change name to context_length

context_window and addressing comments
update docs
2025-02-07 11:50:38 -08:00 · 2025-02-05 11:26:55 -08:00 · 2025-02-04 15:17:16 -08:00 · 2025-02-04 10:47:32 -08:00 · 2025-01-30 13:53:24 -08:00 · 2025-01-30 13:50:25 -08:00
466 changed files with 23670 additions and 25024 deletions
--- a/.golangci.yaml
+++ b/.golangci.yaml
@@ -8,8 +8,6 @@ linters:
    - containedctx
    - contextcheck
    - errcheck
-    - exportloopref
-    - gci
    - gocheckcompilerdirectives
    - gofmt
    - gofumpt
@@ -30,8 +28,6 @@ linters:
    - wastedassign
    - whitespace
 linters-settings:
-  gci:
-    sections: [standard, default, localmodule]
  staticcheck:
    checks:
      - all
--- a/.prettierrc.json
+++ b/.prettierrc.json
@@ -1,10 +0,0 @@
-{
-  "trailingComma": "es5",
-  "tabWidth": 2,
-  "useTabs": false,
-  "semi": false,
-  "singleQuote": true,
-  "jsxSingleQuote": true,
-  "printWidth": 120,
-  "arrowParens": "avoid"
-}
--- a/7
+++ b/7
@@ -66,9 +66,11 @@ COPY . .
 ARG OLLAMA_SKIP_CUDA_GENERATE
 ARG OLLAMA_SKIP_ROCM_GENERATE
 ARG OLLAMA_FAST_BUILD
+ARG VERSION
+ARG CUSTOM_CPU_FLAGS
 RUN --mount=type=cache,target=/root/.ccache \
    if grep "^flags" /proc/cpuinfo|grep avx>/dev/null; then \
-        make -j $(expr $(nproc) / 2 ) dist ; \
+        make -j $(nproc) dist ; \
    else \
        make -j 5 dist ; \
    fi
@@ -91,6 +93,7 @@ WORKDIR /go/src/github.com/ollama/ollama/
 COPY . .
 ARG CGO_CFLAGS
 ENV GOARCH arm64
+ARG VERSION
 RUN --mount=type=cache,target=/root/.ccache \
    make -j 5 dist_cuda_v11 \
        CUDA_ARCHITECTURES="72;87" \
@@ -109,6 +112,7 @@ WORKDIR /go/src/github.com/ollama/ollama/
 COPY . .
 ARG CGO_CFLAGS
 ENV GOARCH arm64
+ARG VERSION
 RUN --mount=type=cache,target=/root/.ccache \
    make -j 5 dist_cuda_v12 \
        CUDA_ARCHITECTURES="87" \
@@ -120,6 +124,7 @@ FROM --platform=linux/arm64 unified-builder-arm64 AS build-arm64
 COPY . .
 ARG OLLAMA_SKIP_CUDA_GENERATE
 ARG OLLAMA_FAST_BUILD
+ARG VERSION
 RUN --mount=type=cache,target=/root/.ccache \
    make -j 5 dist
 COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
--- a/2
+++ b/2
@@ -8,11 +8,9 @@ include make/cuda-v12-defs.make
 include make/rocm-defs.make

 ifeq ($(CUSTOM_CPU_FLAGS),)
-ifneq ($(OS),darwin)
 ifeq ($(ARCH),amd64)
 	RUNNER_TARGETS=cpu
 endif
-endif
 # Without CUSTOM_CPU_FLAGS we default to build both v11 and v12 if present
 ifeq ($(OLLAMA_SKIP_CUDA_GENERATE),)
 ifneq ($(CUDA_11_COMPILER),)
--- a/README.md
+++ b/README.md
@@ -1,11 +1,11 @@
 <div align="center">
- <img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
+  <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>

 # Ollama

-[![Discord](https://dcbadge.vercel.app/api/server/ollama?style=flat&compact=true)](https://discord.gg/ollama)
-
 Get up and running with large language models.

 ### macOS
@@ -33,6 +33,11 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
 - [ollama-python](https://github.com/ollama/ollama-python)
 - [ollama-js](https://github.com/ollama/ollama-js)

+### Community
+
+- [Discord](https://discord.gg/ollama)
+- [Reddit](https://reddit.com/r/ollama)
+
 ## Quickstart

 To run and chat with [Llama 3.2](https://ollama.com/library/llama3.2):
@@ -56,8 +61,8 @@ Here are some example models that can be downloaded:
 | Llama 3.2 Vision   | 90B        | 55GB  | `ollama run llama3.2-vision:90b` |
 | 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`                |
-| Phi 3 Medium       | 14B        | 7.9GB | `ollama run phi3:medium`         |
 | 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`          |
@@ -97,7 +102,7 @@ Ollama supports importing GGUF models in the Modelfile:
   ollama run example
   ```

-### Import from PyTorch or Safetensors
+### Import from Safetensors

 See the [guide](docs/import.md) on importing models for more information.

@@ -132,7 +137,7 @@ ollama run mario
 Hello! It's your friend Mario.
 ```

-For more examples, see the [examples](examples) directory. For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.
+For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.

 ## CLI Reference

@@ -298,6 +303,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [AnythingLLM (Docker + MacOs/Windows/Linux native app)](https://github.com/Mintplex-Labs/anything-llm)
 - [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
 - [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
+- [IntelliBar](https://intellibar.app/) (AI-powered assistant for macOS)
 - [QA-Pilot](https://github.com/reid41/QA-Pilot) (Interactive chat tool that can leverage Ollama models for rapid understanding and navigation of GitHub code repositories)
 - [ChatOllama](https://github.com/sugarforever/chat-ollama) (Open Source Chatbot based on Ollama with Knowledge Bases)
 - [CRAG Ollama Chat](https://github.com/Nagi-ovo/CRAG-Ollama-Chat) (Simple Web Search with Corrective RAG)
@@ -327,6 +333,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [BoltAI for Mac](https://boltai.com) (AI Chat Client for Mac)
 - [Harbor](https://github.com/av/harbor) (Containerized LLM Toolkit with Ollama as default backend)
 - [PyGPT](https://github.com/szczyglis-dev/py-gpt) (AI desktop assistant for Linux, Windows and Mac)
+- [Alpaca](https://github.com/Jeffser/Alpaca) (An Ollama client application for linux and macos made with GTK4 and Adwaita)
 - [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT/blob/master/docs/content/platform/ollama.md) (AutoGPT Ollama integration)
 - [Go-CREW](https://www.jonathanhecl.com/go-crew/) (Powerful Offline RAG in Golang)
 - [PartCAD](https://github.com/openvmp/partcad/) (CAD model generation with OpenSCAD and CadQuery)
@@ -361,6 +368,8 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Abbey](https://github.com/US-Artificial-Intelligence/abbey) (A configurable AI interface server with notebooks, document storage, and YouTube support)
 - [Minima](https://github.com/dmayboroda/minima) (RAG with on-premises or fully local workflow)
 - [aidful-ollama-model-delete](https://github.com/AidfulAI/aidful-ollama-model-delete) (User interface for simplified model cleanup)
+- [Perplexica](https://github.com/ItzCrazyKns/Perplexica) (An AI-powered search engine & an open-source alternative to Perplexity AI)
+- [AI Toolkit for Visual Studio Code](https://aka.ms/ai-tooklit/ollama-docs) (Microsoft-official VSCode extension to chat, test, evaluate models with Ollama support, and use them in your AI applications.)

