.Equal(mask.Floats(), test.expectedMask) {
 				t.Errorf("TestCache: have %v (shape %v); want %v (shape %v); mask: have %v (shape %v) want %v", out.Floats(), out.Shape(), test.expected, test.expectedShape, mask.Floats(), mask.Shape(), test.expectedMask)
@@ -305,13 +314,17 @@ func (b *testBackend) NewContext() ml.Context {
 	return &testContext{}
 }
 
+func (b *testBackend) NewContextSize(int) ml.Context {
+	return &testContext{}
+}
+
 func (b *testBackend) SystemInfo() string {
 	return "not implemented"
 }
 
 type testContext struct{}
 
-func (c *testContext) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
+func (c *testContext) Empty(dtype ml.DType, shape ...int) ml.Tensor {
 	total := 0
 
 	if len(shape) > 0 {
@@ -324,8 +337,12 @@ func (c *testContext) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
 	return &testTensor{dtype: dtype, elementSize: 4, data: make([]float32, total), shape: shape}
 }
 
+func (c *testContext) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
+	return c.Empty(dtype, shape...)
+}
+
 func (c *testContext) FromFloatSlice(s []float32, shape ...int) (ml.Tensor, error) {
-	t := c.Zeros(ml.DTypeF32, shape...).(*testTensor)
+	t := c.Empty(ml.DTypeF32, shape...).(*testTensor)
 
 	copy(t.data, s)
 
@@ -344,11 +361,15 @@ func (c *testContext) FromIntSlice(s []int32, shape ...int) (ml.Tensor, error) {
 	return out, nil
 }
 
-func (c *testContext) Forward(ml.Tensor) {}
+func (c *testContext) Input() ml.Context    { return c }
+func (c *testContext) Output() ml.Context   { return c }
+func (c *testContext) Layer(int) ml.Context { return c }
+
+func (c *testContext) Forward(...ml.Tensor) ml.Context { return c }
 
 func (c *testContext) Compute(...ml.Tensor) {}
 
-func (c *testContext) MaxTensors() int {
+func (c *testContext) MaxGraphNodes() int {
 	return 10
 }
 
@@ -393,7 +414,7 @@ func (t *testTensor) Floats() []float32 {
 }
 
 func (t *testTensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
-	out := ctx.Zeros(t.DType(), t.Shape()...).(*testTensor)
+	out := ctx.Empty(t.DType(), t.Shape()...).(*testTensor)
 
 	for i := range out.data {
 		out.data[i] = t.data[i] + t2.(*testTensor).data[i]
@@ -430,11 +451,19 @@ func (t *testTensor) Scale(ctx ml.Context, s float64) ml.Tensor {
 	panic("not implemented")
 }
 
+func (t *testTensor) AvgPool1D(ctx ml.Context, k, s, p int) ml.Tensor {
+	panic("not implemented")
+}
+
+func (t *testTensor) AvgPool2D(ctx ml.Context, k, s int, p float32) ml.Tensor {
+	panic("not implemented")
+}
+
 func (t *testTensor) Conv2D(ctx ml.Context, weight ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
 	panic("not implemented")
 }
 
-func (t *testTensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, dim uint32, base, scale float32) ml.Tensor {
+func (t *testTensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, dim, ropeType uint32, base, scale float32) ml.Tensor {
 	panic("not implemented")
 }
 
@@ -470,7 +499,7 @@ func (t *testTensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
 
 	context := &testContext{}
 
-	view := context.Zeros(t.dtype, s...).(*testTensor)
+	view := context.Empty(t.dtype, s...).(*testTensor)
 	view.data = t.data[offset : offset+len(view.data)]
 
 	return view
@@ -484,6 +513,10 @@ func (t *testTensor) Contiguous(ctx ml.Context) ml.Tensor {
 	panic("not implemented")
 }
 
+func (t *testTensor) Set(ctx ml.Context, t2 ml.Tensor, offset int, strides ...int) ml.Tensor {
+	panic("not implemented")
+}
+
 func (t *testTensor) Pad(ctx ml.Context, shape ...int) ml.Tensor {
 	panic("not implemented")
 }

kvcache/encoder.go

@@ -1,7 +1,10 @@
 package kvcache
 
 import (
+	"fmt"
+
 	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model/input"
 )
 
 // Encoder cache stores K and V tensors that are position independent
@@ -11,6 +14,9 @@ import (
 //
 // Not currently safe for multiple sequences
 type EncoderCache struct {
+	// config controls mostly backend-specific optimizations
+	config *ml.CacheConfig
+
 	// ** current forward pass **
 
 	// the active layer for Get and Put
@@ -30,36 +36,63 @@ type EncoderCache struct {
 	encoderPos int32
 
 	// ** cache data storage **
-
-	cacheCtx     ml.Context
-	keys, values []ml.Tensor
+	backend      ml.Backend
+	ctxs         map[int]ml.Context
+	keys, values map[int]ml.Tensor
 }
 
 func NewEncoderCache() *EncoderCache {
-	return &EncoderCache{}
+	return &EncoderCache{
+		ctxs:   make(map[int]ml.Context),
+		keys:   make(map[int]ml.Tensor),
+		values: make(map[int]ml.Tensor),
+	}
 }
 
-func (c *EncoderCache) Init(backend ml.Backend, dtype ml.DType, capacity int32) {
-	c.cacheCtx = backend.NewContext()
+func (c *EncoderCache) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
+	if c.config == nil {
+		var config ml.CacheConfig
+		if cc, ok := backend.(ml.BackendCacheConfig); ok {
+			config = cc.CacheConfig()
+		}
+		c.config = &config
+	}
+
+	if maxSequences > 1 {
+		panic(fmt.Errorf("encoder cache does not support multiple sequences; requested: %v", maxSequences))
+	}
+
+	if c.config.CachePadding != 0 && c.config.CachePadding != 1 {
+		panic(fmt.Errorf("encoder cache is unable to enforce requested CachePadding (%v)", c.config.CachePadding))
+	}
+
+	c.backend = backend
+}
+
+func (c *EncoderCache) SetConfig(config ml.CacheConfig) {
+	if c.config != nil {
+		panic("config cannot be changed after being previously set, either by the model or backend")
+	}
+
+	c.config = &config
 }
 
 func (c *EncoderCache) Close() {
-	c.cacheCtx.Close()
+	for _, ctx := range c.ctxs {
+		ctx.Close()
+	}
 }
 
-func (c *EncoderCache) StartForward(ctx ml.Context, positions []int32, seqs []int) error {
-	// The image is always in the first position
-	c.curPos = positions[0]
+func (c *EncoderCache) StartForward(ctx ml.Context, batch input.Batch) error {
+	// We work with the most recent image
+	if len(batch.Multimodal) > 0 {
+		c.curPos = batch.Positions[batch.Multimodal[len(batch.Multimodal)-1].Index]
+	}
 
 	return nil
 }
 
 func (c *EncoderCache) SetLayer(layer int) {
-	if layer >= len(c.keys) {
-		c.keys = append(c.keys, make([]ml.Tensor, layer-len(c.keys)+1)...)
-		c.values = append(c.values, make([]ml.Tensor, layer-len(c.values)+1)...)
-	}
-
 	c.curLayer = layer
 }
 
@@ -75,13 +108,26 @@ func (c *EncoderCache) Put(ctx ml.Context, key, value ml.Tensor) {
 	c.encoderPos = c.curPos
 	c.encoderCached = true
 
-	if c.keys[c.curLayer] == nil || c.values[c.curLayer] == nil {
-		c.keys[c.curLayer] = c.cacheCtx.Zeros(key.DType(), key.Shape()...)
-		c.values[c.curLayer] = c.cacheCtx.Zeros(value.DType(), value.Shape()...)
+	if c.config.PermutedV {
+		value = value.Permute(ctx, 1, 2, 0, 3)
 	}
 
-	ctx.Forward(key.Copy(ctx, c.keys[c.curLayer]))
-	ctx.Forward(value.Copy(ctx, c.values[c.curLayer]))
+	if _, ok := c.ctxs[c.curLayer]; !ok {
+		c.ctxs[c.curLayer] = c.backend.NewContextSize(2).Layer(c.curLayer)
+	}
+
+	if _, ok := c.keys[c.curLayer]; !ok {
+		c.keys[c.curLayer] = c.ctxs[c.curLayer].Empty(key.DType(), key.Shape()...)
+	}
+
+	if _, ok := c.values[c.curLayer]; !ok {
+		c.values[c.curLayer] = c.ctxs[c.curLayer].Empty(value.DType(), value.Shape()...)
+	}
+
+	ctx.Forward(
+		key.Copy(ctx, c.keys[c.curLayer]),
+		value.Copy(ctx, c.values[c.curLayer]),
+	)
 }
 
 func (c *EncoderCache) CopyPrefix(srcSeq, dstSeq int, len int32) {

kvcache/wrapper.go

@@ -4,6 +4,7 @@ import (
 	"math"
 
 	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model/input"
 )
 
 // Wrapper cache is a container for multiple types of caches,
@@ -22,9 +23,15 @@ func NewWrapperCache(caches ...Cache) *WrapperCache {
 	}
 }
 
-func (c *WrapperCache) Init(backend ml.Backend, dtype ml.DType, capacity int32) {
+func (c *WrapperCache) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
 	for _, cache := range c.caches {
-		cache.Init(backend, dtype, capacity)
+		cache.Init(backend, dtype, maxSequences, capacity, maxBatch)
+	}
+}
+
+func (c *WrapperCache) SetConfig(config ml.CacheConfig) {
+	for _, cache := range c.caches {
+		cache.SetConfig(config)
 	}
 }
 
@@ -34,14 +41,14 @@ func (c *WrapperCache) Close() {
 	}
 }
 
-func (c *WrapperCache) StartForward(ctx ml.Context, positions []int32, seqs []int) error {
+func (c *WrapperCache) StartForward(ctx ml.Context, batch input.Batch) error {
 	for i, cache := range c.caches {
-		err := cache.StartForward(ctx, positions, seqs)
+		err := cache.StartForward(ctx, batch)
 		if err != nil {
 			// unwind on error - Remove with endIndex set to math.MaxInt32 does not fail
 			for j := i - 1; j >= 0; j-- {
-				for k := range positions {
-					_ = c.caches[j].Remove(seqs[k], positions[k], math.MaxInt32)
+				for k := range batch.Positions {
+					_ = c.caches[j].Remove(batch.Sequences[k], batch.Positions[k], math.MaxInt32)
 				}
 			}
 			return err

llama/llama.cpp/include/llama.h

@@ -105,6 +105,7 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_CHAMELEON      = 26,
         LLAMA_VOCAB_PRE_TYPE_MINERVA        = 27,
         LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM  = 28,
+        LLAMA_VOCAB_PRE_TYPE_GPT4O          = 29,
     };
 
     enum llama_rope_type {

llama/llama.cpp/src/llama-arch.cpp

@@ -37,6 +37,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_MINICPM3,         "minicpm3"         },
     { LLM_ARCH_GEMMA,            "gemma"            },
     { LLM_ARCH_GEMMA2,           "gemma2"           },
+    { LLM_ARCH_GEMMA3,           "gemma3"           },
     { LLM_ARCH_STARCODER2,       "starcoder2"       },
     { LLM_ARCH_MAMBA,            "mamba"            },
     { LLM_ARCH_XVERSE,           "xverse"           },
@@ -804,6 +805,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
         },
     },
+    {
+        LLM_ARCH_GEMMA3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
+        },
+    },
     {
         LLM_ARCH_STARCODER2,
         {

llama/llama.cpp/src/llama-arch.h

@@ -41,6 +41,7 @@ enum llm_arch {
     LLM_ARCH_MINICPM3,
     LLM_ARCH_GEMMA,
     LLM_ARCH_GEMMA2,
+    LLM_ARCH_GEMMA3,
     LLM_ARCH_STARCODER2,
     LLM_ARCH_MAMBA,
     LLM_ARCH_XVERSE,

llama/llama.cpp/src/llama-model.cpp

@@ -878,6 +878,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                }
             } break;
+        case LLM_ARCH_GEMMA3:
+            {
+            } break;
         case LLM_ARCH_STARCODER2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -2283,7 +2286,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
 
                     // output
                     output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, 0);
+                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
 
                     for (int i = 0; i < n_layer; ++i) {
                         auto & layer = layers[i];
@@ -2298,8 +2305,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
                         layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0);
 
-                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
-                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
                     }
                 } break;
             case LLM_ARCH_PHIMOE:
@@ -2533,6 +2540,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
                     }
                 } break;
+            case LLM_ARCH_GEMMA3:
+                {
+                } break;
             case LLM_ARCH_STARCODER2:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -4025,6 +4035,7 @@ enum llama_rope_type llama_model_rope_type(const struct llama_model * model) {
         case LLM_ARCH_PHIMOE:
         case LLM_ARCH_GEMMA:
         case LLM_ARCH_GEMMA2:
+        case LLM_ARCH_GEMMA3:
         case LLM_ARCH_STARCODER2:
         case LLM_ARCH_OPENELM:
         case LLM_ARCH_GPTNEOX:

llama/llama.cpp/src/llama-quant.cpp

@@ -737,6 +737,15 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         // This used to be a regex, but <regex> has an extreme cost to compile times.
         bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
 
+        // don't quantize vision stuff
+        quantize &= name.find("v.blk.") == std::string::npos;
+
+        quantize &= name.find("mm.mm_input_projection.weight") == std::string::npos;
+        quantize &= name.find("mm.mm_soft_emb_norm.weight") == std::string::npos;
+        quantize &= name.find("v.patch_embedding.weight") == std::string::npos;
+        quantize &= name.find("v.position_embedding.weight") == std::string::npos;
+        quantize &= name.find("v.post_layernorm.weight") == std::string::npos;
+
         // quantize only 2D and 3D tensors (experts)
         quantize &= (ggml_n_dims(tensor) >= 2);
 

llama/llama.cpp/src/llama-vocab.cpp

@@ -392,6 +392,13 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_GPT4O:
+                // original regex from tokenizer.json
+                // [^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+
+                regex_exprs = {
+                    "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+                };
+                break;
             default:
                 // default regex for BPE tokenization pre-processing
                 regex_exprs = {
@@ -1436,7 +1443,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
 
             const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
             if (precompiled_charsmap_keyidx != -1) {
-                size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
+                size_t n_precompiled_charsmap = gguf_get_arr_data_n(ctx, precompiled_charsmap_keyidx);
                 const char * pc = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
                 precompiled_charsmap.assign(pc, pc + n_precompiled_charsmap);
 #ifdef IS_BIG_ENDIAN
@@ -1583,6 +1590,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             } else if (
                 tokenizer_pre == "megrez") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
+            } else if (
+                tokenizer_pre == "gpt-4o") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_GPT4O;
+                clean_spaces = false;
             } else {
                 LLAMA_LOG_WARN("%s: missing or unrecognized pre-tokenizer type, using: 'default'\n", __func__);
                 pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;

llama/llama.go

@@ -21,77 +21,45 @@ package llama
 
 extern bool llamaProgressCallback(float progress, void *user_data);
 extern void llamaLog(int level, char* text, void* user_data);
-
-typedef enum {COMP_UNKNOWN,COMP_GCC,COMP_CLANG} COMPILER;
-COMPILER inline get_compiler() {
-#if defined(__clang__)
-	return COMP_CLANG;
-#elif defined(__GNUC__)
-	return COMP_GCC;
-#else
-	return UNKNOWN_COMPILER;
-#endif
-}
-
 */
 import "C"
 
 import (
+	"context"
 	_ "embed"
 	"errors"
 	"fmt"
+	"log/slog"
 	"os"
 	"runtime"
 	"runtime/cgo"
 	"slices"
 	"strings"
-	"sync/atomic"
 	"unsafe"
 
 	_ "github.com/ollama/ollama/llama/llama.cpp/common"
 	_ "github.com/ollama/ollama/llama/llama.cpp/examples/llava"
 	_ "github.com/ollama/ollama/llama/llama.cpp/src"
-	"github.com/ollama/ollama/ml/backend/ggml/ggml/src"
+	ggml "github.com/ollama/ollama/ml/backend/ggml/ggml/src"
 )
 
+func init() {
+	C.llama_log_set(C.ggml_log_callback(C.llamaLog), nil)
+}
+
+//export llamaLog
+func llamaLog(level C.int, text *C.char, _ unsafe.Pointer) {
+	// slog levels zeros INFO and are multiples of 4
+	if slog.Default().Enabled(context.TODO(), slog.Level(int(level-C.GGML_LOG_LEVEL_INFO)*4)) {
+		fmt.Fprint(os.Stderr, C.GoString(text))
+	}
+}
+
 func BackendInit() {
 	ggml.OnceLoad()
 	C.llama_backend_init()
 }
 
-func PrintSystemInfo() string {
-	var compiler string
-	switch C.get_compiler() {
-	case C.COMP_UNKNOWN:
-		compiler = "cgo(unknown_compiler)"
-	case C.COMP_GCC:
-		compiler = "cgo(gcc)"
-	case C.COMP_CLANG:
-		compiler = "cgo(clang)"
-	}
-	return C.GoString(C.llama_print_system_info()) + compiler
-}
-
-var logLevel atomic.Int32
-
-func init() {
-	logLevel.Store(int32(C.GGML_LOG_LEVEL_INFO))
-	C.llama_log_set((C.ggml_log_callback)(C.llamaLog), nil)
-}
-
-func EnableDebug() {
-	logLevel.Store(int32(C.GGML_LOG_LEVEL_DEBUG))
-}
-
-//export llamaLog
-func llamaLog(level int32, text *C.char, _ unsafe.Pointer) {
-	if level < logLevel.Load() {
-		return
-	}
-
-	fmt.Fprint(os.Stderr, C.GoString(text))
-}
-
 func GetModelArch(modelPath string) (string, error) {
 	mp := C.CString(modelPath)
 	defer C.free(unsafe.Pointer(mp))
@@ -269,7 +237,7 @@ func LoadModelFromFile(modelPath string, params ModelParams) (*Model, error) {
 		cparams.progress_callback_user_data = unsafe.Pointer(&handle)
 	}
 
-	m := Model{c: C.llama_load_model_from_file(C.CString(modelPath), cparams)}
+	m := Model{c: C.llama_model_load_from_file(C.CString(modelPath), cparams)}
 	if m.c == nil {
 		return nil, fmt.Errorf("unable to load model: %s", modelPath)
 	}
@@ -277,13 +245,27 @@ func LoadModelFromFile(modelPath string, params ModelParams) (*Model, error) {
 	return &m, nil
 }
 
+func LoadVocabFromFile(path string) (*Vocab, error) {
+	mp := C.CString(path)
+	defer C.free(unsafe.Pointer(mp))
+	v := Vocab{c: C.llama_load_vocab_from_file(mp)}
+	if v.c == nil {
+		return nil, fmt.Errorf("unable to load vocab: %s", path)
+	}
+	return &v, nil
+}
+
+func FreeVocab(vocab *Vocab) {
+	C.llama_free_vocab(vocab.c)
+}
+
 func FreeModel(model *Model) {
-	C.llama_free_model(model.c)
+	C.llama_model_free(model.c)
 }
 
 func NewContextWithModel(model *Model, params ContextParams) (*Context, error) {
 	c := Context{
-		c:          C.llama_new_context_with_model(model.c, params.c),
+		c:          C.llama_init_from_model(model.c, params.c),
 		numThreads: int(params.c.n_threads),
 	}
 	if c.c == nil {
@@ -294,15 +276,15 @@ func NewContextWithModel(model *Model, params ContextParams) (*Context, error) {
 }
 
 func (m *Model) NumVocab() int {
-	return int(C.llama_n_vocab(m.Vocab()))
+	return int(C.llama_vocab_n_tokens(m.Vocab()))
 }
 
 func (m *Model) TokenIsEog(token int) bool {
-	return bool(C.llama_token_is_eog(m.Vocab(), C.llama_token(token)))
+	return bool(C.llama_vocab_is_eog(m.Vocab(), C.llama_token(token)))
 }
 
 func (m *Model) AddBOSToken() bool {
-	return bool(C.llama_add_bos_token(m.Vocab()))
+	return bool(C.llama_vocab_get_add_bos(m.Vocab()))
 }
 
 func (m *Model) ApplyLoraFromFile(context *Context, loraPath string, scale float32, threads int) error {
@@ -325,6 +307,10 @@ func (m *Model) ApplyLoraFromFile(context *Context, loraPath string, scale float
 	return nil
 }
 
+type Vocab struct {
+	c *C.struct_llama_vocab
+}
+
 func (m *Model) Vocab() *C.struct_llama_vocab {
 	return C.llama_model_get_vocab(m.c)
 }
@@ -485,7 +471,7 @@ func (m *Model) Tokenize(text string, addSpecial bool, parseSpecial bool) ([]int
 }
 
 func (m *Model) NEmbd() int {
-	return int(C.llama_n_embd(m.c))
+	return int(C.llama_model_n_embd(m.c))
 }
 
 func Quantize(infile, outfile string, ftype uint32) error {
@@ -701,3 +687,53 @@ func SchemaToGrammar(schema []byte) []byte {
 	}
 	return buf[:n]
 }
+
+type Sampler struct {
+	c *C.struct_llama_sampler
+}
+
+func NewGrammarSampler(vocab *Vocab, grammar string) *Sampler {
+	cGrammar := C.CString(grammar)
+	cRoot := C.CString("root")
+	defer C.free(unsafe.Pointer(cGrammar))
+	defer C.free(unsafe.Pointer(cRoot))
+
+	sampler := &Sampler{c: C.llama_sampler_init_grammar(vocab.c, cGrammar, cRoot)}
+
+	return sampler
+}
+
+func (s *Sampler) Accept(token int32) {
+	C.llama_sampler_accept(s.c, C.llama_token(token))
+}
+
+type TokenData struct {
+	Id    int32
+	Logit float32
+}
+
+func (s *Sampler) Apply(tokens []TokenData) {
+	tds := make([]C.struct_llama_token_data, len(tokens))
+	for i, token := range tokens {
+		tds[i] = C.struct_llama_token_data{
+			id:    C.int32_t(token.Id),
+			logit: C.float(token.Logit),
+			p:     C.float(0.0),
+		}
+	}
+	tda := &C.llama_token_data_array{
+		data:     (*C.struct_llama_token_data)(unsafe.Pointer(&tds[0])),
+		size:     C.size_t(len(tokens)),
+		selected: C.int64_t(-1),
+		sorted:   C.bool(false),
+	}
+
+	var pinner runtime.Pinner
+	pinner.Pin(&tds[0])
+	defer pinner.Unpin()
+
+	C.llama_sampler_apply(s.c, tda)
+	for i := range tokens {
+		tokens[i].Logit = float32(tds[i].logit)
+	}
+}

llama/patches/0015-try-catch-backend-load.patch

@@ -1,69 +0,0 @@
-From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
-From: Michael Yang <mxyng@pm.me>
-Date: Tue, 11 Feb 2025 14:06:36 -0800
-Subject: [PATCH] try/catch backend load
-
----
- ggml/src/ggml-backend-reg.cpp | 45 ++++++++++++++++++-----------------
- 1 file changed, 23 insertions(+), 22 deletions(-)
-
-diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
-index 98d5e14d..1c19129a 100644
---- a/ggml/src/ggml-backend-reg.cpp
-+++ b/ggml/src/ggml-backend-reg.cpp
-@@ -512,32 +512,33 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
-         }
-         fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
-         for (const auto & entry : dir_it) {
--            if (entry.is_regular_file()) {
--                std::wstring filename = entry.path().filename().wstring();
--                std::wstring ext = entry.path().extension().wstring();
--                if (filename.find(file_prefix) == 0 && ext == backend_filename_suffix()) {
--                    dl_handle_ptr handle { dl_load_library(entry.path().wstring()) };
--                    if (!handle && !silent) {
--                        GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
--                    }
--                    if (handle) {
-+            try {
-+                if (entry.is_regular_file()) {
-+                    std::wstring filename = entry.path().filename().wstring();
-+                    std::wstring ext = entry.path().extension().wstring();
-+                    if (filename.find(file_prefix) == 0 && ext == backend_filename_suffix()) {
-+                        dl_handle_ptr handle { dl_load_library(entry.path().wstring()) };
-+                        if (!handle) {
-+                            GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
-+                            continue;
-+                        }
-+
-                         auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
--                        if (score_fn) {
--                            int s = score_fn();
--#ifndef NDEBUG
--                            GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str(), s);
--#endif
--                            if (s > best_score) {
--                                best_score = s;
--                                best_path = entry.path().wstring();
--                            }
--                        } else {
--                            if (!silent) {
--                                GGML_LOG_INFO("%s: failed to find ggml_backend_score in %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
--                            }
-+                        if (!score_fn) {
-+                            GGML_LOG_DEBUG("%s: failed to find ggml_backend_score in %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
-+                            continue;
-+                        }
-+
-+                        int s = score_fn();
-+                        GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str(), s);
-+                        if (s > best_score) {
-+                            best_score = s;
-+                            best_path = entry.path().wstring();
-                         }
-                     }
-                 }
-+            } catch (const std::exception & e) {
-+                GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str(), e.what());
-             }
-         }
-     }

llama/patches/0015-use-std-filesystem-path-instead-of-wstring.patch

@@ -4,11 +4,11 @@ Date: Sun, 16 Feb 2025 20:00:22 -0500
 Subject: [PATCH] use std::filesystem::path instead of wstring
 
 ---
- ggml/src/ggml-backend-reg.cpp | 144 ++++++++++++++--------------------
- 1 file changed, 58 insertions(+), 86 deletions(-)
+ ggml/src/ggml-backend-reg.cpp | 199 +++++++++++++++-------------------
+ 1 file changed, 88 insertions(+), 111 deletions(-)
 
 diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
-index 1c19129a..c854e6bb 100644
+index 98d5e14d..799af5f3 100644
 --- a/ggml/src/ggml-backend-reg.cpp
 +++ b/ggml/src/ggml-backend-reg.cpp
 @@ -66,26 +66,6 @@
@@ -264,47 +264,55 @@ index 1c19129a..c854e6bb 100644
      for (const auto & search_path : search_paths) {
          if (!fs::exists(search_path)) {
              continue;
-@@ -514,31 +486,31 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
+@@ -513,29 +485,26 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
+         fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
          for (const auto & entry : dir_it) {
-             try {
-                 if (entry.is_regular_file()) {
--                    std::wstring filename = entry.path().filename().wstring();
--                    std::wstring ext = entry.path().extension().wstring();
-+                    std::string filename = entry.path().filename().string();
-+                    std::string ext = entry.path().extension().string();
-                     if (filename.find(file_prefix) == 0 && ext == backend_filename_suffix()) {
--                        dl_handle_ptr handle { dl_load_library(entry.path().wstring()) };
-+                        dl_handle_ptr handle { dl_load_library(entry.path()) };
-                         if (!handle) {
--                            GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
-+                            GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path_to_string(entry.path()).c_str());
-                             continue;
-                         }
- 
-                         auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
-                         if (!score_fn) {
--                            GGML_LOG_DEBUG("%s: failed to find ggml_backend_score in %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
-+                            GGML_LOG_DEBUG("%s: failed to find ggml_backend_score in %s\n", __func__, path_to_string(entry.path()).c_str());
-                             continue;
-                         }
- 
-                         int s = score_fn();
--                        GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str(), s);
-+                        GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, path_to_string(entry.path()).c_str(), s);
-                         if (s > best_score) {
-                             best_score = s;
--                            best_path = entry.path().wstring();
-+                            best_path = entry.path();
-                         }
+             if (entry.is_regular_file()) {
+-                std::wstring filename = entry.path().filename().wstring();
+-                std::wstring ext = entry.path().extension().wstring();
++                std::string filename = entry.path().filename().string();
++                std::string ext = entry.path().extension().string();
+                 if (filename.find(file_prefix) == 0 && ext == backend_filename_suffix()) {
+-                    dl_handle_ptr handle { dl_load_library(entry.path().wstring()) };
+-                    if (!handle && !silent) {
+-                        GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
++                    dl_handle_ptr handle { dl_load_library(entry.path()) };
++                    if (!handle) {
++                        GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path_to_string(entry.path()).c_str());
++                        continue;
+                     }
+-                    if (handle) {
+-                        auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
+-                        if (score_fn) {
+-                            int s = score_fn();
+-#ifndef NDEBUG
+-                            GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str(), s);
+-#endif
+-                            if (s > best_score) {
+-                                best_score = s;
+-                                best_path = entry.path().wstring();
+-                            }
+-                        } else {
+-                            if (!silent) {
+-                                GGML_LOG_INFO("%s: failed to find ggml_backend_score in %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
+-                            }
+-                        }
++
++                    auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
++                    if (!score_fn) {
++                        GGML_LOG_DEBUG("%s: failed to find ggml_backend_score in %s\n", __func__, path_to_string(entry.path()).c_str());
++                        continue;
++                    }
++
++                    int s = score_fn();
++                    GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, path_to_string(entry.path()).c_str(), s);
++                    if (s > best_score) {
++                        best_score = s;
++                        best_path = entry.path();
                      }
                  }
-             } catch (const std::exception & e) {
--                GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str(), e.what());
-+                GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, path_to_string(entry.path()).c_str(), e.what());
              }
-         }
-     }
-@@ -546,7 +518,7 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
+@@ -545,7 +514,7 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
      if (best_score == 0) {
          // try to load the base backend
          for (const auto & search_path : search_paths) {
@@ -313,3 +321,49 @@ index 1c19129a..c854e6bb 100644
              if (fs::exists(path)) {
                  return get_reg().load_backend(path, silent);
              }
+@@ -560,6 +529,14 @@ void ggml_backend_load_all() {
+     ggml_backend_load_all_from_path(nullptr);
+ }
+ 
++static void ggml_backend_try_load_best(const char * name, bool silent, const char * user_search_path) {
++    try {
++        ggml_backend_load_best(name, silent, user_search_path);
++    } catch (const std::exception & e) {
++        GGML_LOG_DEBUG("%s: failed to load %s: %s\n", __func__, name, e.what());
++    }
++}
++
+ void ggml_backend_load_all_from_path(const char * dir_path) {
+ #ifdef NDEBUG
+     bool silent = true;
+@@ -567,18 +544,18 @@ void ggml_backend_load_all_from_path(const char * dir_path) {
+     bool silent = false;
+ #endif
+ 
+-    ggml_backend_load_best("blas", silent, dir_path);
+-    ggml_backend_load_best("cann", silent, dir_path);
+-    ggml_backend_load_best("cuda", silent, dir_path);
+-    ggml_backend_load_best("hip", silent, dir_path);
+-    ggml_backend_load_best("kompute", silent, dir_path);
+-    ggml_backend_load_best("metal", silent, dir_path);
+-    ggml_backend_load_best("rpc", silent, dir_path);
+-    ggml_backend_load_best("sycl", silent, dir_path);
+-    ggml_backend_load_best("vulkan", silent, dir_path);
+-    ggml_backend_load_best("opencl", silent, dir_path);
+-    ggml_backend_load_best("musa", silent, dir_path);
+-    ggml_backend_load_best("cpu", silent, dir_path);
++    ggml_backend_try_load_best("blas", silent, dir_path);
++    ggml_backend_try_load_best("cann", silent, dir_path);
++    ggml_backend_try_load_best("cuda", silent, dir_path);
++    ggml_backend_try_load_best("hip", silent, dir_path);
++    ggml_backend_try_load_best("kompute", silent, dir_path);
++    ggml_backend_try_load_best("metal", silent, dir_path);
++    ggml_backend_try_load_best("rpc", silent, dir_path);
++    ggml_backend_try_load_best("sycl", silent, dir_path);
++    ggml_backend_try_load_best("vulkan", silent, dir_path);
++    ggml_backend_try_load_best("opencl", silent, dir_path);
++    ggml_backend_try_load_best("musa", silent, dir_path);
++    ggml_backend_try_load_best("cpu", silent, dir_path);
+     // check the environment variable GGML_BACKEND_PATH to load an out-of-tree backend
+     const char * backend_path = std::getenv("GGML_BACKEND_PATH");
+     if (backend_path) {

llama/patches/0018-add-phi4-support.patch

@@ -0,0 +1,80 @@
+From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
+From: jmorganca <jmorganca@gmail.com>
+Date: Thu, 27 Feb 2025 15:12:26 -0800
+Subject: [PATCH] add phi4 support
+
+---
+ include/llama.h     |  1 +
+ src/llama-model.cpp | 10 +++++++---
+ src/llama-vocab.cpp | 11 +++++++++++
+ 3 files changed, 19 insertions(+), 3 deletions(-)
+
+diff --git a/include/llama.h b/include/llama.h
+index cc948005..16774711 100644
+--- a/include/llama.h
++++ b/include/llama.h
+@@ -105,6 +105,7 @@ extern "C" {
+         LLAMA_VOCAB_PRE_TYPE_CHAMELEON      = 26,
+         LLAMA_VOCAB_PRE_TYPE_MINERVA        = 27,
+         LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM  = 28,
++        LLAMA_VOCAB_PRE_TYPE_GPT4O          = 29,
+     };
+ 
+     enum llama_rope_type {
+diff --git a/src/llama-model.cpp b/src/llama-model.cpp
+index 21819080..ab1a07d1 100644
+--- a/src/llama-model.cpp
++++ b/src/llama-model.cpp
+@@ -2283,7 +2283,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
+ 
+                     // output
+                     output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+-                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, 0);
++                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
++                    // if output is NULL, init from the input tok embed
++                    if (output == NULL) {
++                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
++                    }
+ 
+                     for (int i = 0; i < n_layer; ++i) {
+                         auto & layer = layers[i];
+@@ -2298,8 +2302,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
+                         layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
+                         layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0);
+ 
+-                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+-                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
++                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
++                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                     }
+                 } break;
+             case LLM_ARCH_PHIMOE:
+diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
+index 1ca827eb..c7ff28be 100644
+--- a/src/llama-vocab.cpp
++++ b/src/llama-vocab.cpp
+@@ -392,6 +392,13 @@ struct llm_tokenizer_bpe : llm_tokenizer {
+                     "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                 };
+                 break;
++            case LLAMA_VOCAB_PRE_TYPE_GPT4O:
++                // original regex from tokenizer.json
++                // [^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+
++                regex_exprs = {
++                    "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
++                };
++                break;
+             default:
+                 // default regex for BPE tokenization pre-processing
+                 regex_exprs = {
+@@ -1583,6 +1590,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
+             } else if (
+                 tokenizer_pre == "megrez") {
+                 pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
++            } else if (
++                tokenizer_pre == "gpt-4o") {
++                pre_type = LLAMA_VOCAB_PRE_TYPE_GPT4O;
++                clean_spaces = false;
+             } else {
+                 LLAMA_LOG_WARN("%s: missing or unrecognized pre-tokenizer type, using: 'default'\n", __func__);
+                 pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;

llama/patches/0019-fix-string-arr-kv-loading.patch

@@ -0,0 +1,64 @@
+From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
+From: jmorganca <jmorganca@gmail.com>
+Date: Wed, 5 Mar 2025 17:41:07 -0800
+Subject: [PATCH] fix string arr kv loading
+
+---
+ ggml/include/gguf.h | 1 +
+ ggml/src/gguf.cpp   | 7 +++++--
+ src/llama-vocab.cpp | 2 +-
+ 3 files changed, 7 insertions(+), 3 deletions(-)
+
+diff --git a/ggml/include/gguf.h b/ggml/include/gguf.h
+index 79ee2020..3efb22f0 100644
+--- a/ggml/include/gguf.h
++++ b/ggml/include/gguf.h
+@@ -114,6 +114,7 @@ extern "C" {
+     // get raw pointer to the first element of the array with the given key_id
+     // for bool arrays, note that they are always stored as int8 on all platforms (usually this makes no difference)
+     GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int64_t key_id);
++    GGML_API size_t       gguf_get_arr_data_n(const struct gguf_context * ctx, int64_t key_id);
+ 
+     // get ith C string from array with given key_id
+     GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int64_t key_id, size_t i);
+diff --git a/ggml/src/gguf.cpp b/ggml/src/gguf.cpp
+index ab13669c..f75b923f 100644
+--- a/ggml/src/gguf.cpp
++++ b/ggml/src/gguf.cpp
+@@ -777,10 +777,14 @@ enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int64_t key_id
+ 
+ const void * gguf_get_arr_data(const struct gguf_context * ctx, int64_t key_id) {
+     GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+-    GGML_ASSERT(ctx->kv[key_id].get_type() != GGUF_TYPE_STRING);
+     return ctx->kv[key_id].data.data();
+ }
+ 
++size_t gguf_get_arr_data_n(const struct gguf_context * ctx, int64_t key_id) {
++    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
++    return ctx->kv[key_id].data.size();
++}
++
+ const char * gguf_get_arr_str(const struct gguf_context * ctx, int64_t key_id, size_t i) {
+     GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+     GGML_ASSERT(ctx->kv[key_id].get_type() == GGUF_TYPE_STRING);
+@@ -874,7 +878,6 @@ const char * gguf_get_val_str(const struct gguf_context * ctx, int64_t key_id) {
+ const void * gguf_get_val_data(const struct gguf_context * ctx, int64_t key_id) {
+     GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+     GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
+-    GGML_ASSERT(ctx->kv[key_id].get_type() != GGUF_TYPE_STRING);
+     return ctx->kv[key_id].data.data();
+ }
+ 
+diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
+index c7ff28be..7a185443 100644
+--- a/src/llama-vocab.cpp
++++ b/src/llama-vocab.cpp
+@@ -1443,7 +1443,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
+ 
+             const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
+             if (precompiled_charsmap_keyidx != -1) {
+-                size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
++                size_t n_precompiled_charsmap = gguf_get_arr_data_n(ctx, precompiled_charsmap_keyidx);
+                 const char * pc = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
+                 precompiled_charsmap.assign(pc, pc + n_precompiled_charsmap);
+ #ifdef IS_BIG_ENDIAN

llama/patches/0020-ollama-debug-tensor.patch

@@ -0,0 +1,33 @@
+From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
+From: Michael Yang <mxyng@pm.me>
+Date: Sun, 9 Mar 2025 14:44:16 -0700
+Subject: [PATCH] ollama debug tensor
+
+---
+ ggml/src/ggml-cpu/ggml-cpu.c | 6 ++++++
+ 1 file changed, 6 insertions(+)
+
+diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
+index 2f606d82..ec60e8fc 100644
+--- a/ggml/src/ggml-cpu/ggml-cpu.c
++++ b/ggml/src/ggml-cpu/ggml-cpu.c
+@@ -11,6 +11,8 @@
+ #include "ggml-threading.h"
+ #include "ggml.h"
+ 
++#include "ollama-debug.h"
++
+ #if defined(_MSC_VER) || defined(__MINGW32__)
+ #include <malloc.h> // using malloc.h with MSC/MINGW
+ #elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
+@@ -14103,6 +14105,10 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
+ 
+         ggml_compute_forward(&params, node);
+ 
++#ifdef OLLAMA_DEBUG
++        ollama_debug(node, true);
++#endif
++
+         if (state->ith == 0 && cplan->abort_callback &&
+                 cplan->abort_callback(cplan->abort_callback_data)) {
+             atomic_store_explicit(&tp->abort, node_n + 1, memory_order_relaxed);

llama/patches/0021-gemma3-quantization.patch

@@ -0,0 +1,113 @@
+From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
+From: Patrick Devine <patrick@infrahq.com>
+Date: Fri, 14 Mar 2025 16:33:23 -0700
+Subject: [PATCH] gemma3 quantization
+
+---
+ src/llama-arch.cpp  | 19 +++++++++++++++++++
+ src/llama-arch.h    |  1 +
+ src/llama-model.cpp |  7 +++++++
+ src/llama-quant.cpp |  9 +++++++++
+ 4 files changed, 36 insertions(+)
+
+diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
+index b6f20286..b443fcd3 100644
+--- a/src/llama-arch.cpp
++++ b/src/llama-arch.cpp
+@@ -37,6 +37,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
+     { LLM_ARCH_MINICPM3,         "minicpm3"         },
+     { LLM_ARCH_GEMMA,            "gemma"            },
+     { LLM_ARCH_GEMMA2,           "gemma2"           },
++    { LLM_ARCH_GEMMA3,           "gemma3"           },
+     { LLM_ARCH_STARCODER2,       "starcoder2"       },
+     { LLM_ARCH_MAMBA,            "mamba"            },
+     { LLM_ARCH_XVERSE,           "xverse"           },
+@@ -804,6 +805,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
+             { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
+         },
+     },
++    {
++        LLM_ARCH_GEMMA3,
++        {
++            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
++            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
++            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
++            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
++            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
++            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
++            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
++            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
++            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
++            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
++            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
++            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
++            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
++        },
++    },
+     {
+         LLM_ARCH_STARCODER2,
+         {
+diff --git a/src/llama-arch.h b/src/llama-arch.h
+index ec742224..aad92a5d 100644
+--- a/src/llama-arch.h
++++ b/src/llama-arch.h
+@@ -41,6 +41,7 @@ enum llm_arch {
+     LLM_ARCH_MINICPM3,
+     LLM_ARCH_GEMMA,
+     LLM_ARCH_GEMMA2,
++    LLM_ARCH_GEMMA3,
+     LLM_ARCH_STARCODER2,
+     LLM_ARCH_MAMBA,
+     LLM_ARCH_XVERSE,
+diff --git a/src/llama-model.cpp b/src/llama-model.cpp
+index ab1a07d1..70183041 100644
+--- a/src/llama-model.cpp
++++ b/src/llama-model.cpp
+@@ -878,6 +878,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
+                     default: type = LLM_TYPE_UNKNOWN;
+                }
+             } break;
++        case LLM_ARCH_GEMMA3:
++            {
++            } break;
+         case LLM_ARCH_STARCODER2:
+             {
+                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+@@ -2537,6 +2540,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
+                         layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+                     }
+                 } break;
++            case LLM_ARCH_GEMMA3:
++                {
++                } break;
+             case LLM_ARCH_STARCODER2:
+                 {
+                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+@@ -4029,6 +4035,7 @@ enum llama_rope_type llama_model_rope_type(const struct llama_model * model) {
+         case LLM_ARCH_PHIMOE:
+         case LLM_ARCH_GEMMA:
+         case LLM_ARCH_GEMMA2:
++        case LLM_ARCH_GEMMA3:
+         case LLM_ARCH_STARCODER2:
+         case LLM_ARCH_OPENELM:
+         case LLM_ARCH_GPTNEOX:
+diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
+index 6eb1da08..d2f3a510 100644
+--- a/src/llama-quant.cpp
++++ b/src/llama-quant.cpp
+@@ -737,6 +737,15 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
+         // This used to be a regex, but <regex> has an extreme cost to compile times.
+         bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
+ 
++        // don't quantize vision stuff
++        quantize &= name.find("v.blk.") == std::string::npos;
++
++        quantize &= name.find("mm.mm_input_projection.weight") == std::string::npos;
++        quantize &= name.find("mm.mm_soft_emb_norm.weight") == std::string::npos;
++        quantize &= name.find("v.patch_embedding.weight") == std::string::npos;
++        quantize &= name.find("v.position_embedding.weight") == std::string::npos;
++        quantize &= name.find("v.post_layernorm.weight") == std::string::npos;
++
+         // quantize only 2D and 3D tensors (experts)
+         quantize &= (ggml_n_dims(tensor) >= 2);
+ 