 ### Cloud

@@ -373,6 +382,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [oterm](https://github.com/ggozad/oterm)
 - [Ellama Emacs client](https://github.com/s-kostyaev/ellama)
 - [Emacs client](https://github.com/zweifisch/ollama)
+- [neollama](https://github.com/paradoxical-dev/neollama) UI client for interacting with models from within Neovim
 - [gen.nvim](https://github.com/David-Kunz/gen.nvim)
 - [ollama.nvim](https://github.com/nomnivore/ollama.nvim)
 - [ollero.nvim](https://github.com/marco-souza/ollero.nvim)
@@ -407,6 +417,8 @@ See the [API documentation](./docs/api.md) for all endpoints.

 ### Database

+- [pgai](https://github.com/timescale/pgai) - PostgreSQL as a vector database (Create and search embeddings from Ollama models using pgvector)
+   - [Get started guide](https://github.com/timescale/pgai/blob/main/docs/vectorizer-quick-start.md)
 - [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md) (Connects Ollama models with nearly 200 data platforms and apps)
 - [chromem-go](https://github.com/philippgille/chromem-go/blob/v0.5.0/embed_ollama.go) with [example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama)
 - [Kangaroo](https://github.com/dbkangaroo/kangaroo) (AI-powered SQL client and admin tool for popular databases)
@@ -425,10 +437,12 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
 - [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama)
 - [crewAI](https://github.com/crewAIInc/crewAI)
+- [Yacana](https://remembersoftwares.github.io/yacana/) (User-friendly multi-agent framework for brainstorming and executing predetermined flows with built-in tool integration)
 - [Spring AI](https://github.com/spring-projects/spring-ai) with [reference](https://docs.spring.io/spring-ai/reference/api/chat/ollama-chat.html) and [example](https://github.com/tzolov/ollama-tools)
 - [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example)
 - [LangChain4j](https://github.com/langchain4j/langchain4j) with [example](https://github.com/langchain4j/langchain4j-examples/tree/main/ollama-examples/src/main/java)
 - [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs)
+- [LangChain for .NET](https://github.com/tryAGI/LangChain) with [example](https://github.com/tryAGI/LangChain/blob/main/examples/LangChain.Samples.OpenAI/Program.cs)
 - [LLPhant](https://github.com/theodo-group/LLPhant?tab=readme-ov-file#ollama)
 - [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/ollama/) and [LlamaIndexTS](https://ts.llamaindex.ai/modules/llms/available_llms/ollama)
 - [LiteLLM](https://github.com/BerriAI/litellm)
@@ -468,6 +482,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [GoLamify](https://github.com/prasad89/golamify)
 - [Ollama for Haskell](https://github.com/tusharad/ollama-haskell)
 - [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in unified API)
+- [LlmTornado](https://github.com/lofcz/llmtornado) (C# library providing a unified interface for major FOSS & Commercial inference APIs)

 ### Mobile

@@ -517,6 +532,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [AI Summmary Helper plugin](https://github.com/philffm/ai-summary-helper)
 - [TextCraft](https://github.com/suncloudsmoon/TextCraft) (Copilot in Word alternative using Ollama)
 - [Alfred Ollama](https://github.com/zeitlings/alfred-ollama) (Alfred Workflow)
+- [TextLLaMA](https://github.com/adarshM84/TextLLaMA) A Chrome Extension that helps you write emails, correct grammar, and translate into any language

 ### Supported backends

@@ -525,4 +541,5 @@ See the [API documentation](./docs/api.md) for all endpoints.
 ### Observability

 - [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. 
+- [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.
+- [Langfuse](https://langfuse.com/docs/integrations/ollama) is an open source LLM observability platform that enables teams to collaboratively monitor, evaluate and debug AI applications.
--- a/api/examples/README.md
+++ b/api/examples/README.md
@@ -0,0 +1,17 @@
+# Ollama API Examples
+
+Run the examples in this directory with:
+
+```
+go run example_name/main.go
+```
+## Chat - Chat with a model
+- [chat/main.go](chat/main.go)
+
+## Generate - Generate text from a model
+- [generate/main.go](generate/main.go)
+- [generate-streaming/main.go](generate-streaming/main.go)
+
+## Pull - Pull a model
+- [pull-progress/main.go](pull-progress/main.go)
+
--- a/api/examples/chat/main.go
+++ b/api/examples/chat/main.go
--- a/api/examples/generate-streaming/main.go
+++ b/api/examples/generate-streaming/main.go
--- a/api/examples/generate/main.go
+++ b/api/examples/generate/main.go
--- a/api/examples/multimodal/main.go
+++ b/api/examples/multimodal/main.go
--- a/api/examples/pull-progress/main.go
+++ b/api/examples/pull-progress/main.go
--- a/api/types.go
+++ b/api/types.go
@@ -225,7 +225,6 @@ type Options struct {
 	Mirostat         int      `json:"mirostat,omitempty"`
 	MirostatTau      float32  `json:"mirostat_tau,omitempty"`
 	MirostatEta      float32  `json:"mirostat_eta,omitempty"`
-	PenalizeNewline  bool     `json:"penalize_newline,omitempty"`
 	Stop             []string `json:"stop,omitempty"`
 }