llama/sampling_ext.cpp

@@ -2,6 +2,9 @@
 #include "sampling.h"
 #include "sampling_ext.h"
 #include "json-schema-to-grammar.h"
+#include "llama.h"
+#include "llama-model.h"
+#include "llama-model-loader.h"
 
 struct common_sampler *common_sampler_cinit(const struct llama_model *model, struct common_sampler_cparams *params) {
     try {
@@ -64,3 +67,22 @@ int schema_to_grammar(const char *json_schema, char *grammar, size_t max_len)
         return 0;
     }
 }
+
+struct llama_vocab * llama_load_vocab_from_file(const char * fname) {
+    llama_vocab * vocab = new llama_vocab();
+    try {
+        const auto kv = LLM_KV(LLM_ARCH_UNKNOWN);
+        std::vector<std::string> splits = {};
+        llama_model_loader ml(std::string(fname), splits, false, false, nullptr);
+        vocab->load(ml, kv);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
+        return nullptr;
+    }
+
+    return vocab;
+}
+
+void llama_free_vocab(struct llama_vocab * vocab) {
+    delete vocab;
+}

llama/sampling_ext.h

@@ -35,6 +35,9 @@ extern "C"
 
     int schema_to_grammar(const char *json_schema, char *grammar, size_t max_len);
 
+    struct llama_vocab * llama_load_vocab_from_file(const char * fname);
+    void llama_free_vocab(struct llama_vocab * vocab);
+
 #ifdef __cplusplus
 }
 #endif

llm/memory.go

@@ -115,6 +115,9 @@ func EstimateGPULayers(gpus []discover.GpuInfo, f *ggml.GGML, projectors []strin
 		// multimodal models require at least 2048 context
 		opts.NumCtx = max(opts.NumCtx, 2048)
 	}
+	if projectorWeights == 0 && projectorGraph == 0 {
+		projectorWeights, projectorGraph = f.VisionGraphSize()
+	}
 
 	layers := f.Tensors().GroupLayers()
 	// add one layer worth of memory as a buffer
@@ -215,8 +218,8 @@ func EstimateGPULayers(gpus []discover.GpuInfo, f *ggml.GGML, projectors []strin
 		if blk, ok := layers[fmt.Sprintf("blk.%d", i)]; ok {
 			layerSize = blk.Size()
 			layerSize += kv / f.KV().BlockCount()
+			memoryWeights += blk.Size()
 		}
-		memoryWeights += layerSize
 
 		if opts.NumGPU >= 0 && layerCount >= opts.NumGPU {
 			// Stop allocating on GPU(s) once we hit the users target NumGPU
@@ -373,7 +376,7 @@ func (m MemoryEstimate) LogValue() slog.Value {
 				// memory of the weights
 				"total", format.HumanBytes2(m.memoryWeights),
 				// memory of repeating layers
-				"repeating", format.HumanBytes2(m.memoryWeights-m.memoryLayerOutput),
+				"repeating", format.HumanBytes2(m.memoryWeights),
 				// memory of non-repeating layers
 				"nonrepeating", format.HumanBytes2(m.memoryLayerOutput),
 			),

llm/server.go

@@ -29,7 +29,9 @@ import (
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/grammar"
 	"github.com/ollama/ollama/llama"
+	"github.com/ollama/ollama/model"
 )
 
 type LlamaServer interface {
@@ -54,8 +56,15 @@ type llmServer struct {
 	options     api.Options
 	numParallel int
 	modelPath   string
-	modelLock   sync.Mutex   // Temporary until we switch fully to Go server
-	model       *llama.Model // If non-nil, the runner is a new Go server
+
+	// llamaModel is an instance of the cgo llama.cpp model definition
+	// nil if this server is running the new engine
+	llamaModel     *llama.Model
+	llamaModelLock sync.Mutex
+
+	// textProcessor handles text encoding/decoding for the model in the Ollama engine
+	// nil if this server is running the llama.cpp based engine
+	textProcessor model.TextProcessor
 
 	estimate    MemoryEstimate
 	totalLayers uint64
@@ -89,7 +98,7 @@ func LoadModel(model string, maxArraySize int) (*ggml.GGML, error) {
 
 // NewLlamaServer will run a server for the given GPUs
 // The gpu list must be a single family.
-func NewLlamaServer(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapters, projectors []string, opts api.Options, numParallel int) (LlamaServer, error) {
+func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, adapters, projectors []string, opts api.Options, numParallel int) (LlamaServer, error) {
 	systemInfo := discover.GetSystemInfo()
 	systemTotalMemory := systemInfo.System.TotalMemory
 	systemFreeMemory := systemInfo.System.FreeMemory
@@ -130,7 +139,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapt
 	slog.Info("offload", "", estimate)
 
 	params := []string{
-		"--model", model,
+		"--model", modelPath,
 		"--ctx-size", strconv.Itoa(opts.NumCtx),
 		"--batch-size", strconv.Itoa(opts.NumBatch),
 	}
@@ -153,11 +162,6 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapt
 		}
 	}
 
-	if len(projectors) > 0 {
-		// TODO: applying multiple projectors is not supported by the llama.cpp server yet
-		params = append(params, "--mmproj", projectors[0])
-	}
-
 	defaultThreads := systemInfo.GetOptimalThreadCount()
 	if opts.NumThread > 0 {
 		params = append(params, "--threads", strconv.Itoa(opts.NumThread))
@@ -257,6 +261,34 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapt
 		}
 	}
 	slog.Debug("compatible gpu libraries", "compatible", compatible)
+	exe, err := os.Executable()
+	if err != nil {
+		return nil, fmt.Errorf("unable to lookup executable path: %w", err)
+	}
+
+	if eval, err := filepath.EvalSymlinks(exe); err == nil {
+		exe = eval
+	}
+
+	var llamaModel *llama.Model
+	var textProcessor model.TextProcessor
+	if envconfig.NewEngine() || f.KV().OllamaEngineRequired() {
+		textProcessor, err = model.NewTextProcessor(modelPath)
+		if err != nil {
+			// To prepare for opt-out mode, instead of treating this as an error, we fallback to the old runner
+			slog.Debug("model not yet supported by Ollama engine, switching to compatibility mode", "model", modelPath, "error", err)
+		}
+	}
+	if textProcessor == nil {
+		llamaModel, err = llama.LoadModelFromFile(modelPath, llama.ModelParams{VocabOnly: true})
+		if err != nil {
+			return nil, err
+		}
+	}
+
+	if len(projectors) > 0 && llamaModel != nil {
+		params = append(params, "--mmproj", projectors[0])
+	}
 
 	// iterate through compatible GPU libraries such as 'cuda_v12', 'cuda_v11', 'rocm', etc.
 	// adding each library's respective path to the LD_LIBRARY_PATH, until finally running
@@ -275,7 +307,9 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapt
 			port = rand.Intn(65535-49152) + 49152 // get a random port in the ephemeral range
 		}
 		finalParams := []string{"runner"}
-		if envconfig.NewEngine() {
+		if textProcessor != nil {
+			// New engine
+			// TODO - if we have failure to load scenarios, add logic to retry with the old runner
 			finalParams = append(finalParams, "--ollama-engine")
 		}
 		finalParams = append(finalParams, params...)
@@ -315,28 +349,20 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapt
 		// finally, add the root library path
 		libraryPaths = append(libraryPaths, discover.LibOllamaPath)
 
-		exe, err := os.Executable()
-		if err != nil {
-			return nil, fmt.Errorf("unable to lookup executable path: %w", err)
-		}
-
-		if eval, err := filepath.EvalSymlinks(exe); err == nil {
-			exe = eval
-		}
-
-		// TODO - once fully switched to the Go runner, load the model here for tokenize/detokenize cgo access
 		s := &llmServer{
-			port:        port,
-			cmd:         exec.Command(exe, finalParams...),
-			status:      NewStatusWriter(os.Stderr),
-			options:     opts,
-			modelPath:   model,
-			estimate:    estimate,
-			numParallel: numParallel,
-			sem:         semaphore.NewWeighted(int64(numParallel)),
-			totalLayers: f.KV().BlockCount() + 1,
-			gpus:        gpus,
-			done:        make(chan error, 1),
+			port:          port,
+			cmd:           exec.Command(exe, finalParams...),
+			status:        NewStatusWriter(os.Stderr),
+			options:       opts,
+			modelPath:     modelPath,
+			llamaModel:    llamaModel,
+			textProcessor: textProcessor,
+			estimate:      estimate,
+			numParallel:   numParallel,
+			sem:           semaphore.NewWeighted(int64(numParallel)),
+			totalLayers:   f.KV().BlockCount() + 1,
+			gpus:          gpus,
+			done:          make(chan error, 1),
 		}
 
 		s.cmd.Env = os.Environ()
@@ -377,7 +403,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapt
 			s.cmd.Env = append(s.cmd.Env, visibleDevicesEnv+"="+visibleDevicesEnvVal)
 		}
 
-		slog.Info("starting llama server", "cmd", s.cmd.String())
+		slog.Info("starting llama server", "cmd", s.cmd)
 		if envconfig.Debug() {
 			filteredEnv := []string{}
 			for _, ev := range s.cmd.Env {
@@ -405,6 +431,9 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapt
 			}
 			err := fmt.Errorf("error starting runner: %v %s", err, msg)
 			if len(compatible) == 0 {
+				if llamaModel != nil {
+					llama.FreeModel(llamaModel)
+				}
 				return nil, err
 			}
 
@@ -442,7 +471,7 @@ const ( // iota is reset to 0
 	ServerStatusError
 )
 
-func (s ServerStatus) ToString() string {
+func (s ServerStatus) String() string {
 	switch s {
 	case ServerStatusReady:
 		return "llm server ready"
@@ -457,12 +486,9 @@ func (s ServerStatus) ToString() string {
 	}
 }
 
-type ServerStatusResp struct {
-	Status          string  `json:"status"`
-	SlotsIdle       int     `json:"slots_idle"`
-	SlotsProcessing int     `json:"slots_processing"`
-	Error           string  `json:"error"`
-	Progress        float32 `json:"progress"`
+type ServerStatusResponse struct {
+	Status   ServerStatus `json:"status"`
+	Progress float32      `json:"progress"`
 }
 
 func (s *llmServer) getServerStatus(ctx context.Context) (ServerStatus, error) {
@@ -474,7 +500,7 @@ func (s *llmServer) getServerStatus(ctx context.Context) (ServerStatus, error) {
 		}
 		if s.cmd.ProcessState.ExitCode() == -1 {
 			// Most likely a signal killed it, log some more details to try to help troubleshoot
-			slog.Warn("llama runner process no longer running", "sys", s.cmd.ProcessState.Sys(), "string", s.cmd.ProcessState.String())
+			slog.Warn("llama runner process no longer running", "sys", s.cmd.ProcessState.Sys(), "string", s.cmd.ProcessState)
 		}
 		return ServerStatusError, fmt.Errorf("llama runner process no longer running: %d %s", s.cmd.ProcessState.ExitCode(), msg)
 	}
@@ -499,21 +525,19 @@ func (s *llmServer) getServerStatus(ctx context.Context) (ServerStatus, error) {
 		return ServerStatusError, fmt.Errorf("read health request: %w", err)
 	}
 
-	var status ServerStatusResp
-	if err := json.Unmarshal(body, &status); err != nil {
+	var ssr ServerStatusResponse
+	if err := json.Unmarshal(body, &ssr); err != nil {
 		return ServerStatusError, fmt.Errorf("health unmarshal encode response: %w", err)
 	}
 
-	switch status.Status {
-	case "ok":
-		return ServerStatusReady, nil
-	case "no slot available":
-		return ServerStatusNoSlotsAvailable, nil
-	case "loading model":
-		s.loadProgress = status.Progress
-		return ServerStatusLoadingModel, nil
+	switch ssr.Status {
+	case ServerStatusLoadingModel:
+		s.loadProgress = ssr.Progress
+		return ssr.Status, nil
+	case ServerStatusReady, ServerStatusNoSlotsAvailable:
+		return ssr.Status, nil
 	default:
-		return ServerStatusError, fmt.Errorf("server error: %+v", status)
+		return ssr.Status, fmt.Errorf("server error: %+v", ssr)
 	}
 }
 
@@ -588,7 +612,7 @@ func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
 		status, _ := s.getServerStatus(ctx)
 		if lastStatus != status && status != ServerStatusReady {
 			// Only log on status changes
-			slog.Info("waiting for server to become available", "status", status.ToString())
+			slog.Info("waiting for server to become available", "status", status)
 		}
 		switch status {
 		case ServerStatusReady:
@@ -602,7 +626,7 @@ func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
 				slog.Debug(fmt.Sprintf("model load progress %0.2f", s.loadProgress))
 				stallTimer = time.Now().Add(stallDuration)
 			} else if !fullyLoaded && int(s.loadProgress*100.0) >= 100 {
-				slog.Debug("model load completed, waiting for server to become available", "status", status.ToString())
+				slog.Debug("model load completed, waiting for server to become available", "status", status)
 				stallTimer = time.Now().Add(stallDuration)
 				fullyLoaded = true
 			}
@@ -643,63 +667,26 @@ type ImageData struct {
 	AspectRatioID int    `json:"aspect_ratio_id"`
 }
 
-type completion struct {
-	Content      string `json:"content"`
-	Model        string `json:"model"`
-	Prompt       string `json:"prompt"`
-	Stop         bool   `json:"stop"`
-	StoppedLimit bool   `json:"stopped_limit"`
-
-	Timings struct {
-		PredictedN  int     `json:"predicted_n"`
-		PredictedMS float64 `json:"predicted_ms"`
-		PromptN     int     `json:"prompt_n"`
-		PromptMS    float64 `json:"prompt_ms"`
-	}
-}
-
 type CompletionRequest struct {
 	Prompt  string
 	Format  json.RawMessage
 	Images  []ImageData
 	Options *api.Options
+
+	Grammar string // set before sending the request to the subprocess
 }
 
 type CompletionResponse struct {
-	Content            string
-	DoneReason         string
-	Done               bool
-	PromptEvalCount    int
-	PromptEvalDuration time.Duration
-	EvalCount          int
-	EvalDuration       time.Duration
+	Content            string        `json:"content"`
+	DoneReason         string        `json:"done_reason"`
+	Done               bool          `json:"done"`
+	PromptEvalCount    int           `json:"prompt_eval_count"`
+	PromptEvalDuration time.Duration `json:"prompt_eval_duration"`
+	EvalCount          int           `json:"eval_count"`
+	EvalDuration       time.Duration `json:"eval_duration"`
 }
 
 func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error {
-	request := map[string]any{
-		"prompt":            req.Prompt,
-		"stream":            true,
-		"n_predict":         req.Options.NumPredict,
-		"n_keep":            req.Options.NumKeep,
-		"main_gpu":          req.Options.MainGPU,
-		"temperature":       req.Options.Temperature,
-		"top_k":             req.Options.TopK,
-		"top_p":             req.Options.TopP,
-		"min_p":             req.Options.MinP,
-		"typical_p":         req.Options.TypicalP,
-		"repeat_last_n":     req.Options.RepeatLastN,
-		"repeat_penalty":    req.Options.RepeatPenalty,
-		"presence_penalty":  req.Options.PresencePenalty,
-		"frequency_penalty": req.Options.FrequencyPenalty,
-		"mirostat":          req.Options.Mirostat,
-		"mirostat_tau":      req.Options.MirostatTau,
-		"mirostat_eta":      req.Options.MirostatEta,
-		"seed":              req.Options.Seed,
-		"stop":              req.Options.Stop,
-		"image_data":        req.Images,
-		"cache_prompt":      true,
-	}
-
 	if len(req.Format) > 0 {
 		switch string(req.Format) {
 		case `null`, `""`:
@@ -707,21 +694,31 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 			// these as "not set".
 			break
 		case `"json"`:
-			request["grammar"] = grammarJSON
+			req.Grammar = grammarJSON
 		default:
 			if req.Format[0] != '{' {
 				return fmt.Errorf("invalid format: %q; expected \"json\" or a valid JSON Schema object", req.Format)
 			}
 
 			// User provided a JSON schema
-			g := llama.SchemaToGrammar(req.Format)
-			if g == nil {
-				return fmt.Errorf("invalid JSON schema in format")
+			g, err := grammar.FromSchema(nil, req.Format)
+			if err != nil {
+				return fmt.Errorf("invalid JSON schema in format: %w", err)
 			}
-			request["grammar"] = string(g)
+			req.Grammar = string(g)
 		}
 	}
 
+	if req.Options == nil {
+		opts := api.DefaultOptions()
+		req.Options = &opts
+	}
+
+	if req.Options == nil {
+		opts := api.DefaultOptions()
+		req.Options = &opts
+	}
+
 	if err := s.sem.Acquire(ctx, 1); err != nil {
 		if errors.Is(err, context.Canceled) {
 			slog.Info("aborting completion request due to client closing the connection")
@@ -736,13 +733,12 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 	if req.Options.NumPredict < 0 || req.Options.NumPredict > 10*s.options.NumCtx {
 		req.Options.NumPredict = 10 * s.options.NumCtx
 	}
-
 	// Make sure the server is ready
 	status, err := s.getServerStatusRetry(ctx)
 	if err != nil {
 		return err
 	} else if status != ServerStatusReady {
-		return fmt.Errorf("unexpected server status: %s", status.ToString())
+		return fmt.Errorf("unexpected server status: %s", status)
 	}
 
 	// Handling JSON marshaling with special characters unescaped.
@@ -750,7 +746,7 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 	enc := json.NewEncoder(buffer)
 	enc.SetEscapeHTML(false)
 
-	if err := enc.Encode(request); err != nil {
+	if err := enc.Encode(req); err != nil {
 		return fmt.Errorf("failed to marshal data: %v", err)
 	}
 
@@ -801,7 +797,7 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 				evt = line
 			}
 
-			var c completion
+			var c CompletionResponse
 			if err := json.Unmarshal(evt, &c); err != nil {
 				return fmt.Errorf("error unmarshalling llm prediction response: %v", err)
 			}
@@ -825,20 +821,8 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 				})
 			}
 
-			if c.Stop {
-				doneReason := "stop"
-				if c.StoppedLimit {
-					doneReason = "length"
-				}
-
-				fn(CompletionResponse{
-					Done:               true,
-					DoneReason:         doneReason,
-					PromptEvalCount:    c.Timings.PromptN,
-					PromptEvalDuration: parseDurationMs(c.Timings.PromptMS),
-					EvalCount:          c.Timings.PredictedN,
-					EvalDuration:       parseDurationMs(c.Timings.PredictedMS),
-				})
+			if c.Done {
+				fn(c)
 				return nil
 			}
 		}
@@ -886,7 +870,7 @@ func (s *llmServer) Embedding(ctx context.Context, input string) ([]float32, err
 	if err != nil {
 		return nil, err
 	} else if status != ServerStatusReady {
-		return nil, fmt.Errorf("unexpected server status: %s", status.ToString())
+		return nil, fmt.Errorf("unexpected server status: %s", status)
 	}
 
 	data, err := json.Marshal(EmbeddingRequest{Content: input})
@@ -933,64 +917,25 @@ type TokenizeResponse struct {
 }
 
 func (s *llmServer) Tokenize(ctx context.Context, content string) ([]int, error) {
-	s.modelLock.Lock()
-	defer s.modelLock.Unlock()
-	if s.model != nil {
-		return s.model.Tokenize(content, false, true)
-	}
+	s.llamaModelLock.Lock()
+	defer s.llamaModelLock.Unlock()
 
-	// Make sure the server is ready
-	status, err := s.getServerStatus(ctx)
-	if err != nil {
-		return nil, err
-	} else if status != ServerStatusReady && status != ServerStatusNoSlotsAvailable {
-		return nil, fmt.Errorf("unexpected server status: %s", status.ToString())
+	if s.llamaModel != nil {
+		return s.llamaModel.Tokenize(content, false, true)
 	}
-
-	data, err := json.Marshal(TokenizeRequest{Content: content})
-	if err != nil {
-		return nil, fmt.Errorf("marshaling encode data: %w", err)
-	}
-
-	req, err := http.NewRequestWithContext(ctx, http.MethodPost, fmt.Sprintf("http://127.0.0.1:%d/tokenize", s.port), bytes.NewBuffer(data))
-	if err != nil {
-		return nil, fmt.Errorf("encode request: %w", err)
-	}
-	req.Header.Set("Content-Type", "application/json")
-
-	resp, err := http.DefaultClient.Do(req)
-	if err != nil {
-		return nil, fmt.Errorf("do encode request: %w", err)
-	}
-	defer resp.Body.Close()
-	if resp.StatusCode == http.StatusNotFound {
-		if s.model == nil {
-			slog.Debug("new runner detected, loading model for cgo tokenization")
-			m, err := llama.LoadModelFromFile(s.modelPath, llama.ModelParams{VocabOnly: true})
-			if err != nil {
-				return nil, err
-			}
-			s.model = m
+	if s.textProcessor != nil {
+		tokens, err := s.textProcessor.Encode(content, false)
+		if err != nil {
+			return nil, err
 		}
-		return s.model.Tokenize(content, false, true)
+		toks := make([]int, len(tokens))
+		for i, t := range tokens {
+			toks[i] = int(t)
+		}
+		return toks, nil
 	}
-
-	body, err := io.ReadAll(resp.Body)
-	if err != nil {
-		return nil, fmt.Errorf("read encode request: %w", err)
-	}
-
-	if resp.StatusCode >= 400 {
-		log.Printf("llm encode error: %s", body)
-		return nil, fmt.Errorf("%s", body)
-	}
-
-	var encoded TokenizeResponse
-	if err := json.Unmarshal(body, &encoded); err != nil {
-		return nil, fmt.Errorf("unmarshal encode response: %w", err)
-	}
-
-	return encoded.Tokens, nil
+	// not reached
+	return nil, fmt.Errorf("no tokenizer configured")
 }
 
 type DetokenizeRequest struct {
@@ -1002,80 +947,38 @@ type DetokenizeResponse struct {
 }
 
 func (s *llmServer) Detokenize(ctx context.Context, tokens []int) (string, error) {
-	s.modelLock.Lock()
-	defer s.modelLock.Unlock()
-	if s.model != nil {
+	s.llamaModelLock.Lock()
+	defer s.llamaModelLock.Unlock()
+
+	if s.llamaModel != nil {
 		var resp string
 		for _, token := range tokens {
-			resp += s.model.TokenToPiece(token)
+			resp += s.llamaModel.TokenToPiece(token)
 		}
 		return resp, nil
 	}
-	// Make sure the server is ready
-	status, err := s.getServerStatus(ctx)
-	if err != nil {
-		return "", err
-	} else if status != ServerStatusReady && status != ServerStatusNoSlotsAvailable {
-		return "", fmt.Errorf("unexpected server status: %s", status.ToString())
-	}
-
-	data, err := json.Marshal(DetokenizeRequest{Tokens: tokens})
-	if err != nil {
-		return "", fmt.Errorf("marshaling decode data: %w", err)
-	}
-
-	req, err := http.NewRequestWithContext(ctx, http.MethodPost, fmt.Sprintf("http://127.0.0.1:%d/detokenize", s.port), bytes.NewBuffer(data))
-	if err != nil {
-		return "", fmt.Errorf("decode request: %w", err)
-	}
-	req.Header.Set("Content-Type", "application/json")
-
-	resp, err := http.DefaultClient.Do(req)
-	if err != nil {
-		return "", fmt.Errorf("do decode request: %w", err)
-	}
-	defer resp.Body.Close()
-	if resp.StatusCode == http.StatusNotFound {
-		if s.model == nil {
-			slog.Debug("new runner detected, loading model for cgo tokenization")
-			m, err := llama.LoadModelFromFile(s.modelPath, llama.ModelParams{VocabOnly: true})
-			if err != nil {
-				return "", err
-			}
-			s.model = m
+	if s.textProcessor != nil {
+		toks := make([]int32, len(tokens))
+		for i, t := range tokens {
+			toks[i] = int32(t)
 		}
-		var resp string
-		for _, token := range tokens {
-			resp += s.model.TokenToPiece(token)
+		content, err := s.textProcessor.Decode(toks)
+		if err != nil {
+			return "", err
 		}
-		return resp, nil
+		return content, nil
 	}
-
-	body, err := io.ReadAll(resp.Body)
-	if err != nil {
-		return "", fmt.Errorf("read decode request: %w", err)
-	}
-
-	if resp.StatusCode >= 400 {
-		log.Printf("llm decode error: %s", body)
-		return "", fmt.Errorf("%s", body)
-	}
-
-	var decoded DetokenizeResponse
-	if err := json.Unmarshal(body, &decoded); err != nil {
-		return "", fmt.Errorf("unmarshal encode response: %w", err)
-	}
-
-	return decoded.Content, nil
+	// not reached
+	return "", fmt.Errorf("no tokenizer configured")
 }
 
 func (s *llmServer) Close() error {
-	s.modelLock.Lock()
-	if s.model != nil {
-		llama.FreeModel(s.model)
-		s.model = nil
+	s.llamaModelLock.Lock()
+	if s.llamaModel != nil {
+		llama.FreeModel(s.llamaModel)
+		s.llamaModel = nil
 	}
-	s.modelLock.Unlock()
+	s.llamaModelLock.Unlock()
 
 	if s.cmd != nil {
 		slog.Debug("stopping llama server")
@@ -1112,12 +1015,3 @@ func (s *llmServer) EstimatedVRAMByGPU(gpuID string) uint64 {
 	}
 	return 0
 }
-
-func parseDurationMs(ms float64) time.Duration {
-	dur, err := time.ParseDuration(fmt.Sprintf("%fms", ms))
-	if err != nil {
-		panic(err)
-	}
-
-	return dur
-}

ml/backend.go

@@ -2,9 +2,11 @@ package ml
 
 import (
 	"bytes"
+	"context"
 	"encoding/binary"
 	"fmt"
 	"os"
+	"slices"
 	"strconv"
 	"strings"
 )
@@ -14,20 +16,55 @@ type Config interface {
 	String(string, ...string) string
 	Uint(string, ...uint32) uint32
 	Float(string, ...float32) float32
+	Bool(string, ...bool) bool
 
 	Strings(string, ...[]string) []string
 	Uints(string, ...[]uint32) []uint32
+	Floats(string, ...[]float32) []float32
 }
 
 type Backend interface {
 	Config() Config
 	Get(name string) Tensor
 	NewContext() Context
-	SystemInfo() string
+	NewContextSize(size int) Context
+}
+
+// BackendCacheConfig should be implemented by backends that need special output
+// from the cache to meet specific requirements. It is frequently implemented in
+// conjunction with ScaledDotProductAttention.
+type BackendCacheConfig interface {
+	CacheConfig() CacheConfig
+}
+
+// CacheConfig controls optimizations (mostly backend-specific) that may transform
+// the output the cache to work better with specific kernels.
+type CacheConfig struct {
+	// CachePadding specifies the multiple for the number of tokens of cache history
+	// that will be returned from cache Get for k, v and mask. The capacity of the
+	// cache itself will also be increased to a multiple of this size if needed.
+	CachePadding int
+
+	// PermutedV performs Permute(ctx, 1, 2, 0, 3) on v tensors stored via Put
+	// and return the permuted version via Get. This uses the cache copy operation
+	// to avoid a Contiguous call on the permuted tensor.
+	PermutedV bool
+
+	// MaskDType specifies the data type for generating the mask. If unset it will
+	// default to DTypeF32.
+	MaskDType DType
+
+	// MaskBatchPadding specifies the multiple for the batch size dimension in the mask.
+	// Any position that does not correspond to an actual token will be filled with -Inf.
+	MaskBatchPadding int
 }
 
 // BackendParams controls how the backend loads and executes models
 type BackendParams struct {
+	// Progress is a callback function that allows reporting percentage completion
+	// of model loading
+	Progress func(float32)
+
 	// NumThreads sets the number of threads to use if running on the CPU
 	NumThreads int
 
@@ -39,11 +76,14 @@ type BackendParams struct {
 
 	// TensorSplit is the fraction of the model to offload to each GPU
 	TensorSplit []float32
+
+	// FlashAttention indicates that we should use a fused flash attention kernel
+	FlashAttention bool
 }
 
-var backends = make(map[string]func(*os.File, BackendParams) (Backend, error))
+var backends = make(map[string]func(context.Context, *os.File, BackendParams) (Backend, error))
 
-func RegisterBackend(name string, f func(*os.File, BackendParams) (Backend, error)) {
+func RegisterBackend(name string, f func(context.Context, *os.File, BackendParams) (Backend, error)) {
 	if _, ok := backends[name]; ok {
 		panic("backend: backend already registered")
 	}
@@ -51,23 +91,33 @@ func RegisterBackend(name string, f func(*os.File, BackendParams) (Backend, erro
 	backends[name] = f
 }
 
-func NewBackend(f *os.File, params BackendParams) (Backend, error) {
+func NewBackend(ctx context.Context, f *os.File, params BackendParams) (Backend, error) {
 	if backend, ok := backends["ggml"]; ok {
-		return backend(f, params)
+		return backend(ctx, f, params)
 	}
 
 	return nil, fmt.Errorf("unsupported backend")
 }
 
 type Context interface {
+	Empty(dtype DType, shape ...int) Tensor
 	Zeros(dtype DType, shape ...int) Tensor
 	FromFloatSlice(s []float32, shape ...int) (Tensor, error)
 	FromIntSlice(s []int32, shape ...int) (Tensor, error)
 
-	Forward(Tensor)
+	Forward(...Tensor) Context
 	Compute(...Tensor)
-	MaxTensors() int
+	MaxGraphNodes() int
 	Close()
+
+	// Input returns a context appropriate for creating input tensors
+	Input() Context
+
+	// Output returns a context appropriate for creating output tensors
+	Output() Context
+
+	// Layer returns a context appropriate for creating intermediate tensors
+	Layer(int) Context
 }
 
 type Tensor interface {
@@ -90,8 +140,10 @@ type Tensor interface {
 	RMSNorm(ctx Context, weight Tensor, eps float32) Tensor
 	Scale(ctx Context, s float64) Tensor
 
+	AvgPool2D(ctx Context, k, s int, p float32) Tensor
 	Conv2D(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
-	RoPE(ctx Context, positionIDs, ropeFactors Tensor, dim uint32, base, scale float32) Tensor
+
+	RoPE(ctx Context, positionIDs, ropeFactors Tensor, dim, ropeType uint32, base, scale float32) Tensor
 
 	Tanh(ctx Context) Tensor
 	GELU(ctx Context) Tensor
@@ -101,6 +153,7 @@ type Tensor interface {
 	View(ctx Context, offset int, shape ...int) Tensor
 	Permute(ctx Context, shape ...int) Tensor
 	Contiguous(ctx Context) Tensor
+	Set(ctx Context, t2 Tensor, offset int, strides ...int) Tensor
 
 	Pad(ctx Context, shape ...int) Tensor
 	Unpad(ctx Context, shape ...int) Tensor
@@ -115,6 +168,10 @@ type Tensor interface {
 // operation equivalent to following code on a tensor named
 // query:
 //
+// query = query.Permute(ctx, 0, 2, 1, 3)
+// key = key.Permute(ctx, 0, 2, 1, 3)
+// value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+//
 // kq := key.MulmatFullPrec(ctx, query)
 //
 // kq = kq.Scale(ctx, scale)
@@ -168,8 +225,8 @@ func Dump(ctx Context, t Tensor, opts ...DumpOptions) string {
 		return dump[[]float32](ctx, t, opts[0].Items, func(f float32) string {
 			return strconv.FormatFloat(float64(f), 'f', opts[0].Precision, 32)
 		})
-	case DTypeF16:
-		f32 := ctx.Zeros(DTypeF32, t.Shape()...)
+	case DTypeF16, DTypeQ80, DTypeQ40:
+		f32 := ctx.Empty(DTypeF32, t.Shape()...)
 		f32 = t.Copy(ctx, f32)
 		return dump[[]float32](ctx, f32, opts[0].Items, func(f float32) string {
 			return strconv.FormatFloat(float64(f), 'f', opts[0].Precision, 32)
@@ -185,8 +242,7 @@ func Dump(ctx Context, t Tensor, opts ...DumpOptions) string {
 
 func dump[S ~[]E, E number](ctx Context, t Tensor, items int, fn func(E) string) string {
 	if t.Bytes() == nil {
-		ctx.Forward(t)
-		ctx.Compute(t)
+		ctx.Forward(t).Compute(t)
 	}
 
 	s := make(S, mul(t.Shape()...))
@@ -195,16 +251,17 @@ func dump[S ~[]E, E number](ctx Context, t Tensor, items int, fn func(E) string)
 	}
 
 	shape := t.Shape()
+	slices.Reverse(shape)
 
 	var sb strings.Builder
 	var f func([]int, int)
 	f = func(dims []int, stride int) {
 		prefix := strings.Repeat(" ", len(shape)-len(dims)+1)
-		fmt.Fprint(&sb, "[")
-		defer func() { fmt.Fprint(&sb, "]") }()
+		sb.WriteString("[")
+		defer func() { sb.WriteString("]") }()
 		for i := 0; i < dims[0]; i++ {
 			if i >= items && i < dims[0]-items {
-				fmt.Fprint(&sb, "..., ")
+				sb.WriteString("..., ")
 				// skip to next printable element
 				skip := dims[0] - 2*items
 				if len(dims) > 1 {
@@ -219,9 +276,14 @@ func dump[S ~[]E, E number](ctx Context, t Tensor, items int, fn func(E) string)
 					fmt.Fprint(&sb, ",", strings.Repeat("\n", len(dims)-1), prefix)
 				}
 			} else {
-				fmt.Fprint(&sb, fn(s[stride+i]))
+				text := fn(s[stride+i])
+				if len(text) > 0 && text[0] != '-' {
+					sb.WriteString(" ")
+				}
+
+				sb.WriteString(text)
 				if i < dims[0]-1 {
-					fmt.Fprint(&sb, ", ")
+					sb.WriteString(", ")
 				}
 			}
 		}
@@ -237,5 +299,7 @@ const (
 	DTypeOther DType = iota
 	DTypeF32
 	DTypeF16
+	DTypeQ80
+	DTypeQ40
 	DTypeI32
 )

ml/backend/ggml/ggml/include/gguf.h

@@ -114,6 +114,7 @@ extern "C" {
     // get raw pointer to the first element of the array with the given key_id
     // for bool arrays, note that they are always stored as int8 on all platforms (usually this makes no difference)
     GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int64_t key_id);
+    GGML_API size_t       gguf_get_arr_data_n(const struct gguf_context * ctx, int64_t key_id);
 
     // get ith C string from array with given key_id
     GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int64_t key_id, size_t i);

ml/backend/ggml/ggml/include/ollama-debug.h

@@ -0,0 +1,11 @@
+#include "ggml.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+void ollama_debug(const struct ggml_tensor *tensor, bool verbose);
+
+#ifdef __cplusplus
+}
+#endif

ml/backend/ggml/ggml/src/ggml-backend-reg.cpp

@@ -484,33 +484,29 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
         }
         fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
         for (const auto & entry : dir_it) {
-            try {
-                if (entry.is_regular_file()) {
-                    std::string filename = entry.path().filename().string();
-                    std::string ext = entry.path().extension().string();
-                    if (filename.find(file_prefix) == 0 && ext == backend_filename_suffix()) {
-                        dl_handle_ptr handle { dl_load_library(entry.path()) };
-                        if (!handle) {
-                            GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path_to_string(entry.path()).c_str());
-                            continue;
-                        }
+            if (entry.is_regular_file()) {
+                std::string filename = entry.path().filename().string();
+                std::string ext = entry.path().extension().string();
+                if (filename.find(file_prefix) == 0 && ext == backend_filename_suffix()) {
+                    dl_handle_ptr handle { dl_load_library(entry.path()) };
+                    if (!handle) {
+                        GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path_to_string(entry.path()).c_str());
+                        continue;
+                    }
 
-                        auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
-                        if (!score_fn) {
-                            GGML_LOG_DEBUG("%s: failed to find ggml_backend_score in %s\n", __func__, path_to_string(entry.path()).c_str());
-                            continue;
-                        }
+                    auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
+                    if (!score_fn) {
+                        GGML_LOG_DEBUG("%s: failed to find ggml_backend_score in %s\n", __func__, path_to_string(entry.path()).c_str());
+                        continue;
+                    }
 
-                        int s = score_fn();
-                        GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, path_to_string(entry.path()).c_str(), s);
-                        if (s > best_score) {
-                            best_score = s;
-                            best_path = entry.path();
-                        }
+                    int s = score_fn();
+                    GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, path_to_string(entry.path()).c_str(), s);
+                    if (s > best_score) {
+                        best_score = s;
+                        best_path = entry.path();
                     }
                 }
-            } catch (const std::exception & e) {
-                GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, path_to_string(entry.path()).c_str(), e.what());
             }
         }
     }
@@ -533,6 +529,14 @@ void ggml_backend_load_all() {
     ggml_backend_load_all_from_path(nullptr);
 }
 
+static void ggml_backend_try_load_best(const char * name, bool silent, const char * user_search_path) {
+    try {
+        ggml_backend_load_best(name, silent, user_search_path);
+    } catch (const std::exception & e) {
+        GGML_LOG_DEBUG("%s: failed to load %s: %s\n", __func__, name, e.what());
+    }
+}
+
 void ggml_backend_load_all_from_path(const char * dir_path) {
 #ifdef NDEBUG
     bool silent = true;
@@ -540,18 +544,18 @@ void ggml_backend_load_all_from_path(const char * dir_path) {
     bool silent = false;
 #endif
 
-    ggml_backend_load_best("blas", silent, dir_path);
-    ggml_backend_load_best("cann", silent, dir_path);
-    ggml_backend_load_best("cuda", silent, dir_path);
-    ggml_backend_load_best("hip", silent, dir_path);
-    ggml_backend_load_best("kompute", silent, dir_path);
-    ggml_backend_load_best("metal", silent, dir_path);
-    ggml_backend_load_best("rpc", silent, dir_path);
-    ggml_backend_load_best("sycl", silent, dir_path);
-    ggml_backend_load_best("vulkan", silent, dir_path);
-    ggml_backend_load_best("opencl", silent, dir_path);
-    ggml_backend_load_best("musa", silent, dir_path);
-    ggml_backend_load_best("cpu", silent, dir_path);
+    ggml_backend_try_load_best("blas", silent, dir_path);
+    ggml_backend_try_load_best("cann", silent, dir_path);
+    ggml_backend_try_load_best("cuda", silent, dir_path);
+    ggml_backend_try_load_best("hip", silent, dir_path);
+    ggml_backend_try_load_best("kompute", silent, dir_path);
+    ggml_backend_try_load_best("metal", silent, dir_path);
+    ggml_backend_try_load_best("rpc", silent, dir_path);
+    ggml_backend_try_load_best("sycl", silent, dir_path);
+    ggml_backend_try_load_best("vulkan", silent, dir_path);
+    ggml_backend_try_load_best("opencl", silent, dir_path);
+    ggml_backend_try_load_best("musa", silent, dir_path);
+    ggml_backend_try_load_best("cpu", silent, dir_path);
     // check the environment variable GGML_BACKEND_PATH to load an out-of-tree backend
     const char * backend_path = std::getenv("GGML_BACKEND_PATH");
     if (backend_path) {

ml/backend/ggml/ggml/src/ggml-cpu/cpu_debug.go

@@ -0,0 +1,6 @@
+//go:build debug
+
+package cpu
+
+// #cgo CPPFLAGS: -DOLLAMA_DEBUG
+import "C"

ml/backend/ggml/ggml/src/ggml-cpu/ggml-cpu.c

@@ -11,6 +11,8 @@
 #include "ggml-threading.h"
 #include "ggml.h"
 