@@ -295,17 +294,21 @@ type EmbeddingResponse struct {

 // CreateRequest is the request passed to [Client.Create].
 type CreateRequest struct {
-	Model     string `json:"model"`
-	Modelfile string `json:"modelfile"`
-	Stream    *bool  `json:"stream,omitempty"`
-	Quantize  string `json:"quantize,omitempty"`
+	Model    string `json:"model"`
+	Stream   *bool  `json:"stream,omitempty"`
+	Quantize string `json:"quantize,omitempty"`
+
+	From       string            `json:"from,omitempty"`
+	Files      map[string]string `json:"files,omitempty"`
+	Adapters   map[string]string `json:"adapters,omitempty"`
+	Template   string            `json:"template,omitempty"`
+	License    any               `json:"license,omitempty"`
+	System     string            `json:"system,omitempty"`
+	Parameters map[string]any    `json:"parameters,omitempty"`
+	Messages   []Message         `json:"messages,omitempty"`

 	// Deprecated: set the model name with Model instead
 	Name string `json:"name"`
-
-	// Deprecated: set the file content with Modelfile instead
-	Path string `json:"path"`
-
 	// Deprecated: use Quantize instead
 	Quantization string `json:"quantization,omitempty"`
 }
@@ -602,7 +605,6 @@ func DefaultOptions() Options {
 		Mirostat:         0,
 		MirostatTau:      5.0,
 		MirostatEta:      0.1,
-		PenalizeNewline:  true,
 		Seed:             -1,

 		Runner: Runner{
--- a/cmd/cmd.go
+++ b/cmd/cmd.go
@@ -1,13 +1,10 @@
 package cmd

 import (
-	"archive/zip"
 	"bufio"
-	"bytes"
 	"context"
 	"crypto/ed25519"
 	"crypto/rand"
-	"crypto/sha256"
 	"encoding/json"
 	"encoding/pem"
 	"errors"
@@ -46,15 +43,11 @@ import (
 	"github.com/ollama/ollama/version"
 )

-var (
-	errModelNotFound     = errors.New("no Modelfile or safetensors files found")
-	errModelfileNotFound = errors.New("specified Modelfile wasn't found")
-)
+var errModelfileNotFound = errors.New("specified Modelfile wasn't found")

 func getModelfileName(cmd *cobra.Command) (string, error) {
-	fn, _ := cmd.Flags().GetString("file")
+	filename, _ := cmd.Flags().GetString("file")

-	filename := fn
 	if filename == "" {
 		filename = "Modelfile"
 	}
@@ -66,7 +59,7 @@ func getModelfileName(cmd *cobra.Command) (string, error) {

 	_, err = os.Stat(absName)
 	if err != nil {
-		return fn, err
+		return "", err
 	}

 	return absName, nil
@@ -102,68 +95,52 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}

-	home, err := os.UserHomeDir()
+	status := "gathering model components"
+	spinner := progress.NewSpinner(status)
+	p.Add(status, spinner)
+
+	req, err := modelfile.CreateRequest(filepath.Dir(filename))
 	if err != nil {
 		return err
 	}
+	spinner.Stop()

-	status := "transferring model data"
-	spinner := progress.NewSpinner(status)
-	p.Add(status, spinner)
-	defer p.Stop()
+	req.Name = args[0]
+	quantize, _ := cmd.Flags().GetString("quantize")
+	if quantize != "" {
+		req.Quantize = quantize
+	}

 	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}

-	for i := range modelfile.Commands {
-		switch modelfile.Commands[i].Name {
-		case "model", "adapter":
-			path := modelfile.Commands[i].Args
-			if path == "~" {
-				path = home
-			} else if strings.HasPrefix(path, "~/") {
-				path = filepath.Join(home, path[2:])
-			}
-
-			if !filepath.IsAbs(path) {
-				path = filepath.Join(filepath.Dir(filename), path)
-			}
-
-			fi, err := os.Stat(path)
-			if errors.Is(err, os.ErrNotExist) && modelfile.Commands[i].Name == "model" {
-				continue
-			} else if err != nil {
+	if len(req.Files) > 0 {
+		fileMap := map[string]string{}
+		for f, digest := range req.Files {
+			if _, err := createBlob(cmd, client, f, digest, p); err != nil {
 				return err
 			}
-
-			if fi.IsDir() {
-				// this is likely a safetensors or pytorch directory
-				// TODO make this work w/ adapters
-				tempfile, err := tempZipFiles(path)
-				if err != nil {
-					return err
-				}
-				defer os.RemoveAll(tempfile)
-
-				path = tempfile
-			}
-
-			digest, err := createBlob(cmd, client, path, spinner)
-			if err != nil {
-				return err
-			}
-
-			modelfile.Commands[i].Args = "@" + digest
+			fileMap[filepath.Base(f)] = digest
 		}
+		req.Files = fileMap
+	}
+
+	if len(req.Adapters) > 0 {
+		fileMap := map[string]string{}
+		for f, digest := range req.Adapters {
+			if _, err := createBlob(cmd, client, f, digest, p); err != nil {
+				return err
+			}
+			fileMap[filepath.Base(f)] = digest
+		}
+		req.Adapters = fileMap
 	}

 	bars := make(map[string]*progress.Bar)
 	fn := func(resp api.ProgressResponse) error {
 		if resp.Digest != "" {
-			spinner.Stop()
-
 			bar, ok := bars[resp.Digest]
 			if !ok {
 				bar = progress.NewBar(fmt.Sprintf("pulling %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
@@ -183,145 +160,23 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return nil
 	}

-	quantize, _ := cmd.Flags().GetString("quantize")
-
-	request := api.CreateRequest{Name: args[0], Modelfile: modelfile.String(), Quantize: quantize}
-	if err := client.Create(cmd.Context(), &request, fn); err != nil {
+	if err := client.Create(cmd.Context(), req, fn); err != nil {
+		if strings.Contains(err.Error(), "path or Modelfile are required") {
+			return fmt.Errorf("the ollama server must be updated to use `ollama create` with this client")
+		}
 		return err
 	}

 	return nil
 }

-func tempZipFiles(path string) (string, error) {
-	tempfile, err := os.CreateTemp("", "ollama-tf")
+func createBlob(cmd *cobra.Command, client *api.Client, path string, digest string, p *progress.Progress) (string, error) {
+	realPath, err := filepath.EvalSymlinks(path)
 	if err != nil {
 		return "", err
 	}
-	defer tempfile.Close()