+#include "ollama-debug.h"
+
 #if defined(_MSC_VER) || defined(__MINGW32__)
 #include <malloc.h> // using malloc.h with MSC/MINGW
 #elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
@@ -14103,6 +14105,10 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
 
         ggml_compute_forward(&params, node);
 
+#ifdef OLLAMA_DEBUG
+        ollama_debug(node, true);
+#endif
+
         if (state->ith == 0 && cplan->abort_callback &&
                 cplan->abort_callback(cplan->abort_callback_data)) {
             atomic_store_explicit(&tp->abort, node_n + 1, memory_order_relaxed);

ml/backend/ggml/ggml/src/ggml.go

@@ -7,13 +7,30 @@ package ggml
 // #include <stdlib.h>
 // #include "ggml-backend.h"
 // extern void sink(int level, char *text, void *user_data);
+// static struct ggml_backend_feature * first_feature(ggml_backend_get_features_t fp, ggml_backend_reg_t reg) { return fp(reg); }
+// static struct ggml_backend_feature * next_feature(struct ggml_backend_feature * feature) { return &feature[1]; }
+/*
+typedef enum { COMPILER_CLANG, COMPILER_GNUC, COMPILER_UNKNOWN } COMPILER;
+static COMPILER compiler_name(void) {
+#if defined(__clang__)
+	return COMPILER_CLANG;
+#elif defined(__GNUC__)
+	return COMPILER_GNUC;
+#else
+	return COMPILER_UNKNOWN;
+#endif
+}
+*/
 import "C"
 
 import (
+	"context"
+	"fmt"
 	"log/slog"
 	"os"
 	"path/filepath"
 	"runtime"
+	"strconv"
 	"strings"
 	"sync"
 	"unsafe"
@@ -22,21 +39,14 @@ import (
 )
 
 func init() {
-	C.ggml_log_set((C.ggml_log_callback)(C.sink), nil)
+	C.ggml_log_set(C.ggml_log_callback(C.sink), nil)
 }
 
 //export sink
 func sink(level C.int, text *C.char, _ unsafe.Pointer) {
-	msg := strings.TrimSpace(C.GoString(text))
-	switch level {
-	case C.GGML_LOG_LEVEL_DEBUG:
-		slog.Debug(msg)
-	case C.GGML_LOG_LEVEL_INFO:
-		slog.Info(msg)
-	case C.GGML_LOG_LEVEL_WARN:
-		slog.Warn(msg)
-	case C.GGML_LOG_LEVEL_ERROR:
-		slog.Error(msg)
+	// slog levels zeros INFO and are multiples of 4
+	if slog.Default().Enabled(context.TODO(), slog.Level(int(level-C.GGML_LOG_LEVEL_INFO)*4)) {
+		fmt.Fprint(os.Stderr, C.GoString(text))
 	}
 }
 
@@ -95,4 +105,43 @@ var OnceLoad = sync.OnceFunc(func() {
 			visited[abspath] = struct{}{}
 		}
 	}
+
+	slog.Info("system", "", system{})
 })
+
+type system struct{}
+
+func (system) LogValue() slog.Value {
+	var attrs []slog.Attr
+	names := make(map[string]int)
+	for i := range C.ggml_backend_dev_count() {
+		r := C.ggml_backend_dev_backend_reg(C.ggml_backend_dev_get(i))
+
+		func() {
+			fName := C.CString("ggml_backend_get_features")
+			defer C.free(unsafe.Pointer(fName))
+
+			if fn := C.ggml_backend_reg_get_proc_address(r, fName); fn != nil {
+				var features []any
+				for f := C.first_feature(C.ggml_backend_get_features_t(fn), r); f.name != nil; f = C.next_feature(f) {
+					features = append(features, C.GoString(f.name), C.GoString(f.value))
+				}
+
+				name := C.GoString(C.ggml_backend_reg_name(r))
+				attrs = append(attrs, slog.Group(name+"."+strconv.Itoa(names[name]), features...))
+				names[name] += 1
+			}
+		}()
+	}
+
+	switch C.compiler_name() {
+	case C.COMPILER_CLANG:
+		attrs = append(attrs, slog.String("compiler", "cgo(clang)"))
+	case C.COMPILER_GNUC:
+		attrs = append(attrs, slog.String("compiler", "cgo(gcc)"))
+	default:
+		attrs = append(attrs, slog.String("compiler", "cgo(unknown)"))
+	}
+
+	return slog.GroupValue(attrs...)
+}

ml/backend/ggml/ggml/src/gguf.cpp

@@ -777,10 +777,14 @@ enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int64_t key_id
 
 const void * gguf_get_arr_data(const struct gguf_context * ctx, int64_t key_id) {
     GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
-    GGML_ASSERT(ctx->kv[key_id].get_type() != GGUF_TYPE_STRING);
     return ctx->kv[key_id].data.data();
 }
 
+size_t gguf_get_arr_data_n(const struct gguf_context * ctx, int64_t key_id) {
+    GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
+    return ctx->kv[key_id].data.size();
+}
+
 const char * gguf_get_arr_str(const struct gguf_context * ctx, int64_t key_id, size_t i) {
     GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
     GGML_ASSERT(ctx->kv[key_id].get_type() == GGUF_TYPE_STRING);
@@ -874,7 +878,6 @@ const char * gguf_get_val_str(const struct gguf_context * ctx, int64_t key_id) {
 const void * gguf_get_val_data(const struct gguf_context * ctx, int64_t key_id) {
     GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx));
     GGML_ASSERT(ctx->kv[key_id].get_ne() == 1);
-    GGML_ASSERT(ctx->kv[key_id].get_type() != GGUF_TYPE_STRING);
     return ctx->kv[key_id].data.data();
 }
 

ml/backend/ggml/ggml/src/ollama-debug.c

@@ -0,0 +1,116 @@
+#include <string.h>
+#include <inttypes.h>
+
+#include "ollama-debug.h"
+
+static int mul(int64_t *dims, int ndims) {
+    int result = 1;
+    for (int i = 0; i < ndims; i++) {
+        result *= dims[i];
+    }
+
+    return result;
+}
+
+static void repeat(char c, int n) {
+    for (int i = 0; i < n; i++) {
+        fprintf(stderr, "%c", c);
+    }
+}
+
+static void print_tensor(const void *tensor, void (*cb)(const void *, int),
+                         int shape,
+                         int64_t *dims, int ndims, int stride,
+                         int nitems, int pad) {
+    fprintf(stderr, "[");
+    for (int i = 0; i < dims[0]; i++) {
+        if (i >= nitems && i < dims[0] - nitems) {
+            fprintf(stderr, "... (%" PRIi64 " more), ", dims[0] - 2 * nitems);
+            int skip = dims[0] - 2 * nitems;
+            if (ndims > 1) {
+                stride += mul(dims + 1, ndims - 1) * skip;
+                repeat('\n', ndims - 1);
+                repeat(' ', shape - ndims + 1 + pad);
+            }
+            i += skip - 1;
+        } else if (ndims > 1) {
+            print_tensor(tensor, cb, shape, dims + 1, ndims - 1, stride,
+                         nitems, pad);
+            stride += mul(dims + 1, ndims - 1);
+            if (i < dims[0] - 1) {
+                fprintf(stderr, ", ");
+                repeat('\n', ndims - 1);
+                repeat(' ', shape - ndims + 1 + pad);
+            }
+        } else {
+            cb(tensor, stride + i);
+            if (i < dims[0] - 1) {
+                fprintf(stderr, ", ");
+            }
+        }
+    }
+    fprintf(stderr, "]");
+}
+
+static void print_tensor_f16(const void *tensor, int i) {
+    float value = ggml_fp16_to_fp32(((const ggml_fp16_t *)tensor)[i]);
+    fprintf(stderr, "%s%f", value < 0 ? "" : " ", value);
+}
+
+static void print_tensor_f32(const void *tensor, int i) {
+    float value = ((const float *)tensor)[i];
+    fprintf(stderr, "%s%f", value < 0 ? "" : " ", value);
+}
+
+static void print_tensor_i32(const void *tensor, int i) {
+    int32_t value = ((const int32_t *)tensor)[i];
+    fprintf(stderr, "%s%d", value < 0 ? "" : " ", value);
+}
+
+static void ollama_debug_tensor(const struct ggml_tensor *tensor, bool verbose, const char *prefix, int indent) {
+    fprintf(stderr, "%s%s %s (%s): [%" PRIi64 " %" PRIi64 " %" PRIi64 " %" PRIi64 "]\n", prefix, tensor->name,
+            ggml_op_name(tensor->op), ggml_type_name(tensor->type), tensor->ne[0],
+            tensor->ne[1], tensor->ne[2], tensor->ne[3]);
+
+    if (!verbose) {
+        return;
+    }
+
+    for (int i = 0; i < indent; i++) {
+        fprintf(stderr, " ");
+    }
+
+    switch (tensor->type) {
+    case GGML_TYPE_F16:
+        print_tensor(ggml_get_data(tensor), print_tensor_f16, ggml_n_dims(tensor),
+                     (int64_t *)tensor->ne, ggml_n_dims(tensor), 0, 3, indent);
+        break;
+    case GGML_TYPE_F32:
+        print_tensor(ggml_get_data(tensor), print_tensor_f32, ggml_n_dims(tensor),
+                     (int64_t *)tensor->ne, ggml_n_dims(tensor), 0, 3, indent);
+        break;
+    case GGML_TYPE_I32:
+        print_tensor(ggml_get_data(tensor), print_tensor_i32, ggml_n_dims(tensor),
+                     (int64_t *)tensor->ne, ggml_n_dims(tensor), 0, 3, indent);
+        break;
+    default:
+        fprintf(stderr, "<unsupported type>\n");
+        return;
+    }
+
+    fprintf(stderr, "\n");
+}
+
+void ollama_debug(const struct ggml_tensor *tensor, bool verbose) {
+    ollama_debug_tensor(tensor, verbose, ">>> ", 4);
+
+    for (int i = 0; i < GGML_MAX_SRC && tensor->src[i] != NULL; ++i) {
+        char src[8];
+        const int n = snprintf(src, sizeof(src), " src%d ", i);
+        if (n >= sizeof(src)) {
+            src[sizeof(src) - 1] = '\0';
+        }
+
+        ollama_debug_tensor(tensor->src[i], verbose, src, 4);
+    }
+}

ml/backend/ggml/threads.go

@@ -0,0 +1,7 @@
+//go:build !debug
+
+package ggml
+
+func Threads(n int) int {
+	return n
+}

ml/backend/ggml/threads_debug.go

@@ -0,0 +1,7 @@
+//go:build debug
+
+package ggml
+
+func Threads(_ int) int {
+	return 1
+}

ml/nn/attention.go

@@ -3,6 +3,7 @@ package nn
 import (
 	"fmt"
 
+	"github.com/ollama/ollama/kvcache"
 	"github.com/ollama/ollama/ml"
 )
 
@@ -11,40 +12,50 @@ import (
 //
 // Parameters:
 //   - ctx: Context for tensor operations
-//   - query: Query tensor (Q) with shape [d_k, seq_len_q, heads]
-//   - key: Key tensor (K) with shape [d_k, seq_len_k, kv_heads]
-//   - value: Value tensor (V) with shape [seq_len_k, d_v, kv_heads]
-//   - mask: Optional attention mask that is added to the attention score. If
-//     provided, should broadcast to [seq_len_k, seq_len_q, heads]
+//   - query: Query tensor (Q) with shape [d_k, heads, seq_len_q]
+//   - key: Key tensor (K) with shape [d_k, kv_heads, seq_len_k], can be nil to read from cache only
+//   - value: Value tensor (V) with shape [d_v, kv_heads, seq_len_k], can be nil to read from cache only
 //   - scale: Scaling factor, typically 1/√d_k where d_k is the key dimension
+//   - cache: KV cache to store key/value and get past history, can be nil to only use provided key/value
 //
 // Returns:
 //
 //	Attention output with shape [d_v, heads, seq_len_q]
-func Attention(ctx ml.Context, query, key, value, mask ml.Tensor, scale float64) ml.Tensor {
-	if query.Dim(0) != key.Dim(0) {
-		panic(fmt.Errorf("d_k in attention operation does not match between query(%v) and key(%v)", query.Dim(0), key.Dim(0)))
+func Attention(ctx ml.Context, query, key, value ml.Tensor, scale float64, cache kvcache.Cache) ml.Tensor {
+	if key != nil && value != nil {
+		if query.Dim(0) != key.Dim(0) {
+			panic(fmt.Errorf("d_k in attention operation does not match between query(%v) and key(%v)", query.Dim(0), key.Dim(0)))
+		}
+
+		if key.Dim(1) != value.Dim(1) {
+			panic(fmt.Errorf("kv_heads in attention operation does not match between key(%v) and value(%v)", key.Dim(1), value.Dim(1)))
+		}
+
+		if key.Dim(2) != value.Dim(2) {
+			panic(fmt.Errorf("seq_len_k in attention operation does not match between key(%v) and value(%v)", key.Dim(2), value.Dim(2)))
+		}
+
+		if cache != nil {
+			cache.Put(ctx, key, value)
+		}
+	} else if cache == nil {
+		panic("key & value tensors must be provided if cache is nil")
 	}
 
-	if mask != nil && query.Dim(1) != mask.Dim(1) {
-		panic(fmt.Errorf("seq_len_q in attention operation does not match between query(%v) and mask(%v)", query.Dim(1), mask.Dim(1)))
+	var mask ml.Tensor
+	if cache != nil {
+		key, value, mask = cache.Get(ctx)
 	}
 
-	if key.Dim(1) != value.Dim(0) {
-		panic(fmt.Errorf("seq_len_k in attention operation does not match between key(%v) and value(%v)", key.Dim(1), value.Dim(0)))
-	}
-
-	if mask != nil && key.Dim(1) != mask.Dim(0) {
-		panic(fmt.Errorf("seq_len_k in attention operation does not match between key(%v) and mask(%v)", key.Dim(1), mask.Dim(0)))
-	}
-
-	if key.Dim(2) != value.Dim(2) {
-		panic(fmt.Errorf("kv_heads in attention operation does not match between key(%v) and value(%v)", key.Dim(2), value.Dim(2)))
-	}
-
-	if sdpa, ok := query.(ml.ScaledDotProductAttention); ok {
+	// Only use the fast SDPA implementation if we have a cache, since that's what
+	// will do any expected backend-specific transformations for us
+	if sdpa, ok := query.(ml.ScaledDotProductAttention); ok && cache != nil {
 		return sdpa.ScaledDotProductAttention(ctx, key, value, mask, scale)
 	} else {
+		query = query.Permute(ctx, 0, 2, 1, 3)
+		key = key.Permute(ctx, 0, 2, 1, 3)
+		value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+
 		kq := key.MulmatFullPrec(ctx, query)
 
 		kq = kq.Scale(ctx, scale)

model/input/input.go

@@ -0,0 +1,58 @@
+package input
+
+import "github.com/ollama/ollama/ml"
+
+// Input represents one token in the input stream
+type Input struct {
+	// Token is a single element of text.
+	Token int32
+
+	// Multimodal is opaque data representing a non-text
+	// element such as an image (or part of one if the image
+	// can be processed in pieces). It may be either together
+	// with Token or on its own.
+	Multimodal any
+
+	// MultimodalHash is a unique representation of the data
+	// stored in Multimodal, used for caching and comparing
+	// equality.
+	MultimodalHash uint64
+
+	// SameBatch forces the following number of tokens to be processed
+	// in a single batch, breaking and extending batches as needed.
+	// Useful for things like images that must be processed in one
+	// shot.
+	SameBatch int
+}
+
+// MultimodalIndex is a multimodal element (such as an image)
+// together with an index into the slice of Inputs with the
+// corresponding token. Note that the index is not the same
+// as the position - to find that use the index with the
+// Positions slice.
+type MultimodalIndex struct {
+	Index      int
+	Multimodal any
+}
+
+// Batch contains the inputs for a model forward pass
+type Batch struct {
+	// Inputs is the input tokens, including placeholders for multimodal inputs.
+	Inputs ml.Tensor
+
+	// Multimodal is a set of multimodal embeddings previously created by
+	// EncodeMultimodal, along with an index into Inputs. Unused for text-only
+	// models or for batches without multimodal elements.
+	Multimodal []MultimodalIndex
+
+	// Positions is the position for each Input, relative to its sequence. Equal
+	// in length to Inputs.
+	Positions []int32
+
+	// Sequences is the sequence for each Input. Equal in length to Inputs.
+	Sequences []int
+
+	// Outputs are the set of indicies into Inputs for which output data should
+	// be returned.
+	Outputs []int32
+}

model/model.go

@@ -1,9 +1,9 @@
 package model
 
 import (
+	"context"
 	"errors"
 	"fmt"
-	"image"
 	_ "image/jpeg"
 	_ "image/png"
 	"log/slog"
@@ -16,23 +16,52 @@ import (
 	_ "golang.org/x/image/tiff"
 	_ "golang.org/x/image/webp"
 
+	fs "github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/kvcache"
 	"github.com/ollama/ollama/ml"
 	_ "github.com/ollama/ollama/ml/backend"
+	"github.com/ollama/ollama/model/input"
 )
 
-// Options contains the inputs for a model forward pass
-type Options struct {
-	Inputs    []int32
-	Positions []int32
-	Sequences []int
-	Outputs   []int32
+var ErrNoVisionModel = errors.New("this model is missing data required for image input")
 
-	Images []image.Image
+// Model implements a specific model architecture, defining the forward pass and any model-specific configuration
+type Model interface {
+	Forward(ml.Context, input.Batch) (ml.Tensor, error)
+
+	Backend() ml.Backend
+	Config() config
 }
 
-type config struct {
-	Cache kvcache.Cache
+// MultimodalProcessor must be implemented by multimodal models.
+type MultimodalProcessor interface {
+	// EncodeMultimodal processes a single input (such as an image) and
+	// generates an output (typically an embedding) that can be used by the model.
+	//
+	// The return value is most typically an ml.Tensor, however, different
+	// type are possible, such as an object containing a tensor plus
+	// additional metadata, a slice of tensors or even just the original input.
+	//
+	// The result may be cached by the runner.
+	EncodeMultimodal(ml.Context, []byte) (any, error)
+
+	// PostTokenize is called after tokenization to allow the model to edit the
+	// input stream to correctly arrange multimodal elements.
+	//
+	// The input is a slice of tokens with the results of EncodeMultimodal interleaved
+	// in the order that the user provided them. Each element of the slice will be
+	// either a single token or single multimodal object.
+	//
+	// The model must ensure that inputs are stored according to how they will be
+	// processed and stored in the cache. For example, Llava-style models should insert
+	// placeholder tokens equal to the feature size of the corresponding image with
+	// the image itself attached to and split across these tokens. When Forward is called
+	// a partial subset of these tokens may be submitted according to the batch size.
+	//
+	// This function is also responsible for updating MultimodalHash for any Multimodal
+	// that is modified to ensure that there is a unique hash value that accurately
+	// represents the contents.
+	PostTokenize([]input.Input) ([]input.Input, error)
 }
 
 // Base implements the common fields and methods for all models
@@ -41,6 +70,10 @@ type Base struct {
 	config
 }
 
+type config struct {
+	Cache kvcache.Cache
+}
+
 // Backend returns the underlying backend that will run the model
 func (m *Base) Backend() ml.Backend {
 	return m.b
@@ -50,14 +83,6 @@ func (m *Base) Config() config {
 	return m.config
 }
 
-// Model implements a specific model architecture, defining the forward pass and any model-specific configuration
-type Model interface {
-	Forward(ml.Context, Options) (ml.Tensor, error)
-
-	Backend() ml.Backend
-	Config() config
-}
-
 var models = make(map[string]func(ml.Config) (Model, error))
 
 // Register registers a model constructor for the given architecture
@@ -70,14 +95,14 @@ func Register(name string, f func(ml.Config) (Model, error)) {
 }
 
 // New initializes a new model instance with the provided configuration based on the metadata in the model file
-func New(modelPath string, params ml.BackendParams) (Model, error) {
+func New(ctx context.Context, modelPath string, params ml.BackendParams) (Model, error) {
 	r, err := os.Open(modelPath)
 	if err != nil {
 		return nil, err
 	}
 	defer r.Close()
 
-	b, err := ml.NewBackend(r, params)
+	b, err := ml.NewBackend(ctx, r, params)
 	if err != nil {
 		return nil, err
 	}
@@ -100,6 +125,36 @@ func New(modelPath string, params ml.BackendParams) (Model, error) {
 	return m, nil
 }
 