-	detectContentType := func(path string) (string, error) {
-		f, err := os.Open(path)
-		if err != nil {
-			return "", err
-		}
-		defer f.Close()
-
-		var b bytes.Buffer
-		b.Grow(512)
-
-		if _, err := io.CopyN(&b, f, 512); err != nil && !errors.Is(err, io.EOF) {
-			return "", err
-		}
-
-		contentType, _, _ := strings.Cut(http.DetectContentType(b.Bytes()), ";")
-		return contentType, nil
-	}
-
-	glob := func(pattern, contentType string) ([]string, error) {
-		matches, err := filepath.Glob(pattern)
-		if err != nil {
-			return nil, err
-		}
-
-		for _, safetensor := range matches {
-			if ct, err := detectContentType(safetensor); err != nil {
-				return nil, err
-			} else if ct != contentType {
-				return nil, fmt.Errorf("invalid content type: expected %s for %s", ct, safetensor)
-			}
-		}
-
-		return matches, nil
-	}
-
-	var files []string
-	if st, _ := glob(filepath.Join(path, "model*.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// safetensors files might be unresolved git lfs references; skip if they are
-		// covers model-x-of-y.safetensors, model.fp32-x-of-y.safetensors, model.safetensors
-		files = append(files, st...)
-	} else if st, _ := glob(filepath.Join(path, "adapters.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// covers adapters.safetensors
-		files = append(files, st...)
-	} else if st, _ := glob(filepath.Join(path, "adapter_model.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// covers adapter_model.safetensors
-		files = append(files, st...)
-	} else if pt, _ := glob(filepath.Join(path, "pytorch_model*.bin"), "application/zip"); len(pt) > 0 {
-		// pytorch files might also be unresolved git lfs references; skip if they are
-		// covers pytorch_model-x-of-y.bin, pytorch_model.fp32-x-of-y.bin, pytorch_model.bin
-		files = append(files, pt...)
-	} else if pt, _ := glob(filepath.Join(path, "consolidated*.pth"), "application/zip"); len(pt) > 0 {
-		// pytorch files might also be unresolved git lfs references; skip if they are
-		// covers consolidated.x.pth, consolidated.pth
-		files = append(files, pt...)
-	} else {
-		return "", errModelNotFound
-	}
-
-	// add configuration files, json files are detected as text/plain
-	js, err := glob(filepath.Join(path, "*.json"), "text/plain")
-	if err != nil {
-		return "", err
-	}
-	files = append(files, js...)
-
-	// bert models require a nested config.json
-	// TODO(mxyng): merge this with the glob above
-	js, err = glob(filepath.Join(path, "**/*.json"), "text/plain")
-	if err != nil {
-		return "", err
-	}
-	files = append(files, js...)
-
-	if tks, _ := glob(filepath.Join(path, "tokenizer.model"), "application/octet-stream"); len(tks) > 0 {
-		// add tokenizer.model if it exists, tokenizer.json is automatically picked up by the previous glob
-		// tokenizer.model might be a unresolved git lfs reference; error if it is
-		files = append(files, tks...)
-	} else if tks, _ := glob(filepath.Join(path, "**/tokenizer.model"), "text/plain"); len(tks) > 0 {
-		// some times tokenizer.model is in a subdirectory (e.g. meta-llama/Meta-Llama-3-8B)
-		files = append(files, tks...)
-	}
-
-	zipfile := zip.NewWriter(tempfile)
-	defer zipfile.Close()
-
-	for _, file := range files {
-		f, err := os.Open(file)
-		if err != nil {
-			return "", err
-		}
-		defer f.Close()
-
-		fi, err := f.Stat()
-		if err != nil {
-			return "", err
-		}
-
-		zfi, err := zip.FileInfoHeader(fi)
-		if err != nil {
-			return "", err
-		}
-
-		zfi.Name, err = filepath.Rel(path, file)
-		if err != nil {
-			return "", err
-		}
-
-		zf, err := zipfile.CreateHeader(zfi)
-		if err != nil {
-			return "", err
-		}
-
-		if _, err := io.Copy(zf, f); err != nil {
-			return "", err
-		}
-	}
-
-	return tempfile.Name(), nil
-}
-
-func createBlob(cmd *cobra.Command, client *api.Client, path string, spinner *progress.Spinner) (string, error) {
-	bin, err := os.Open(path)
+	bin, err := os.Open(realPath)
 	if err != nil {
 		return "", err
 	}
@@ -334,18 +189,11 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, spinner *pr
 	}
 	fileSize := fileInfo.Size()

-	hash := sha256.New()
-	if _, err := io.Copy(hash, bin); err != nil {
-		return "", err
-	}
-
-	if _, err := bin.Seek(0, io.SeekStart); err != nil {
-		return "", err
-	}
-
 	var pw progressWriter
-	status := "transferring model data 0%"
-	spinner.SetMessage(status)
+	status := fmt.Sprintf("copying file %s 0%%", digest)
+	spinner := progress.NewSpinner(status)
+	p.Add(status, spinner)
+	defer spinner.Stop()

 	done := make(chan struct{})
 	defer close(done)
@@ -356,15 +204,14 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, spinner *pr
 		for {
 			select {
 			case <-ticker.C:
-				spinner.SetMessage(fmt.Sprintf("transferring model data %d%%", int(100*pw.n.Load()/fileSize)))
+				spinner.SetMessage(fmt.Sprintf("copying file %s %d%%", digest, int(100*pw.n.Load()/fileSize)))
 			case <-done:
-				spinner.SetMessage("transferring model data 100%")
+				spinner.SetMessage(fmt.Sprintf("copying file %s 100%%", digest))
 				return
 			}
 		}
 	}()

-	digest := fmt.Sprintf("sha256:%x", hash.Sum(nil))
 	if err = client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
 		return "", err
 	}
@@ -601,7 +448,7 @@ func ListHandler(cmd *cobra.Command, args []string) error {
 	var data [][]string