+func NewTextProcessor(s string) (TextProcessor, error) {
+	r, err := os.Open(s)
+	if err != nil {
+		return nil, err
+	}
+	defer r.Close()
+	meta, _, err := fs.Decode(r, -1)
+	if err != nil {
+		return nil, err
+	}
+	return getTextProcessor(meta.KV())
+}
+
+func getTextProcessor(kv fs.KV) (TextProcessor, error) {
+	arch := kv.Architecture()
+	f, ok := models[arch]
+	if !ok {
+		return nil, fmt.Errorf("unsupported model architecture %q", arch)
+	}
+	m, err := f(kv)
+	if err != nil {
+		return nil, err
+	}
+	tp, ok := m.(TextProcessor)
+	if !ok {
+		return nil, fmt.Errorf("%v is not a TextProcessor", m)
+	}
+	return tp, nil
+}
+
 func populateFields(base Base, v reflect.Value, tags ...Tag) reflect.Value {
 	t := v.Type()
 
@@ -226,30 +281,35 @@ func canNil(t reflect.Type) bool {
 		t.Kind() == reflect.Slice
 }
 
-func Forward(ctx ml.Context, m Model, opts Options) (ml.Tensor, error) {
-	if len(opts.Positions) != len(opts.Sequences) {
-		return nil, fmt.Errorf("length of positions (%v) must match length of seqs (%v)", len(opts.Positions), len(opts.Sequences))
+func Forward(ctx ml.Context, m Model, inputs []int32, batch input.Batch) (ml.Tensor, error) {
+	if len(batch.Positions) != len(batch.Sequences) {
+		return nil, fmt.Errorf("length of positions (%v) must match length of seqs (%v)", len(batch.Positions), len(batch.Sequences))
 	}
 
-	if len(opts.Positions) < 1 {
+	if len(batch.Positions) < 1 {
 		return nil, errors.New("batch size cannot be less than 1")
 	}
 
+	var err error
+	batch.Inputs, err = ctx.Input().FromIntSlice(inputs, len(inputs))
+	if err != nil {
+		return nil, err
+	}
+
 	cache := m.Config().Cache
 	if cache != nil {
-		err := cache.StartForward(ctx, opts.Positions, opts.Sequences)
+		err := cache.StartForward(ctx, batch)
 		if err != nil {
 			return nil, err
 		}
 	}
 
-	t, err := m.Forward(ctx, opts)
+	t, err := m.Forward(ctx, batch)
 	if err != nil {
 		return nil, err
 	}
 
-	ctx.Forward(t)
-	ctx.Compute(t)
+	ctx.Forward(t).Compute(t)
 
 	return t, nil
 }

model/model_test.go

@@ -3,12 +3,15 @@ package model
 import (
 	"reflect"
 	"slices"
+	"strings"
 	"testing"
 
 	"github.com/google/go-cmp/cmp"
+	fs "github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/ml/backend/ggml"
 	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model/input"
 )
 
 func TestParseTags(t *testing.T) {
@@ -134,3 +137,40 @@ func TestPopulateFieldsAlternateName(t *testing.T) {
 		t.Errorf("populateFields() set incorrect values (-want +got):\n%s", diff)
 	}
 }
+
+func TestGetTextProcessor(t *testing.T) {
+	tp, err := getTextProcessor(fs.KV{})
+	if err == nil {
+		t.Error("expected error")
+	} else if !strings.Contains(err.Error(), "unsupported model architecture") {
+		t.Errorf("unexpected error: %v", err)
+	} else if tp != nil {
+		t.Error("expected nil tp")
+	}
+
+	models["dummy"] = func(ml.Config) (Model, error) {
+		return notTextProcessorModel{}, nil
+	}
+	tp, err = getTextProcessor(fs.KV{"general.architecture": "dummy"})
+	if err == nil {
+		t.Error("expected error")
+	} else if !strings.Contains(err.Error(), "not a TextProcessor") {
+		t.Errorf("unexpected error: %v", err)
+	} else if tp != nil {
+		t.Error("expected nil tp")
+	}
+}
+
+type notTextProcessorModel struct{}
+
+func (notTextProcessorModel) Forward(ml.Context, input.Batch) (ml.Tensor, error) {
+	panic("unimplemented")
+}
+
+func (notTextProcessorModel) Backend() ml.Backend {
+	panic("unimplemented")
+}
+
+func (notTextProcessorModel) Config() config {
+	panic("unimplemented")
+}

model/models/gemma2/model.go

@@ -0,0 +1,214 @@
+package gemma2
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Options struct {
+	hiddenSize, numHeads, numKVHeads int
+	attnKeyLen, attnValLen           int
+	eps, ropeBase, ropeScale         float32
+	attnLogitSoftcap                 float32
+	finalLogitSoftcap                float32
+	largeModelScaling                bool
+}
+
+type Model struct {
+	model.Base
+	model.SentencePieceModel
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Layers         []Layer       `gguf:"blk"`
+	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
+	Output         *nn.Linear    `gguf:"output,alt:token_embd"` // just set to token_embd?
+
+	*Options
+}
+
+const (
+	gemma27BLayerCount = 46
+)
+
+func New(c ml.Config) (model.Model, error) {
+	m := Model{
+		SentencePieceModel: model.NewSentencePieceModel(
+			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Scores: c.Floats("tokenizer.ggml.scores"),
+				Types:  c.Uints("tokenizer.ggml.token_type"),
+				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
+				EOS:    int32(c.Uint("tokenizer.ggml.eos_token_id")),
+			},
+		),
+		Layers: make([]Layer, c.Uint("block_count")),
+		Options: &Options{
+			hiddenSize:        int(c.Uint("embedding_length")),
+			numHeads:          int(c.Uint("attention.head_count")),
+			numKVHeads:        int(c.Uint("attention.head_count_kv")),
+			attnKeyLen:        int(c.Uint("attention.key_length")),
+			attnValLen:        int(c.Uint("attention.value_length")),
+			eps:               c.Float("attention.layer_norm_rms_epsilon"),
+			ropeBase:          c.Float("rope.freq_base", 10000.0),
+			ropeScale:         c.Float("rope.freq_scale", 1.0),
+			attnLogitSoftcap:  c.Float("attn_logit_softcapping"),
+			finalLogitSoftcap: c.Float("final_logit_softcapping"),
+		},
+	}
+
+	slidingWindowLen := int32(c.Uint("attention.sliding_window"))
+	m.Cache = kvcache.NewWrapperCache(kvcache.NewSWACache(slidingWindowLen, m.Shift), kvcache.NewCausalCache(m.Shift))
+	m.Cache.SetConfig(ml.CacheConfig{})
+
+	return &m, nil
+}
+
+type SelfAttention struct {
+	Query  *nn.Linear `gguf:"attn_q"`
+	Key    *nn.Linear `gguf:"attn_k"`
+	Value  *nn.Linear `gguf:"attn_v"`
+	Output *nn.Linear `gguf:"attn_output"`
+}
+
+func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	batchSize := hiddenState.Dim(1)
+	ropeType := uint32(2)
+
+	q := sa.Query.Forward(ctx, hiddenState)
+	q = q.Reshape(ctx, opts.attnKeyLen, opts.numHeads, batchSize)
+	q = q.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, opts.ropeBase, opts.ropeScale)
+
+	if opts.largeModelScaling {
+		q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.hiddenSize/opts.numHeads)))
+	} else {
+		q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.attnKeyLen)))
+	}
+
+	k := sa.Key.Forward(ctx, hiddenState)
+	k = k.Reshape(ctx, opts.attnKeyLen, opts.numKVHeads, batchSize)
+	k = k.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, opts.ropeBase, opts.ropeScale)
+
+	v := sa.Value.Forward(ctx, hiddenState)
+	v = v.Reshape(ctx, opts.attnValLen, opts.numKVHeads, batchSize)
+
+	cache.Put(ctx, k, v)
+	k, v, mask := cache.Get(ctx)
+
+	q = q.Permute(ctx, 0, 2, 1, 3)
+	k = k.Permute(ctx, 0, 2, 1, 3)
+	v = v.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+
+	kq := k.Mulmat(ctx, q)
+
+	// logit softcap
+	kq = kq.Scale(ctx, 1.0/float64(opts.attnLogitSoftcap))
+	kq = kq.Tanh(ctx)
+	kq = kq.Scale(ctx, float64(opts.attnLogitSoftcap))
+
+	kq = kq.Add(ctx, mask)
+	kq = kq.Softmax(ctx)
+
+	kqv := v.Mulmat(ctx, kq)
+	kqv = kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	kqv = kqv.Reshape(ctx, opts.attnValLen*opts.numHeads, batchSize)
+
+	return sa.Output.Forward(ctx, kqv)
+}
+
+func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return key.RoPE(ctx, shift, nil, uint32(m.Options.attnKeyLen), uint32(2), m.Options.ropeBase, m.Options.ropeScale), nil
+}
+
+type MLP struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+	Gate *nn.Linear `gguf:"ffn_gate"`
+}
+
+func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	return mlp.Down.Forward(ctx, hiddenState)
+}
+
+type Layer struct {
+	AttentionNorm     *nn.RMSNorm `gguf:"attn_norm"`
+	SelfAttention     *SelfAttention
+	PostAttentionNorm *nn.RMSNorm `gguf:"post_attention_norm"`
+	MLPNorm           *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP               *MLP
+	PostMLPNorm       *nn.RMSNorm `gguf:"post_ffw_norm"`
+}
+
+func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.SelfAttention.Forward(ctx, hiddenState, positionIDs, cache, opts)
+	hiddenState = l.PostAttentionNorm.Forward(ctx, hiddenState, opts.eps)
+
+	// In the final layer (outputs != nil), optimize by pruning to just the token positions
+	// we need logits for.
+	if outputs != nil {
+		hiddenState = hiddenState.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
+	hiddenState = l.PostMLPNorm.Forward(ctx, hiddenState, opts.eps)
+	return hiddenState.Add(ctx, residual)
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	positions, err := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	if err != nil {
+		return nil, err
+	}
+
+	outputs, err := ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
+	if err != nil {
+		return nil, err
+	}
+
+	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)
+	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.Options.hiddenSize)))
+
+	if len(m.Layers) == gemma27BLayerCount {
+		m.Options.largeModelScaling = true
+	}
+
+	for i, layer := range m.Layers {
+		cacheType := i % 2
+		m.Cache.SetLayer(i)
+		wc := m.Cache.(*kvcache.WrapperCache)
+		wc.SetLayerType(cacheType)
+
+		var lastLayerOutputs ml.Tensor
+		if i == len(m.Layers)-1 {
+			lastLayerOutputs = outputs
+		}
+
+		hiddenState = layer.Forward(ctx, hiddenState, positions, lastLayerOutputs, m.Cache, m.Options)
+	}
+
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	hiddenState = m.Output.Forward(ctx, hiddenState)
+
+	// final logit softcap
+	hiddenState = hiddenState.Scale(ctx, 1.0/float64(m.Options.finalLogitSoftcap))
+	hiddenState = hiddenState.Tanh(ctx)
+	return hiddenState.Scale(ctx, float64(m.Options.finalLogitSoftcap)), nil
+}
+
+func init() {
+	model.Register("gemma2", New)
+}

model/models/gemma3/model.go

@@ -0,0 +1,158 @@
+package gemma3
+
+import (
+	"bytes"
+	"image"
+	"math"
+	"slices"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Model struct {
+	model.Base
+	model.SentencePieceModel
+
+	*VisionModel `gguf:"v,vision"`
+	*TextModel
+
+	*MultiModalProjector `gguf:"mm"`
+
+	ImageProcessor
+}
+
+var _ model.MultimodalProcessor = (*Model)(nil)
+
+type MultiModalProjector struct {
+	SoftEmbNorm     *nn.RMSNorm `gguf:"mm_soft_emb_norm"`
+	InputProjection *nn.Linear  `gguf:"mm_input_projection"`
+
+	tokensPerImage int
+}
+
+func (p *MultiModalProjector) Forward(ctx ml.Context, visionOutputs ml.Tensor, imageSize, patchSize int, eps float32) ml.Tensor {
+	l := visionOutputs.Dim(0)
+
+	visionOutputs = visionOutputs.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
+	patchesPerImage := imageSize / patchSize
+	visionOutputs = visionOutputs.Reshape(ctx, patchesPerImage, patchesPerImage, l)
+
+	kernelSize := patchesPerImage / int(math.Sqrt(float64(p.tokensPerImage)))
+	visionOutputs = visionOutputs.AvgPool2D(ctx, kernelSize, kernelSize, 0)
+	visionOutputs = visionOutputs.Reshape(ctx, visionOutputs.Dim(0)*visionOutputs.Dim(1), l)
+	visionOutputs = visionOutputs.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
+	visionOutputs = p.SoftEmbNorm.Forward(ctx, visionOutputs, eps)
+
+	// TODO: inputProjection must be transposed since they're incompatible with visionOutputs
+	visionOutputs = p.InputProjection.Weight.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx).Mulmat(ctx, visionOutputs)
+	return visionOutputs
+}
+
+func New(c ml.Config) (model.Model, error) {
+	m := Model{
+		SentencePieceModel: model.NewSentencePieceModel(
+			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Scores: c.Floats("tokenizer.ggml.scores"),
+				Types:  c.Uints("tokenizer.ggml.token_type"),
+				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				EOS:    int32(1),
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+				EOT:    int32(106),
+				AddEOT: c.Bool("tokenizer.ggml.add_eot_token", false),
+			},
+		),
+		ImageProcessor: newImageProcessor(c),
+		VisionModel:    newVisionModel(c),
+		TextModel:      newTextModel(c),
+		MultiModalProjector: &MultiModalProjector{
+			tokensPerImage: int(c.Uint("mm_tokens_per_image", 256)),
+		},
+	}
+
+	slidingWindowLen := int32(c.Uint("attention.sliding_window"))
+	m.Cache = kvcache.NewWrapperCache(kvcache.NewSWACache(slidingWindowLen, m.Shift), kvcache.NewCausalCache(m.Shift))
+
+	return &m, nil
+}
+
+func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, error) {
+	if len(m.VisionModel.Layers) == 0 {
+		return nil, model.ErrNoVisionModel
+	}
+
+	image, _, err := image.Decode(bytes.NewReader(multimodalData))
+	if err != nil {
+		return nil, err
+	}
+
+	f32s, err := m.ImageProcessor.ProcessImage(image)
+	if err != nil {
+		return nil, err
+	}
+
+	pixelValues, err := ctx.Input().FromFloatSlice(f32s,
+		m.ImageProcessor.imageSize,
+		m.ImageProcessor.imageSize,
+		m.ImageProcessor.numChannels,
+	)
+	if err != nil {
+		return nil, err
+	}
+
+	visionOutputs := m.VisionModel.Forward(ctx, pixelValues)
+	visionOutputs = m.MultiModalProjector.Forward(ctx, visionOutputs, m.imageSize, m.patchSize, m.VisionModel.eps)
+	return visionOutputs, nil
+}
+
+func (m *Model) PostTokenize(inputs []input.Input) ([]input.Input, error) {
+	var result []input.Input
+
+	for _, inp := range inputs {
+		if inp.Multimodal == nil {
+			result = append(result, inp)
+		} else {
+			inputMultimodal := inp.Multimodal.(ml.Tensor)
+
+			result = append(result,
+				input.Input{Token: 108, SameBatch: inputMultimodal.Dim(1) + 3},               // "\n\n"
+				input.Input{Token: 255999},                                                   // "<start_of_image>""
+				input.Input{Multimodal: inputMultimodal, MultimodalHash: inp.MultimodalHash}, // image data is on the first placeholder
+			)
+
+			// add image token placeholders
+			result = append(result, slices.Repeat([]input.Input{{Token: 0}}, inputMultimodal.Dim(1)-1)...)
+
+			result = append(result,
+				input.Input{Token: 256000}, // <end_of_image>
+				input.Input{Token: 108},    // "\n\n"
+			)
+		}
+	}
+
+	return result, nil
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	positions, err := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	if err != nil {
+		return nil, err
+	}
+
+	outputs, err := ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
+	if err != nil {
+		return nil, err
+	}
+
+	return m.TextModel.Forward(ctx, batch.Inputs, positions, outputs, batch, m.Cache), nil
+}
+
+func init() {
+	model.Register("gemma3", New)
+}

model/models/gemma3/model_text.go

@@ -0,0 +1,214 @@
+package gemma3
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type TextOptions struct {
+	hiddenSize, numHeads, numKVHeads int
+	attnKeyLen, attnValLen           int
+	eps, ropeScale                   float32
+	ropeLocalBase, ropeGlobalBase    float32
+	largeModelScaling                bool
+}
+
+type TextModel struct {
+	model.Base
+	model.SentencePieceModel
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Layers         []TextLayer   `gguf:"blk"`
+	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
+	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
+
+	*TextOptions
+}
+
+const (
+	gemmaGlobalCacheCount = 6
+	gemma27BLayerCount    = 62
+)
+
+const (
+	cacheTypeSWA = iota
+	cacheTypeCausal
+)
+
+func newTextModel(c ml.Config) *TextModel {
+	numBlocks := int(c.Uint("block_count"))
+
+	m := TextModel{
+		SentencePieceModel: model.NewSentencePieceModel(
+			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Scores: c.Floats("tokenizer.ggml.scores"),
+				Types:  c.Uints("tokenizer.ggml.token_type"),
+				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
+				EOS:    int32(c.Uint("tokenizer.ggml.eos_token_id")),
+			},
+		),
+		Layers: make([]TextLayer, numBlocks),
+		TextOptions: &TextOptions{
+			hiddenSize:     int(c.Uint("embedding_length")),
+			numHeads:       int(c.Uint("attention.head_count")),
+			numKVHeads:     int(c.Uint("attention.head_count_kv")),
+			attnKeyLen:     int(c.Uint("attention.key_length", 256)),
+			attnValLen:     int(c.Uint("attention.value_length", 256)),
+			eps:            c.Float("attention.layer_norm_rms_epsilon", 1e-06),
+			ropeLocalBase:  c.Float("rope.local.freq_base", 10000.0),
+			ropeGlobalBase: c.Float("rope.global.freq_base", 1000000.0),
+			ropeScale:      c.Float("rope.freq_scale", 1.0),
+		},
+	}
+
+	if numBlocks == gemma27BLayerCount {
+		m.largeModelScaling = true
+	}
+
+	return &m
+}
+
+type TextSelfAttention struct {
+	Query     *nn.Linear  `gguf:"attn_q"`
+	QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"`
+	Key       *nn.Linear  `gguf:"attn_k"`
+	KeyNorm   *nn.RMSNorm `gguf:"attn_k_norm"`
+	Value     *nn.Linear  `gguf:"attn_v"`
+	Output    *nn.Linear  `gguf:"attn_output"`
+}
+
+func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
+	batchSize := hiddenState.Dim(1)
+	ropeType := uint32(2)
+
+	ropeBase := opts.ropeLocalBase
+	if (layer+1)%gemmaGlobalCacheCount == 0 {
+		ropeBase = opts.ropeGlobalBase
+	}
+
+	q := sa.Query.Forward(ctx, hiddenState)
+	q = q.Reshape(ctx, opts.attnKeyLen, opts.numHeads, batchSize)
+	q = sa.QueryNorm.Forward(ctx, q, opts.eps)
+	q = q.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, ropeBase, opts.ropeScale)
+
+	if opts.largeModelScaling {
+		q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.hiddenSize/opts.numHeads)))
+	} else {
+		q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.attnKeyLen)))
+	}
+
+	k := sa.Key.Forward(ctx, hiddenState)
+	k = k.Reshape(ctx, opts.attnKeyLen, opts.numKVHeads, batchSize)
+	k = sa.KeyNorm.Forward(ctx, k, opts.eps)
+	k = k.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, ropeBase, opts.ropeScale)
+
+	v := sa.Value.Forward(ctx, hiddenState)
+	v = v.Reshape(ctx, opts.attnValLen, opts.numKVHeads, batchSize)
+
+	scaleFactor := 1.0
+	kqv := nn.Attention(ctx, q, k, v, scaleFactor, cache)
+	kqv = kqv.Reshape(ctx, opts.attnValLen*opts.numHeads, batchSize)
+
+	return sa.Output.Forward(ctx, kqv)
+}
+
+func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	ropeBase := m.TextOptions.ropeLocalBase
+	if (layer+1)%gemmaGlobalCacheCount == 0 {
+		ropeBase = m.TextOptions.ropeGlobalBase
+	}
+
+	return key.RoPE(ctx, shift, nil, uint32(m.TextOptions.attnKeyLen), uint32(2), ropeBase, m.TextOptions.ropeScale), nil
+}
+
+type TextMLP struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+	Gate *nn.Linear `gguf:"ffn_gate"`
+}
+
+func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor {
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	return mlp.Down.Forward(ctx, hiddenState)
+}
+
+type TextLayer struct {
+	AttentionNorm     *nn.RMSNorm `gguf:"attn_norm"`
+	SelfAttention     *TextSelfAttention
+	PostAttentionNorm *nn.RMSNorm `gguf:"post_attention_norm"`
+	MLPNorm           *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP               *TextMLP
+	PostMLPNorm       *nn.RMSNorm `gguf:"post_ffw_norm"`
+}
+
+func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.SelfAttention.Forward(ctx, layer, hiddenState, positionIDs, cache, opts)
+	hiddenState = l.PostAttentionNorm.Forward(ctx, hiddenState, opts.eps)
+
+	// In the final layer (outputs != nil), optimize by pruning to just the token positions
+	// we need logits for.
+	if outputs != nil {
+		hiddenState = hiddenState.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
+	hiddenState = l.PostMLPNorm.Forward(ctx, hiddenState, opts.eps)
+	return hiddenState.Add(ctx, residual)
+}
+
+func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, batch input.Batch, cache kvcache.Cache) ml.Tensor {
+	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
+	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.TextOptions.hiddenSize)))
+
+	// set image embeddings
+	var except []int
+	for _, image := range batch.Multimodal {
+		visionOutputs := image.Multimodal.(ml.Tensor)
+		ctx.Forward(visionOutputs.Copy(ctx, hiddenState.View(ctx, image.Index*hiddenState.Stride(1), visionOutputs.Dim(0)*visionOutputs.Dim(1))))
+
+		for i := range visionOutputs.Dim(1) {
+			except = append(except, image.Index+i)
+		}
+	}
+
+	for i, layer := range m.Layers {
+		// gemma alternates between the sliding window (local) and causal (global)
+		// kv cache every 6 layers
+		cacheType := cacheTypeSWA
+		if (i+1)%gemmaGlobalCacheCount == 0 {
+			cacheType = cacheTypeCausal
+		}
+		cache.SetLayer(i)
+		wc := cache.(*kvcache.WrapperCache)
+		wc.SetLayerType(cacheType)
+
+		if causal, ok := wc.UnderlyingCache().(*kvcache.Causal); ok {
+			causal.SetCausal(ctx, kvcache.CausalOptions{Except: except})
+		}
+
+		var lastLayerOutputs ml.Tensor
+		if i == len(m.Layers)-1 {
+			lastLayerOutputs = outputs
+		}
+
+		hiddenState = layer.Forward(ctx, i, hiddenState, positions, lastLayerOutputs, cache, m.TextOptions)
+	}
+
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	return m.Output.Forward(ctx, hiddenState)
+}