 	for _, m := range models.Models {
-		if len(args) == 0 || strings.HasPrefix(m.Name, args[0]) {
+		if len(args) == 0 || strings.HasPrefix(strings.ToLower(m.Name), strings.ToLower(args[0])) {
 			data = append(data, []string{m.Name, m.Digest[:12], format.HumanBytes(m.Size), format.HumanTime(m.ModifiedAt, "Never")})
 		}
 	}
--- a/cmd/cmd_test.go
+++ b/cmd/cmd_test.go
@@ -293,7 +293,7 @@ func TestGetModelfileName(t *testing.T) {
 			name:          "modelfile specified, no modelfile exists",
 			modelfileName: "crazyfile",
 			fileExists:    false,
-			expectedName:  "crazyfile",
+			expectedName:  "",
 			expectedErr:   os.ErrNotExist,
 		},
 		{
@@ -338,8 +338,8 @@ func TestGetModelfileName(t *testing.T) {
 					t.Fatalf("couldn't set file flag: %v", err)
 				}
 			} else {
+				expectedFilename = tt.expectedName
 				if tt.modelfileName != "" {
-					expectedFilename = tt.modelfileName
 					err := cmd.Flags().Set("file", tt.modelfileName)
 					if err != nil {
 						t.Fatalf("couldn't set file flag: %v", err)
@@ -489,3 +489,130 @@ func TestPushHandler(t *testing.T) {
 		})
 	}
 }
+
+func TestCreateHandler(t *testing.T) {
+	tests := []struct {
+		name           string
+		modelName      string
+		modelFile      string
+		serverResponse map[string]func(w http.ResponseWriter, r *http.Request)
+		expectedError  string
+		expectedOutput string
+	}{
+		{
+			name:      "successful create",
+			modelName: "test-model",
+			modelFile: "FROM foo",
+			serverResponse: map[string]func(w http.ResponseWriter, r *http.Request){
+				"/api/create": func(w http.ResponseWriter, r *http.Request) {
+					if r.Method != http.MethodPost {
+						t.Errorf("expected POST request, got %s", r.Method)
+					}
+
+					req := api.CreateRequest{}
+					if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
+						http.Error(w, err.Error(), http.StatusBadRequest)
+						return
+					}
+
+					if req.Name != "test-model" {
+						t.Errorf("expected model name 'test-model', got %s", req.Name)
+					}
+
+					if req.From != "foo" {
+						t.Errorf("expected from 'foo', got %s", req.From)
+					}
+
+					responses := []api.ProgressResponse{
+						{Status: "using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"},
+						{Status: "writing manifest"},
+						{Status: "success"},
+					}
+
+					for _, resp := range responses {
+						if err := json.NewEncoder(w).Encode(resp); err != nil {
+							http.Error(w, err.Error(), http.StatusInternalServerError)
+							return
+						}
+						w.(http.Flusher).Flush()
+					}
+				},
+			},
+			expectedOutput: "",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			mockServer := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+				handler, ok := tt.serverResponse[r.URL.Path]
+				if !ok {
+					t.Errorf("unexpected request to %s", r.URL.Path)
+					http.Error(w, "not found", http.StatusNotFound)
+					return
+				}
+				handler(w, r)
+			}))
+			t.Setenv("OLLAMA_HOST", mockServer.URL)
+			t.Cleanup(mockServer.Close)
+			tempFile, err := os.CreateTemp("", "modelfile")
+			if err != nil {
+				t.Fatal(err)
+			}
+			defer os.Remove(tempFile.Name())
+
+			if _, err := tempFile.WriteString(tt.modelFile); err != nil {
+				t.Fatal(err)
+			}
+			if err := tempFile.Close(); err != nil {
+				t.Fatal(err)
+			}
+
+			cmd := &cobra.Command{}
+			cmd.Flags().String("file", "", "")
+			if err := cmd.Flags().Set("file", tempFile.Name()); err != nil {
+				t.Fatal(err)
+			}
+
+			cmd.Flags().Bool("insecure", false, "")
+			cmd.SetContext(context.TODO())
+
+			// Redirect stderr to capture progress output
+			oldStderr := os.Stderr
+			r, w, _ := os.Pipe()
+			os.Stderr = w
+
+			// Capture stdout for the "Model pushed" message
+			oldStdout := os.Stdout
+			outR, outW, _ := os.Pipe()
+			os.Stdout = outW
+
+			err = CreateHandler(cmd, []string{tt.modelName})
+
+			// Restore stderr
+			w.Close()
+			os.Stderr = oldStderr
+			// drain the pipe
+			if _, err := io.ReadAll(r); err != nil {
+				t.Fatal(err)
+			}
+
+			// Restore stdout and get output
+			outW.Close()
+			os.Stdout = oldStdout
+			stdout, _ := io.ReadAll(outR)
+
+			if tt.expectedError == "" {
+				if err != nil {
+					t.Errorf("expected no error, got %v", err)
+				}
+
+				if tt.expectedOutput != "" {
+					if got := string(stdout); got != tt.expectedOutput {
+						t.Errorf("expected output %q, got %q", tt.expectedOutput, got)
+					}
+				}
+			}
+		})
+	}
+}
--- a/cmd/interactive.go
+++ b/cmd/interactive.go
@@ -13,11 +13,9 @@ import (
 	"strings"

 	"github.com/spf13/cobra"
-	"golang.org/x/exp/maps"

 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/readline"
 	"github.com/ollama/ollama/types/errtypes"
 )
@@ -213,10 +211,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 				return err
 			}

-			req := &api.CreateRequest{
-				Name:      args[1],
-				Modelfile: buildModelfile(opts),
-			}
+			req := NewCreateRequest(args[1], opts)
 			fn := func(resp api.ProgressResponse) error { return nil }
 			err = client.Create(cmd.Context(), req, fn)
 			if err != nil {
@@ -459,36 +454,25 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 	}
 }

-func buildModelfile(opts runOptions) string {
-	var f parser.File
-	f.Commands = append(f.Commands, parser.Command{Name: "model", Args: cmp.Or(opts.ParentModel, opts.Model)})
+func NewCreateRequest(name string, opts runOptions) *api.CreateRequest {
+	req := &api.CreateRequest{
+		Name: name,
+		From: cmp.Or(opts.ParentModel, opts.Model),
+	}

 	if opts.System != "" {
-		f.Commands = append(f.Commands, parser.Command{Name: "system", Args: opts.System})
+		req.System = opts.System
 	}

-	keys := maps.Keys(opts.Options)
-	slices.Sort(keys)
-	for _, k := range keys {
-		v := opts.Options[k]
-		var cmds []parser.Command
-		switch t := v.(type) {
-		case []string:
-			for _, s := range t {
-				cmds = append(cmds, parser.Command{Name: k, Args: s})
-			}
-		default:
-			cmds = append(cmds, parser.Command{Name: k, Args: fmt.Sprintf("%v", t)})
-		}
-
-		f.Commands = append(f.Commands, cmds...)
+	if len(opts.Options) > 0 {
+		req.Parameters = opts.Options
 	}

-	for _, msg := range opts.Messages {
-		f.Commands = append(f.Commands, parser.Command{Name: "message", Args: fmt.Sprintf("%s: %s", msg.Role, msg.Content)})
+	if len(opts.Messages) > 0 {
+		req.Messages = opts.Messages
 	}