model/models/gemma3/model_vision.go

@@ -0,0 +1,127 @@
+package gemma3
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+)
+
+var batchSize int = 1
+
+type VisionSelfAttention struct {
+	Query  *nn.Linear `gguf:"attn_q"`
+	Key    *nn.Linear `gguf:"attn_k"`
+	Value  *nn.Linear `gguf:"attn_v"`
+	Output *nn.Linear `gguf:"attn_output"`
+}
+
+func (sa *VisionSelfAttention) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *VisionModelOptions) ml.Tensor {
+	headDim := opts.hiddenSize / opts.numHeads
+
+	query := sa.Query.Forward(ctx, hiddenState)
+	key := sa.Key.Forward(ctx, hiddenState)
+	value := sa.Value.Forward(ctx, hiddenState)
+
+	query = query.Reshape(ctx, headDim, opts.numHeads, query.Dim(1), batchSize)
+	key = key.Reshape(ctx, headDim, opts.numHeads, key.Dim(1), batchSize)
+	value = value.Reshape(ctx, headDim, opts.numHeads, value.Dim(1), batchSize)
+
+	attention := nn.Attention(ctx, query, key, value, 1.0/math.Sqrt(float64(headDim)), nil)
+	attention = attention.Reshape(ctx, opts.hiddenSize, attention.Dim(2), batchSize)
+
+	hiddenState = sa.Output.Forward(ctx, attention)
+	return hiddenState
+}
+
+type VisionMLP struct {
+	FC1 *nn.Linear `gguf:"fc1"`
+	FC2 *nn.Linear `gguf:"fc2"`
+}
+
+func (mlp *VisionMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *VisionModelOptions) ml.Tensor {
+	hiddenState = mlp.FC1.Forward(ctx, hiddenState).GELU(ctx)
+	hiddenState = mlp.FC2.Forward(ctx, hiddenState)
+	return hiddenState
+}
+
+type VisionEncoderLayer struct {
+	LayerNorm1    *nn.LayerNorm `gguf:"layer_norm1"`
+	SelfAttention *VisionSelfAttention
+
+	LayerNorm2 *nn.LayerNorm `gguf:"layer_norm2"`
+	MLP        *VisionMLP    `gguf:"mlp"`
+}
+
+func (e *VisionEncoderLayer) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *VisionModelOptions) ml.Tensor {
+	residual := hiddenState
+
+	// self attention
+	hiddenState = e.LayerNorm1.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = e.SelfAttention.Forward(ctx, hiddenState, opts)
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	// feed forward
+	hiddenState = e.LayerNorm2.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = e.MLP.Forward(ctx, hiddenState, opts)
+	return hiddenState.Add(ctx, residual)
+}
+
+type VisionModelOptions struct {
+	hiddenSize, numHeads int
+	imageSize, patchSize int
+	eps                  float32
+}
+
+type VisionModel struct {
+	PatchEmbedding    *nn.Conv2D    `gguf:"patch_embedding"`
+	PositionEmbedding *nn.Embedding `gguf:"position_embedding"`
+	PostLayerNorm     *nn.LayerNorm `gguf:"post_layernorm"`
+
+	Layers []VisionEncoderLayer `gguf:"blk"`
+
+	*VisionModelOptions
+}
+
+func (m *VisionModel) Forward(ctx ml.Context, pixelValues ml.Tensor) ml.Tensor {
+	numPatches := (m.imageSize / m.patchSize) * (m.imageSize / m.patchSize)
+
+	hiddenState := m.PatchEmbedding.Forward(ctx, pixelValues, m.patchSize, m.patchSize, 0, 0, 1, 1)
+	hiddenState = hiddenState.Reshape(ctx, numPatches, m.hiddenSize)
+	hiddenState = hiddenState.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
+
+	positions := make([]int32, numPatches)
+	for i := range positions {
+		positions[i] = int32(i)
+	}
+
+	positionIDs, err := ctx.Input().FromIntSlice(positions, len(positions))
+	if err != nil {
+		panic(err)
+	}
+
+	hiddenState = hiddenState.Add(ctx, m.PositionEmbedding.Forward(ctx, positionIDs))
+
+	for _, layer := range m.Layers {
+		hiddenState = layer.Forward(ctx, hiddenState, m.VisionModelOptions)
+	}
+
+	hiddenState = m.PostLayerNorm.Forward(ctx, hiddenState, m.eps)
+	return hiddenState
+}
+
+func newVisionModel(c ml.Config) *VisionModel {
+	return &VisionModel{
+		Layers: make([]VisionEncoderLayer, c.Uint("vision.block_count")),
+		VisionModelOptions: &VisionModelOptions{
+			hiddenSize: int(c.Uint("vision.embedding_length")),
+			numHeads:   int(c.Uint("vision.attention.head_count")),
+
+			imageSize: int(c.Uint("vision.image_size")),
+			patchSize: int(c.Uint("vision.patch_size")),
+
+			eps: c.Float("vision.attention.layer_norm_epsilon"),
+		},
+	}
+}

model/models/gemma3/process_image.go

@@ -0,0 +1,58 @@
+package gemma3
+
+import (
+	"image"
+
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model/imageproc"
+)
+
+type ImageProcessor struct {
+	imageSize, patchSize, numChannels int
+}
+
+func newImageProcessor(c ml.Config) ImageProcessor {
+	return ImageProcessor{
+		imageSize:   int(c.Uint("vision.image_size")),
+		patchSize:   int(c.Uint("vision.patch_size")),
+		numChannels: int(c.Uint("vision.num_channels")),
+	}
+}
+
+func (p *ImageProcessor) pack(img image.Image, mean, std [3]float32) []float32 {
+	var pixelVals, rVals, gVals, bVals []float32
+
+	bounds := img.Bounds()
+	for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
+		for x := bounds.Min.X; x < bounds.Max.X; x++ {
+			c := img.At(x, y)
+			r, g, b, _ := c.RGBA()
+			rVal := float32(r>>8) / 255.0
+			gVal := float32(g>>8) / 255.0
+			bVal := float32(b>>8) / 255.0
+
+			rVal = (rVal - mean[0]) / std[0]
+			gVal = (gVal - mean[1]) / std[1]
+			bVal = (bVal - mean[2]) / std[2]
+
+			rVals = append(rVals, rVal)
+			gVals = append(gVals, gVal)
+			bVals = append(bVals, bVal)
+		}
+	}
+
+	pixelVals = append(pixelVals, rVals...)
+	pixelVals = append(pixelVals, gVals...)
+	pixelVals = append(pixelVals, bVals...)
+
+	return pixelVals
+}
+
+func (p ImageProcessor) ProcessImage(img image.Image) ([]float32, error) {
+	outputSize := image.Point{p.imageSize, p.imageSize}
+	newImage := imageproc.Composite(img)
+	newImage = imageproc.Resize(newImage, outputSize, imageproc.ResizeBilinear)
+
+	data := p.pack(newImage, imageproc.ImageNetStandardMean, imageproc.ImageNetStandardSTD)
+	return data, nil
+}

model/models/llama/model.go

@@ -1,16 +1,18 @@
 package llama
 
 import (
+	"fmt"
 	"math"
+	"strings"
 
 	"github.com/ollama/ollama/kvcache"
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/ml/nn"
 	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
 )
 
 type Options struct {
-	RopeFactors                      ml.Tensor `gguf:"rope_freqs.weight"`
 	hiddenSize, numHeads, numKVHeads int
 	eps, ropeBase, ropeScale         float32
 	ropeDim                          uint32
@@ -29,6 +31,10 @@ type Model struct {
 }
 
 func New(c ml.Config) (model.Model, error) {
+	if !strings.EqualFold(c.String("tokenizer.ggml.model"), "gpt2") {
+		return nil, fmt.Errorf("tokenizer %s not yet supported", c.String("tokenizer.ggml.model"))
+	}
+
 	m := Model{
 		BytePairEncoding: model.NewBytePairEncoding(
 			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
@@ -37,7 +43,9 @@ func New(c ml.Config) (model.Model, error) {
 				Types:  c.Uints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),
 				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
 				EOS:    int32(c.Uint("tokenizer.ggml.eos_token_id")),
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
 			},
 		),
 		Layers: make([]Layer, c.Uint("block_count")),
@@ -58,43 +66,38 @@ func New(c ml.Config) (model.Model, error) {
 }
 
 type SelfAttention struct {
-	Query  *nn.Linear `gguf:"attn_q"`
-	Key    *nn.Linear `gguf:"attn_k"`
-	Value  *nn.Linear `gguf:"attn_v"`
-	Output *nn.Linear `gguf:"attn_output"`
+	Query       *nn.Linear `gguf:"attn_q"`
+	Key         *nn.Linear `gguf:"attn_k"`
+	Value       *nn.Linear `gguf:"attn_v"`
+	Output      *nn.Linear `gguf:"attn_output"`
+	RopeFactors ml.Tensor  `gguf:"rope_freqs.weight"`
 }
 
 func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
 	batchSize := hiddenState.Dim(1)
 	headDim := opts.hiddenSize / opts.numHeads
+	ropeType := uint32(0)
 
 	q := sa.Query.Forward(ctx, hiddenState)
 	q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
-	q = q.RoPE(ctx, positionIDs, opts.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+	q = q.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
 
 	k := sa.Key.Forward(ctx, hiddenState)
 	k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
-	k = k.RoPE(ctx, positionIDs, opts.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+	k = k.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
 
 	v := sa.Value.Forward(ctx, hiddenState)
 	v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
 
-	cache.Put(ctx, k, v)
-	k, v, mask := cache.Get(ctx)
-
-	q = q.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
-	k = k.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
-	v = v.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
-
 	scaleFactor := 1.0 / math.Sqrt(float64(headDim))
-	kqv := nn.Attention(ctx, q, k, v, mask, scaleFactor)
+	kqv := nn.Attention(ctx, q, k, v, scaleFactor, cache)
 	kqv = kqv.Reshape(ctx, opts.hiddenSize, batchSize)
 
 	return sa.Output.Forward(ctx, kqv)
 }
 
 func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-	return key.RoPE(ctx, shift, m.Options.RopeFactors, m.Options.ropeDim, m.Options.ropeBase, m.Options.ropeScale), nil
+	return key.RoPE(ctx, shift, m.Layers[layer].SelfAttention.RopeFactors, uint32(0), m.ropeDim, m.ropeBase, m.ropeScale), nil
 }
 
 type MLP struct {
@@ -136,23 +139,18 @@ func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Ten
 	return hiddenState.Add(ctx, residual)
 }
 
-func (m *Model) Forward(ctx ml.Context, opts model.Options) (ml.Tensor, error) {
-	inputs, err := ctx.FromIntSlice(opts.Inputs, len(opts.Inputs))
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	positions, err := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
 	if err != nil {
 		return nil, err
 	}
 
-	positions, err := ctx.FromIntSlice(opts.Positions, len(opts.Positions))
+	outputs, err := ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
 	if err != nil {
 		return nil, err
 	}
 
-	outputs, err := ctx.FromIntSlice(opts.Outputs, len(opts.Outputs))
-	if err != nil {
-		return nil, err
-	}
-
-	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
+	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 
 	for i, layer := range m.Layers {
 		m.Cache.SetLayer(i)

model/models/mllama/model.go

@@ -1,10 +1,18 @@
 package mllama
 
 import (
+	"bytes"
+	"encoding/binary"
+	"fmt"
+	"hash/fnv"
+	"image"
+	"slices"
+
 	"github.com/ollama/ollama/kvcache"
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/ml/nn"
 	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
 )
 
 type Model struct {
@@ -25,6 +33,10 @@ const (
 )
 
 func New(c ml.Config) (model.Model, error) {
+	// Verify unified config
+	if c.Uint("vision.block_count") == 0 {
+		return nil, fmt.Errorf("non-unified vision model not supported")
+	}
 	m := Model{
 		BytePairEncoding: model.NewBytePairEncoding(
 			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
@@ -33,7 +45,9 @@ func New(c ml.Config) (model.Model, error) {
 				Types:  c.Uints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),
 				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
 				EOS:    int32(c.Uint("tokenizer.ggml.eos_token_id")),
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
 			},
 		),
 		ImageProcessor: newImageProcessor(c),
@@ -41,65 +55,107 @@ func New(c ml.Config) (model.Model, error) {
 		TextModel:      newTextModel(c),
 	}
 
-	m.Cache = kvcache.NewWrapperCache(kvcache.NewEncoderCache(), kvcache.NewCausalCache(m.TextModel.Shift))
+	encoderCache := kvcache.NewEncoderCache()
+	encoderCache.SetConfig(ml.CacheConfig{})
+	m.Cache = kvcache.NewWrapperCache(encoderCache, kvcache.NewCausalCache(m.TextModel.Shift))
 
 	return &m, nil
 }
 
-func (m *Model) Forward(ctx ml.Context, opts model.Options) (ml.Tensor, error) {
+func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, error) {
+	if len(m.VisionModel.Transformer.Layers) == 0 || len(m.GlobalTransformer.Layers) == 0 {
+		return nil, model.ErrNoVisionModel
+	}
+
+	image, _, err := image.Decode(bytes.NewReader(multimodalData))
+	if err != nil {
+		return nil, err
+	}
+
+	f32s, aspectRatioID, err := m.ImageProcessor.ProcessImage(image)
+	if err != nil {
+		return nil, err
+	}
+
+	pixelValues, err := ctx.Input().FromFloatSlice(f32s,
+		m.ImageProcessor.imageSize,
+		m.ImageProcessor.imageSize,
+		m.ImageProcessor.numChannels,
+		m.ImageProcessor.maxNumTiles,
+	)
+	if err != nil {
+		return nil, err
+	}
+
+	aspectRatio, err := ctx.Input().FromIntSlice([]int32{int32(aspectRatioID)}, 1)
+	if err != nil {
+		return nil, err
+	}
+
+	positions := make([]int32, 1601)
+	for i := range positions {
+		positions[i] = int32(i)
+	}
+
+	positionIDs, err := ctx.Input().FromIntSlice(positions, len(positions))
+	if err != nil {
+		return nil, err
+	}
+
+	crossAttentionStates := m.VisionModel.Forward(ctx, pixelValues, positionIDs, aspectRatio)
+	return m.Projector.Forward(ctx, crossAttentionStates), nil
+}
+
+func (m *Model) PostTokenize(inputs []input.Input) ([]input.Input, error) {
+	var images []input.Input
+	fnvHash := fnv.New64a()
+
+	for i := range inputs {
+		if inputs[i].Multimodal == nil {
+			if len(images) > 0 {
+				inputs[i].Multimodal = []ml.Tensor{images[0].Multimodal.(ml.Tensor)}
+				inputs[i].MultimodalHash = images[0].MultimodalHash
+				for j := 1; j < len(images); j++ {
+					inputs[i].Multimodal = append(inputs[i].Multimodal.([]ml.Tensor), images[0].Multimodal.(ml.Tensor))
+					fnvHash.Reset()
+					binary.Write(fnvHash, binary.NativeEndian, inputs[i].MultimodalHash)
+					binary.Write(fnvHash, binary.NativeEndian, inputs[j].MultimodalHash)
+					inputs[i].MultimodalHash = fnvHash.Sum64()
+				}
+				images = nil
+			}
+		} else {
+			images = append(images, inputs[i])
+			inputs[i].Token = -1
+		}
+	}
+
+	inputs = slices.DeleteFunc(inputs, func(input input.Input) bool { return input.Token == -1 })
+
+	return inputs, nil
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 	var crossAttentionStates ml.Tensor
-	if opts.Images != nil {
-		f32s, aspectRatioID, err := m.ImageProcessor.ProcessImage(opts.Images[0])
-		if err != nil {
-			return nil, err
+	if len(batch.Multimodal) > 0 {
+		images := batch.Multimodal[len(batch.Multimodal)-1].Multimodal.([]ml.Tensor)
+		if len(images) > 0 {
+			crossAttentionStates = images[len(images)-1]
 		}
-
-		pixelValues, err := ctx.FromFloatSlice(f32s,
-			m.ImageProcessor.imageSize,
-			m.ImageProcessor.imageSize,
-			m.ImageProcessor.numChannels,
-			m.ImageProcessor.maxNumTiles,
-		)
-		if err != nil {
-			return nil, err
-		}
-
-		aspectRatio, err := ctx.FromIntSlice([]int32{int32(aspectRatioID)}, 1)
-		if err != nil {
-			return nil, err
-		}
-
-		positions := make([]int32, 1601)
-		for i := range positions {
-			positions[i] = int32(i)
-		}
-
-		positionIDs, err := ctx.FromIntSlice(positions, len(positions))
-		if err != nil {
-			return nil, err
-		}
-
-		crossAttentionStates = m.VisionModel.Forward(ctx, pixelValues, positionIDs, aspectRatio)
-		crossAttentionStates = m.Projector.Forward(ctx, crossAttentionStates)
 	}
 
-	inputs, err := ctx.FromIntSlice(opts.Inputs, len(opts.Inputs))
+	positions, err := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
 	if err != nil {
 		return nil, err
 	}
 
-	positions, err := ctx.FromIntSlice(opts.Positions, len(opts.Positions))
-	if err != nil {
-		return nil, err
-	}
-
-	outputs, err := ctx.FromIntSlice(opts.Outputs, len(opts.Outputs))
+	outputs, err := ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
 	if err != nil {
 		return nil, err
 	}
 
 	// TODO: attention mask, cross attention mask
-	return m.TextModel.Forward(ctx, inputs, positions, outputs, nil, crossAttentionStates, nil, m.Cache.(*kvcache.WrapperCache)), nil
+	return m.TextModel.Forward(ctx, batch.Inputs, positions, outputs, nil, crossAttentionStates, nil, m.Cache.(*kvcache.WrapperCache)), nil
 }
 
 func init() {

model/models/mllama/model_text.go

@@ -10,36 +10,31 @@ import (
 )
 
 type TextSelfAttention struct {
-	Query  *nn.Linear `gguf:"attn_q"`
-	Key    *nn.Linear `gguf:"attn_k"`
-	Value  *nn.Linear `gguf:"attn_v"`
-	Output *nn.Linear `gguf:"attn_output"`
+	Query       *nn.Linear `gguf:"attn_q"`
+	Key         *nn.Linear `gguf:"attn_k"`
+	Value       *nn.Linear `gguf:"attn_v"`
+	Output      *nn.Linear `gguf:"attn_output"`
+	RopeFactors ml.Tensor  `gguf:"rope_freqs.weight"`
 }
 
 func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, _ ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
 	batchSize := hiddenState.Dim(1)
 	headDim := opts.hiddenSize / opts.numHeads
+	ropeType := uint32(0)
 
 	query := sa.Query.Forward(ctx, hiddenState)
 	query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)
-	query = query.RoPE(ctx, positions, opts.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+	query = query.RoPE(ctx, positions, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
 
 	key := sa.Key.Forward(ctx, hiddenState)
 	key = key.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
-	key = key.RoPE(ctx, positions, opts.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+	key = key.RoPE(ctx, positions, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
 
 	value := sa.Value.Forward(ctx, hiddenState)
 	value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
 