-	return f.String()
+	return req
 }

 func normalizeFilePath(fp string) string {
--- a/cmd/interactive_test.go
+++ b/cmd/interactive_test.go
@@ -3,10 +3,7 @@ package cmd
 import (
 	"testing"

-	"github.com/google/go-cmp/cmp"
 	"github.com/stretchr/testify/assert"
-
-	"github.com/ollama/ollama/api"
 )

 func TestExtractFilenames(t *testing.T) {
@@ -53,56 +50,3 @@ d:\path with\spaces\seven.JPEG inbetween7 c:\users\jdoe\eight.png inbetween8
 	assert.Contains(t, res[9], "ten.PNG")
 	assert.Contains(t, res[9], "E:")
 }
-
-func TestModelfileBuilder(t *testing.T) {
-	opts := runOptions{
-		Model:  "hork",
-		System: "You are part horse and part shark, but all hork. Do horklike things",
-		Messages: []api.Message{
-			{Role: "user", Content: "Hey there hork!"},
-			{Role: "assistant", Content: "Yes it is true, I am half horse, half shark."},
-		},
-		Options: map[string]any{
-			"temperature":      0.9,
-			"seed":             42,
-			"penalize_newline": false,
-			"stop":             []string{"hi", "there"},
-		},
-	}
-
-	t.Run("model", func(t *testing.T) {
-		expect := `FROM hork
-SYSTEM You are part horse and part shark, but all hork. Do horklike things
-PARAMETER penalize_newline false
-PARAMETER seed 42
-PARAMETER stop hi
-PARAMETER stop there
-PARAMETER temperature 0.9
-MESSAGE user Hey there hork!
-MESSAGE assistant Yes it is true, I am half horse, half shark.
-`
-
-		actual := buildModelfile(opts)
-		if diff := cmp.Diff(expect, actual); diff != "" {
-			t.Errorf("mismatch (-want +got):\n%s", diff)
-		}
-	})
-
-	t.Run("parent model", func(t *testing.T) {
-		opts.ParentModel = "horseshark"
-		expect := `FROM horseshark
-SYSTEM You are part horse and part shark, but all hork. Do horklike things
-PARAMETER penalize_newline false
-PARAMETER seed 42
-PARAMETER stop hi
-PARAMETER stop there
-PARAMETER temperature 0.9
-MESSAGE user Hey there hork!
-MESSAGE assistant Yes it is true, I am half horse, half shark.
-`
-		actual := buildModelfile(opts)
-		if diff := cmp.Diff(expect, actual); diff != "" {
-			t.Errorf("mismatch (-want +got):\n%s", diff)
-		}
-	})
-}
--- a/convert/convert.go
+++ b/convert/convert.go
@@ -187,8 +187,12 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 		conv = &gemma2Model{}
 	case "Phi3ForCausalLM":
 		conv = &phi3Model{}
+	case "Qwen2ForCausalLM":
+		conv = &qwen2Model{}
 	case "BertModel":
 		conv = &bertModel{}
+	case "CohereForCausalLM":
+		conv = &commandrModel{}
 	default:
 		return errors.New("unsupported architecture")
 	}
--- a/convert/convert_commandr.go
+++ b/convert/convert_commandr.go
@@ -0,0 +1,76 @@
+package convert
+
+import (
+	"cmp"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type commandrModel struct {
+	ModelParameters
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	HiddenLayers          uint32  `json:"num_hidden_layers"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	LayerNormEPS          float32 `json:"layer_norm_eps"`
+	RopeTheta             float32 `json:"rope_theta"`
+	UseQKNorm             bool    `json:"use_qk_norm"`
+	MaxLength             uint32  `json:"model_max_length"`
+	LogitScale            float32 `json:"logit_scale"`
+	NCtx                  uint32  `json:"n_ctx"`
+}
+
+var _ ModelConverter = (*commandrModel)(nil)
+
+func (p *commandrModel) KV(t *Tokenizer) llm.KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "command-r"
+	kv["general.name"] = "command-r"
+	kv["command-r.context_length"] = cmp.Or(p.MaxLength, p.MaxPositionEmbeddings, p.NCtx)
+	kv["command-r.embedding_length"] = p.HiddenSize
+	kv["command-r.block_count"] = p.HiddenLayers
+	kv["command-r.feed_forward_length"] = p.IntermediateSize
+	kv["command-r.attention.head_count"] = p.NumAttentionHeads
+	kv["command-r.attention.head_count_kv"] = p.NumKeyValueHeads
+	kv["command-r.attention.layer_norm_epsilon"] = p.LayerNormEPS
+	kv["command-r.rope.freq_base"] = p.RopeTheta
+	kv["command-r.max_position_embeddings"] = cmp.Or(p.MaxLength, p.MaxPositionEmbeddings)
+	kv["command-r.logit_scale"] = p.LogitScale
+	kv["command-r.rope.scaling.type"] = "none"
+
+	return kv
+}
+
+func (p *commandrModel) Tensors(ts []Tensor) []llm.Tensor {
+	var out []llm.Tensor
+	for _, t := range ts {
+		out = append(out, llm.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *commandrModel) Replacements() []string {
+	return []string{
+		"self_attn.q_norm", "attn_q_norm",
+		"self_attn.k_norm", "attn_k_norm",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"mlp.down_proj", "ffn_down",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.up_proj", "ffn_up",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.o_proj", "attn_output",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.v_proj", "attn_v",
+		"model.norm", "output_norm",
+		"model.embed_tokens", "token_embd",
+	}
+}
--- a/convert/convert_qwen2.go
+++ b/convert/convert_qwen2.go
@@ -0,0 +1,78 @@
+package convert
+
+import "github.com/ollama/ollama/llm"
+
+type qwen2Model struct {
+	ModelParameters
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	HiddenLayers          uint32  `json:"num_hidden_layers"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	RopeTheta             float32 `json:"rope_theta"`
+	RopeScaling           struct {
+		Type                          string     `json:"type"`
+		Factor                        ropeFactor `json:"factor"`
+		OriginalMaxPositionEmbeddings uint32     `json:"original_max_position_embeddings"`
+	} `json:"rope_scaling"`
+	RMSNormEPS float32 `json:"rms_norm_eps"`
+}
+
+var _ ModelConverter = (*qwen2Model)(nil)
+
+func (q *qwen2Model) KV(t *Tokenizer) llm.KV {
+	kv := q.ModelParameters.KV(t)
+	kv["general.architecture"] = "qwen2"
+	kv["qwen2.block_count"] = q.HiddenLayers
+	kv["qwen2.context_length"] = q.MaxPositionEmbeddings
+	kv["qwen2.embedding_length"] = q.HiddenSize
+	kv["qwen2.feed_forward_length"] = q.IntermediateSize
+	kv["qwen2.attention.head_count"] = q.NumAttentionHeads
+	kv["qwen2.attention.head_count_kv"] = q.NumKeyValueHeads
+	kv["qwen2.rope.freq_base"] = q.RopeTheta
+	kv["qwen2.attention.layer_norm_rms_epsilon"] = q.RMSNormEPS
+
+	switch q.RopeScaling.Type {
+	case "":
+		// no scaling
+	case "yarn":
+		kv["qwen2.rope.scaling.type"] = q.RopeScaling.Type
+		kv["qwen2.rope.scaling.factor"] = q.RopeScaling.Factor
+	default:
+		panic("unknown rope scaling type")
+	}
+	return kv
+}
+
+func (q *qwen2Model) Tensors(ts []Tensor) []llm.Tensor {
+	var out []llm.Tensor
+	for _, t := range ts {
+		out = append(out, llm.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *qwen2Model) Replacements() []string {
+	return []string{
+		"lm_head", "output",
+		"model.embed_tokens", "token_embd",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.o_proj", "attn_output",
+		"mlp.down_proj", "ffn_down",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.up_proj", "ffn_up",
+		"post_attention_layernorm", "ffn_norm",
+		"model.norm", "output_norm",
+	}
+}
--- a/convert/convert_test.go
+++ b/convert/convert_test.go
@@ -108,6 +108,8 @@ func TestConvertModel(t *testing.T) {
 		"Phi-3-mini-128k-instruct",
 		"all-MiniLM-L6-v2",
 		"gemma-2-9b-it",
+		"Qwen2.5-0.5B-Instruct",
+		"c4ai-command-r-v01",
 	}