-	cache.Put(ctx, key, value)
-	key, value, mask := cache.Get(ctx)
-
-	query = query.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
-	key = key.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
-	value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
-
 	scaleFactor := 1.0 / math.Sqrt(float64(headDim))
-	attention := nn.Attention(ctx, query, key, value, mask, scaleFactor)
+	attention := nn.Attention(ctx, query, key, value, scaleFactor, cache)
 	attention = attention.Reshape(ctx, opts.hiddenSize, batchSize)
 
 	return sa.Output.Forward(ctx, attention)
@@ -47,7 +42,11 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, _ m
 
 func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
 	// This will only get called for layers in the cache, which are just the self attention layers
-	return key.RoPE(ctx, shift, m.RopeFactors, m.ropeDim, m.ropeBase, m.ropeScale), nil
+	if sa, ok := m.Transformer.Layers[layer].(*TextSelfAttentionDecoderLayer); ok {
+		return key.RoPE(ctx, shift, sa.SelfAttention.RopeFactors, m.ropeDim, uint32(0), m.ropeBase, m.ropeScale), nil
+	}
+
+	return key, nil
 }
 
 type TextMLP struct {
@@ -107,7 +106,7 @@ func (ca *TextCrossAttention) Forward(ctx ml.Context, hiddenState, crossAttentio
 	query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)
 	query = ca.QueryNorm.Forward(ctx, query, opts.eps)
 
-	var key, value, mask ml.Tensor
+	var key, value ml.Tensor
 	if crossAttentionStates != nil {
 		numVisionTokens, numTiles := crossAttentionStates.Dim(1), crossAttentionStates.Dim(2)
 
@@ -119,16 +118,23 @@ func (ca *TextCrossAttention) Forward(ctx ml.Context, hiddenState, crossAttentio
 		value = value.Reshape(ctx, headDim, opts.numKVHeads, numVisionTokens*numTiles)
 
 		cache.Put(ctx, key, value)
-	} else {
-		key, value, mask = cache.Get(ctx)
 	}
 
-	query = query.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
-	key = key.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
-	value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+	key, value, _ = cache.Get(ctx)
 
 	scaleFactor := 1.0 / math.Sqrt(float64(headDim))
-	attention := nn.Attention(ctx, query, key, value, mask, scaleFactor)
+
+	query = query.Permute(ctx, 0, 2, 1, 3)
+	key = key.Permute(ctx, 0, 2, 1, 3)
+	value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+
+	kq := key.MulmatFullPrec(ctx, query)
+
+	kq = kq.Scale(ctx, scaleFactor)
+	kq = kq.Softmax(ctx)
+
+	kqv := value.Mulmat(ctx, kq)
+	attention := kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	attention = attention.Reshape(ctx, opts.hiddenSize, batchSize)
 
 	return ca.Output.Forward(ctx, attention)
@@ -191,8 +197,6 @@ func (d *TextDecoder) Forward(ctx ml.Context, hiddenState, positionIDs, outputs,
 }
 
 type TextModelOptions struct {
-	RopeFactors ml.Tensor `gguf:"rope_freqs.weight"`
-
 	hiddenSize, numHeads, numKVHeads int
 	eps, ropeBase, ropeScale         float32
 	ropeDim                          uint32

model/models/mllama/process_image.go

@@ -144,8 +144,6 @@ func (p *ImageProcessor) splitToTiles(img image.Image, numTilesSize image.Point)
 	return images
 }
 
-// remove the "alpha" channel by drawing over a prefilled image
-//
 // remove the "alpha" channel by drawing over a prefilled image
 //
 //nolint:unused

model/models/models.go

@@ -1,6 +1,8 @@
 package models
 
 import (
+	_ "github.com/ollama/ollama/model/models/gemma2"
+	_ "github.com/ollama/ollama/model/models/gemma3"
 	_ "github.com/ollama/ollama/model/models/llama"
 	_ "github.com/ollama/ollama/model/models/mllama"
 )

model/process_text.go

@@ -4,6 +4,7 @@ import (
 	"cmp"
 	"iter"
 	"log/slog"
+	"slices"
 	"strings"
 	"sync"
 
@@ -18,19 +19,30 @@ const (
 	SpecialEOS
 )
 
+const (
+	TOKEN_TYPE_NORMAL = iota + 1
+	TOKEN_TYPE_UNKNOWN
+	TOKEN_TYPE_CONTROL
+	TOKEN_TYPE_USER_DEFINED
+	TOKEN_TYPE_UNUSED
+	TOKEN_TYPE_BYTE
+)
+
 type TextProcessor interface {
-	Encode(string) ([]int32, error)
+	Encode(s string, addSpecial bool) ([]int32, error)
 	Decode([]int32) (string, error)
 	Is(int32, Special) bool
+	Vocab() *Vocabulary
 }
 
 type Vocabulary struct {
 	Values []string
 	Types  []uint32
-	Scores []uint32
+	Scores []float32
 	Merges []string
 
-	BOS, EOS int32
+	BOS, EOS, EOT          int32
+	AddBOS, AddEOS, AddEOT bool
 
 	specialOnce sync.Once
 	special     []string
@@ -47,7 +59,7 @@ func (v *Vocabulary) Is(id int32, special Special) bool {
 	case SpecialBOS:
 		return id == v.BOS
 	case SpecialEOS:
-		return id == v.EOS
+		return id == v.EOS || id == v.EOT
 	default:
 		return false
 	}
@@ -75,7 +87,9 @@ func (v *Vocabulary) Decode(id int32) string {
 func (v *Vocabulary) SpecialVocabulary() []string {
 	v.specialOnce.Do(func() {
 		for i := range v.Values {
-			if v.Types[i] == 3 {
+			if slices.Contains([]int{105, 106}, i) {
+				v.special = append(v.special, v.Values[i])
+			} else if v.Types[i] == TOKEN_TYPE_CONTROL {
 				v.special = append(v.special, v.Values[i])
 			}
 		}
@@ -143,7 +157,7 @@ type merge struct {
 	runes []rune
 }
 
-func (bpe BytePairEncoding) Encode(s string) ([]int32, error) {
+func (bpe BytePairEncoding) Encode(s string, addSpecial bool) ([]int32, error) {
 	fragments := []fragment{{value: s}}
 	for _, special := range bpe.vocab.SpecialVocabulary() {
 		// TODO: process special tokens concurrently
@@ -176,7 +190,6 @@ func (bpe BytePairEncoding) Encode(s string) ([]int32, error) {
 	for _, frag := range fragments {
 		if len(frag.ids) > 0 {
 			ids = append(ids, frag.ids...)
-			slog.Debug("encoded", "text", frag.value, "ids", frag.ids, "special", true)
 			continue
 		}
 
@@ -200,7 +213,6 @@ func (bpe BytePairEncoding) Encode(s string) ([]int32, error) {
 			// short circuit if the fragment is in the vocabulary
 			if id := bpe.vocab.Encode(sb.String()); id >= 0 {
 				ids = append(ids, id)
-				slog.Debug("encoded", "text", sb.String(), "ids", []int32{id})
 				continue
 			}
 
@@ -274,13 +286,32 @@ func (bpe BytePairEncoding) Encode(s string) ([]int32, error) {
 					// TODO: handle the edge case where the rune isn't in the vocabulary
 					if id := bpe.vocab.Encode(string(merge.runes)); id >= 0 {
 						ids = append(ids, id)
-						slog.Debug("encoded", "text", string(merge.runes), "ids", []int32{id})
 					}
 				}
 			}
 		}
 	}
 
+	if addSpecial && len(ids) > 0 {
+		if bpe.vocab.AddBOS {
+			if ids[0] == bpe.vocab.BOS {
+				slog.Warn("adding bos token to prompt which already has it", "id", bpe.vocab.BOS)
+			}
+
+			slog.Debug("adding bos token to prompt", "id", bpe.vocab.BOS)
+			ids = append([]int32{bpe.vocab.BOS}, ids...)
+		}
+
+		if bpe.vocab.AddEOS {
+			if ids[len(ids)-1] == bpe.vocab.EOS {
+				slog.Warn("adding eos token to prompt which already has it", "id", bpe.vocab.EOS)
+			}
+
+			slog.Debug("adding eos token to prompt", "id", bpe.vocab.EOS)
+			ids = append(ids, bpe.vocab.EOS)
+		}
+	}
+
 	return ids, nil
 }
 
@@ -308,6 +339,5 @@ func (bpe BytePairEncoding) Decode(ids []int32) (string, error) {
 		}
 	}
 
-	slog.Debug("decoded", "ids", ids, "text", sb.String())
 	return sb.String(), nil
 }

model/process_text_spm.go

@@ -0,0 +1,250 @@
+package model
+
+import (
+	"iter"
+	"log/slog"
+	"strings"
+
+	"github.com/dlclark/regexp2"
+	queue "github.com/emirpasic/gods/v2/queues/priorityqueue"
+)
+
+const spmWhitespaceSep = "▁"
+
+func replaceWhitespaceBySeperator(s string) string {
+	return strings.ReplaceAll(s, " ", spmWhitespaceSep)
+}
+
+type SentencePieceModel struct {
+	maxTokenLen int
+	pre         *regexp2.Regexp
+	vocab       *Vocabulary
+}
+
+var _ TextProcessor = (*SentencePieceModel)(nil)
+
+func NewSentencePieceModel(pre string, vocab *Vocabulary) SentencePieceModel {
+	slog.Debug("Tokens", "num tokens", len(vocab.Values), "vals", vocab.Values[:5], "scores", vocab.Scores[:5], "types", vocab.Types[:5])
+
+	counter := map[int]int{}
+	var maxTokenLen int
+	for cnt := range vocab.Types {
+		switch vocab.Types[cnt] {
+		case TOKEN_TYPE_NORMAL, TOKEN_TYPE_USER_DEFINED, TOKEN_TYPE_UNUSED:
+			maxTokenLen = max(maxTokenLen, len(vocab.Values[cnt]))
+			fallthrough
+		default:
+			counter[int(vocab.Types[cnt])] += 1
+		}
+	}
+
+	slog.Debug("Token counts", "normal", counter[TOKEN_TYPE_NORMAL], "unknown", counter[TOKEN_TYPE_UNKNOWN], "control", counter[TOKEN_TYPE_CONTROL],
+		"user defined", counter[TOKEN_TYPE_USER_DEFINED], "unused", counter[TOKEN_TYPE_UNUSED], "byte", counter[TOKEN_TYPE_BYTE],
+		"max token len", maxTokenLen)
+
+	return SentencePieceModel{
+		maxTokenLen: maxTokenLen,
+		pre:         regexp2.MustCompile(pre, regexp2.Unicode|regexp2.RE2),
+		vocab:       vocab,
+	}
+}
+
+func (spm SentencePieceModel) Is(id int32, special Special) bool {
+	return spm.vocab.Is(id, special)
+}
+
+func (spm SentencePieceModel) Vocab() *Vocabulary {
+	return spm.vocab
+}
+
+func (spm *SentencePieceModel) split(s string) iter.Seq[string] {
+	return func(yield func(string) bool) {
+		for m, _ := spm.pre.FindStringMatch(s); m != nil; m, _ = spm.pre.FindNextMatch(m) {
+			if !yield(m.String()) {
+				break
+			}
+		}
+	}
+}
+
+func (spm SentencePieceModel) Encode(s string, addSpecial bool) ([]int32, error) {
+	fragments := []fragment{{value: s}}
+	for _, special := range spm.vocab.SpecialVocabulary() {
+		// TODO: process special tokens concurrently
+		id := spm.vocab.Encode(special)
+		for i := 0; i < len(fragments); i++ {
+			frag := fragments[i]
+			if len(frag.ids) > 0 {
+				continue
+			}
+
+			var middle []fragment
+			switch i := strings.Index(frag.value, special); {
+			case i < 0:
+				middle = append(middle, frag)
+			case i > 0:
+				middle = append(middle, fragment{value: frag.value[:i]})
+				fallthrough
+			default:
+				middle = append(middle, fragment{value: special, ids: []int32{id}})
+				if rest := frag.value[i+len(special):]; rest != "" {
+					middle = append(middle, fragment{value: rest})
+				}
+			}
+
+			fragments = append(fragments[:i], append(middle, fragments[i+1:]...)...)
+		}
+	}
+	slog.Debug("fragments", "frags", fragments)
+
+	var ids []int32
+	for _, frag := range fragments {
+		if len(frag.ids) > 0 {
+			ids = append(ids, frag.ids...)
+			continue
+		}
+
+		for split := range spm.split(frag.value) {
+			split = replaceWhitespaceBySeperator(split)
+
+			var sb strings.Builder
+			sb.Write([]byte(split))
+			if id := spm.vocab.Encode(sb.String()); id >= 0 {
+				ids = append(ids, id)
+				continue
+			}
+
+			runes := []rune(sb.String())
+			pq := queue.NewWith(func(a, b any) int {
+				priA := a.(*candidate)
+				priB := b.(*candidate)
+				if priA.score > priB.score || (priA.score == priB.score && priA.a < priB.a) {
+					return -1
+				}
+				return 1
+			})
+
+			merges := make([]merge, len(runes))
+			for r := range runes {
+				merges[r] = merge{
+					p:     r - 1,
+					n:     r + 1,
+					runes: []rune{runes[r]},
+				}
+			}
+
+			slog.Debug("tokenizer", "merges", merges)
+
+			pairwise := func(a, b int) *candidate {
+				if a < 0 || b >= len(runes) {
+					return nil
+				}
+
+				left, right := string(merges[a].runes), string(merges[b].runes)
+				if id := spm.vocab.Encode(left + right); id >= 0 {
+					return &candidate{
+						a:     a,
+						b:     b,
+						score: spm.vocab.Scores[id],
+					}
+				}
+				return nil
+			}
+
+			for i := range len(runes) - 1 {
+				if pair := pairwise(i, i+1); pair != nil {
+					pq.Enqueue(pair)
+				}
+			}
+
+			pqv := pq.Values()
+			for _, v := range pqv {
+				e := v.(*candidate)
+				slog.Debug("candidate", "candidate", e)
+			}
+
+			for !pq.Empty() {
+				v, _ := pq.Dequeue()
+				pair := v.(*candidate)
+				left, right := merges[pair.a], merges[pair.b]
+
+				slog.Debug("pair", "left", left, "right", right)
+				if len(left.runes) == 0 || len(right.runes) == 0 {
+					continue
+				}
+
+				if id := spm.vocab.Encode(string(left.runes) + string(right.runes)); id < 0 {
+					continue
+				}
+
+				merges[pair.a].runes = append(left.runes, right.runes...)
+				merges[pair.b].runes = nil
+				merges[pair.a].n = right.n
+				if right.n < len(merges) {
+					merges[right.n].p = pair.a
+				}
+
+				if pair := pairwise(merges[pair.a].p, pair.a); pair != nil {
+					pq.Enqueue(pair)
+				}
+
+				if pair := pairwise(pair.a, merges[pair.a].n); pair != nil {
+					pq.Enqueue(pair)
+				}
+			}
+
+			slog.Debug("merges", "merges", merges)
+
+			for _, merge := range merges {
+				if len(merge.runes) > 0 {
+					if id := spm.vocab.Encode(string(merge.runes)); id >= 0 {
+						ids = append(ids, id)
+					} else {
+						slog.Debug("missing token", "token", string(merge.runes))
+					}
+				}
+			}
+		}
+	}
+
+	if addSpecial && len(ids) > 0 {
+		if spm.vocab.AddBOS {
+			if ids[0] == spm.vocab.BOS {
+				slog.Warn("adding bos token to prompt which already has it", "id", spm.vocab.BOS)
+			}
+
+			slog.Debug("adding bos token to prompt", "id", spm.vocab.BOS)
+			ids = append([]int32{spm.vocab.BOS}, ids...)
+		}
+
+		if spm.vocab.AddEOS {
+			if ids[len(ids)-1] == spm.vocab.EOS {
+				slog.Warn("adding eos token to prompt which already has it", "id", spm.vocab.EOS)
+			}
+
+			slog.Debug("adding eos token to prompt", "id", spm.vocab.EOS)
+			ids = append(ids, spm.vocab.EOS)
+		}
+	}
+
+	return ids, nil
+}
+
+type candidate struct {
+	a, b  int
+	score float32
+}
+
+func (spm SentencePieceModel) Decode(ids []int32) (string, error) {
+	var sb strings.Builder
+	for _, id := range ids {
+		data := spm.vocab.Decode(id)
+		data = strings.ReplaceAll(data, spmWhitespaceSep, " ")
+		if _, err := sb.WriteString(data); err != nil {
+			return "", err
+		}
+	}
+
+	slog.Debug("decoded", "ids", ids, "text", sb.String())
+	return sb.String(), nil
+}

model/process_text_spm_test.go

@@ -0,0 +1,118 @@
+package model
+
+import (
+	"log/slog"
+	"os"
+	"path/filepath"
+	"slices"
+	"testing"
+
+	"google.golang.org/protobuf/proto"
+
+	"github.com/ollama/ollama/convert/sentencepiece"
+)
+
+func loadSentencePieceVocab(t *testing.T) SentencePieceModel {
+	t.Helper()
+
+	bts, err := os.ReadFile(filepath.Join("testdata", "gemma2", "tokenizer.model"))
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	var spm sentencepiece.ModelProto
+	if err := proto.Unmarshal(bts, &spm); err != nil {
+		t.Fatal(err)
+	}
+
+	preTokenizer := `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`
+
+	var v Vocabulary
+
+	for _, piece := range spm.GetPieces() {
+		v.Values = append(v.Values, piece.GetPiece())
+		v.Scores = append(v.Scores, piece.GetScore())
+		switch t := piece.GetType(); t {
+		case sentencepiece.ModelProto_SentencePiece_UNKNOWN,
+			sentencepiece.ModelProto_SentencePiece_CONTROL,
+			sentencepiece.ModelProto_SentencePiece_UNUSED,
+			sentencepiece.ModelProto_SentencePiece_BYTE:
+			v.Types = append(v.Types, uint32(t))
+		default:
+			tt := uint32(sentencepiece.ModelProto_SentencePiece_NORMAL)
+			// todo parse the special tokens file
+			//   - this will roundtrip correctly but the <start_of_turn> and
+			//     <end_of_turn> tokens aren't processed
+			v.Types = append(v.Types, tt)
+		}
+	}
+
+	return NewSentencePieceModel(preTokenizer, &v)
+}
+
+func TestSentencePieceEncode(t *testing.T) {
+	logger := slog.New(slog.NewTextHandler(os.Stdout, &slog.HandlerOptions{Level: slog.LevelDebug}))
+	slog.SetDefault(logger)
+
+	tokenizer := loadSentencePieceVocab(t)
+
+	t.Run("basic roundtrip", func(t *testing.T) {
+		t.Parallel()
+
+		cases := []string{
+			"hello",
+			"hello ",
+			"hello  ",
+			" hello",
+			" hello ",
+			" hello  ",
+			"hello world",
+			"请考试我的软件！12345",
+			"你好",
+			"Hello 你好 world!",
+			"Special characters: !@#$%^&*()_+-=[]{}|;':\",./<>?",
+			"Multilingual: 你好 こんにちは Привет Hola مرحبا",
+			"Numbers and symbols: 123456789 +- */",
+			"Special tokens: <bos> text <eos>",
+			"Code snippets: func main() { fmt.Println(\"Hello World\") }",
+			"Long text: " + "Lorem ipsum dolor sit amet, consectetur adipiscing elit. " +
+				"Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. " +
+				"Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.",
+		}
+
+		for _, want := range cases {
+			ids, err := tokenizer.Encode(want, true)
+			if err != nil {
+				t.Fatal(err)
+			}
+
+			if got, err := tokenizer.Decode(ids); err != nil {
+				t.Fatal(err)
+			} else if got != want {
+				t.Errorf("got %q, want %q [%#v]", got, want, ids)
+			}
+		}
+	})
+
+	t.Run("special tokens", func(t *testing.T) {
+		type candidate struct {
+			token string
+			ids   []int32
+		}
+
+		cases := []candidate{
+			{"<bos>", []int32{2}},
+			{"<eos>", []int32{1}},
+		}
+
+		for _, want := range cases {
+			ids, err := tokenizer.Encode(want.token, true)
+			if err != nil {
+				t.Fatal(err)
+			}
+			if !slices.Equal(ids, want.ids) {
+				t.Errorf("got %#v, want %#v", ids, want.ids)
+			}
+		}
+	})
+}