 	for i := range cases {
--- a/convert/testdata/Qwen2.5-0.5B-Instruct.json
+++ b/convert/testdata/Qwen2.5-0.5B-Instruct.json
@@ -0,0 +1,314 @@
+{
+    "general.architecture": "qwen2",
+    "general.file_type": "1",
+    "general.parameter_count": "494032768",
+    "general.quantization_version": "2",
+    "output_norm.weight": "93a01a6db3419e85320a244bbf8ae81c43033b1d10c342bea3797ff2ce348390",
+    "qwen2.attention.head_count": "14",
+    "qwen2.attention.head_count_kv": "2",
+    "qwen2.attention.layer_norm_rms_epsilon": "1e-06",
+    "qwen2.block_count": "24",
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+  }
--- a/convert/testdata/c4ai-command-r-v01.json
+++ b/convert/testdata/c4ai-command-r-v01.json
@@ -0,0 +1,344 @@
+{
+    "general.architecture": "command-r",
+    "general.name": "command-r",
+    "command-r.attention.head_count": "64",
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+    "blk.33.attn_norm.weight": "0251200c24cc8445607ace6dc8c5aa0566567997262b7cca53a11ac23cc564b2",
+    "blk.33.attn_output.weight": "b2423205bdf6a1096d43c44d8d12f1a84fcd4e1bb70fcf6dc8542b8b8a71a13c",
+    "blk.33.attn_q.weight": "00b425c3ef71065ce5e0234e702bf38143b4952da78a85f52ab2c2e3073d97ab",
+    "blk.33.attn_v.weight": "035edd2335df816c42c765a5e66b9d9b9e15a822a8dc1863508145499c942c14",
+    "blk.33.ffn_down.weight": "4894a923a3db75bae4496ba3ce5f28796ad31fe33996a066271fb8654964310e",
+    "blk.33.ffn_gate.weight": "8f6c819b8bbfbe3357fae89e1ac5a3d58be85b3b04be3bacf7b62775869046ff",
+    "blk.33.ffn_up.weight": "257c3544b5b544fd5d839665bf5caf107a329b59dbc3751efcaa24ae63c56179",
+    "blk.34.attn_k.weight": "b6cd8bba892e38dac4a2ebc3ba1bce49e71b967fc436fde30c6d76f54a18935f",
+    "blk.34.attn_norm.weight": "2b3c8e60a064cba9955752bbbbdd92c71ba5c2f1bd721097bdbe88b5abc68787",
+    "blk.34.attn_output.weight": "8cc272551c9aaca9db5a660c6927bab94a0243d74a30b2bc165f06bd577714ea",
+    "blk.34.attn_q.weight": "74b561eb4792484e6a94b58fe2583848c3ae28ff2f1bf3d02939a0cfdfa49990",
+    "blk.34.attn_v.weight": "dba19e24ff05154dc5a1f55c023729303a583d13d68732ce22ea74d4410dc8f0",
+    "blk.34.ffn_down.weight": "76eca5dfeb274c35774e0bf9f22ee420ed9085c8e99aa2cd5a236e4918b44c61",
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+    "blk.34.ffn_up.weight": "2a03126bf73587eaba99ece2066103d12e47bcd4ce30ff6c17b2f383b81d40df",
+    "blk.35.attn_k.weight": "52513fc0cd4e997a842729af7d21dd09399bce0a339558374738be266d0fa2f0",
+    "blk.35.attn_norm.weight": "e5281fa911964263ccf1630b14762edbd41d0b9472d6ec695fc600fed4892c35",
+    "blk.35.attn_output.weight": "b391d6705d5dc6f48326b5fd16573f679edf64109d86fb729a498819676590ca",
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+    "blk.35.attn_v.weight": "9d8cdf23ffdb0c5c74106843390b94b24c9f33ef0eb9998d39f78c73390101ea",
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+    "blk.35.ffn_up.weight": "1c4330d9dc71bf4c98812c34356c51f520f47610a534152aa6d29284b758090d",
+    "blk.36.attn_k.weight": "ef720655e5ca2465f13db2dfc4732fb4ef2c9d53acde52f514fd4f301e974081",
+    "blk.36.attn_norm.weight": "88f4b9310b3c8c2644e3029160cd35678c79dfa59280430e03f5c29a6fe84a58",
+    "blk.36.attn_output.weight": "aec6f915fffd7bb72cd783273e871b4f09605950089d45e72059d1316b6c4b01",
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+    "blk.37.attn_q.weight": "e38c0462aff139afcbab289189823527e453abc9e541154adde5e7af88cacf0b",
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+    "blk.37.ffn_up.weight": "d2d2ba5cfc4ef47338dd7384219e22bf030a5a2209e0354d88f5bbaaafd20e87",
+    "blk.38.attn_k.weight": "abc4bb189dedf7ce661e79028427623a4f91ac091c2cd60e31b58bc62b1cda71",
+    "blk.38.attn_norm.weight": "9f4803a7d03fd40fcb83d85f84eb1d5682ea4e5bb084f210c02850675d804c3d",
+    "blk.38.attn_output.weight": "77cb66007f1a41df7135d0e7f900ceb499c2f667dfc3f1a6ac01a3203bbd3ccf",
+    "blk.38.attn_q.weight": "d94a8b26cd375bf2bcaa76597e314aa8268ee50a479d00931e5e0e021feadb5d",
+    "blk.38.attn_v.weight": "660c907888bc5016dc69b7d35fe6f55c7ded697c93be0e2d332a2f17aff88758",
+    "blk.38.ffn_down.weight": "6f06173bae5b00ffaf88ef383619a8b9c6a8d0d5c6494695d17f6c1de1a68a13",
+    "blk.38.ffn_gate.weight": "89f99be149d03f116527bfcabe073c50001c874de40fb6e817f6619027f3cd05",
+    "blk.38.ffn_up.weight": "8d57557c8d5e2d2688b73f01dddf1ce8d5194990cda6358153320aea88aac7f8",
+    "blk.39.attn_k.weight": "21be09c988b46c8393e6c2ec9230f3b5136eb7607dd1953ba92d0811c2f0dd75",
+    "blk.39.attn_norm.weight": "ba7c1912dd1c4e2d16917201f62396fd0600e4a451137eaddff255548c209abd",
+    "blk.39.attn_output.weight": "acfaf4abb3fd27fd899b5563c3877f176b597d8f6cdb2f2fd3f3a0bd4da15ed6",
+    "blk.39.attn_q.weight": "e8adbc140d4c8f0db2a27ca584c5531d5b1e080555fe627e34d80d0814a92bed",
+    "blk.39.attn_v.weight": "92f96b0e1f724e73a0f90a76c145654418844c04a6d4b14c05eb5af8a62bf8dc",
+    "blk.39.ffn_down.weight": "4d9ee7c65fc16fe95d10c47b79ac6a525741947600a64b5fcea5d300a82c50de",
+    "blk.39.ffn_gate.weight": "7e18507989f39b32191133d2657c2ee3b74f42f070579204d727eb72215793d1",
+    "blk.39.ffn_up.weight": "22cda752269c9757ba918abede1df95bb0f83a5c772dea13c8deea3d5f2723d9",
+    "output_norm.weight": "2858cf0e39d32caf52b7861378ace076000241e147f10b9eb21d8a5cd149e3cb"
+}
--- a/convert/tokenizer.go
+++ b/convert/tokenizer.go
@@ -100,6 +100,8 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
 			t.Pre = "deepseek-llm"
 		case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
 			t.Pre = "deepseek-coder"
+		case "1ff7f41064896984db5d1bb6ff64fa4bc29007d08c1b439e505b7392777a319e":
+			t.Pre = "qwen2"
 		case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
 			// noop, empty pretokenizer
 		default:
--- a/discover/gpu.go
+++ b/discover/gpu.go
@@ -542,7 +542,6 @@ func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
 	patterns = append(patterns, defaultPatterns...)
 	slog.Debug("gpu library search", "globs", patterns)
 	for _, pattern := range patterns {
-
 		// Nvidia PhysX known to return bogus results
 		if strings.Contains(pattern, "PhysX") {
 			slog.Debug("skipping PhysX cuda library path", "path", pattern)
--- a/docs/README.md
+++ b/docs/README.md
@@ -2,7 +2,7 @@

 ### Getting Started
 * [Quickstart](../README.md#quickstart)
-* [Examples](../examples)
+* [Examples](./examples.md)
 * [Importing models](./import.md)
 * [Linux Documentation](./linux.md)
 * [Windows Documentation](./windows.md)
--- a/docs/api.md
+++ b/docs/api.md
@@ -13,6 +13,7 @@
 - [Push a Model](#push-a-model)
 - [Generate Embeddings](#generate-embeddings)
 - [List Running Models](#list-running-models)
+- [Version](#version)

 ## Conventions

@@ -927,14 +928,25 @@ A single JSON object is returned:
 POST /api/create
 ```

-Create a model from a [`Modelfile`](./modelfile.md). It is recommended to set `modelfile` to the content of the Modelfile rather than just set `path`. This is a requirement for remote create. Remote model creation must also create any file blobs, fields such as `FROM` and `ADAPTER`, explicitly with the server using [Create a Blob](#create-a-blob) and the value to the path indicated in the response.
+Create a model from:
+ * another model;
+ * a safetensors directory; or
+ * a GGUF file.
+
+If you are creating a model from a safetensors directory or from a GGUF file, you must [create a blob](#create-a-blob) for each of the files and then use the file name and SHA256 digest associated with each blob in the `files` field.

 ### Parameters

 - `model`: name of the model to create
- `modelfile` (optional): contents of the Modelfile
+- `from`: (optional) name of an existing model to create the new model from
+- `files`: (optional) a dictionary of file names to SHA256 digests of blobs to create the model from
+- `adapters`: (optional) a dictionary of file names to SHA256 digests of blobs for LORA adapters
+- `template`: (optional) the prompt template for the model
+- `license`: (optional) a string or list of strings containing the license or licenses for the model
+- `system`: (optional) a string containing the system prompt for the model
+- `parameters`: (optional) a dictionary of parameters for the model (see [Modelfile](./modelfile.md#valid-parameters-and-values) for a list of parameters)
+- `messages`: (optional) a list of message objects used to create a conversation
 - `stream`: (optional) if `false` the response will be returned as a single response object, rather than a stream of objects
- `path` (optional): path to the Modelfile
 - `quantize` (optional): quantize a non-quantized (e.g. float16) model

 #### Quantization types
@@ -960,14 +972,15 @@ Create a model from a [`Modelfile`](./modelfile.md). It is recommended to set `m

 #### Create a new model

-Create a new model from a `Modelfile`.
+Create a new model from an existing model.

 ##### Request

 ```shell
 curl http://localhost:11434/api/create -d '{
  "model": "mario",
-  "modelfile": "FROM llama3\nSYSTEM You are mario from Super Mario Bros."
+  "from": "llama3.2",
+  "system": "You are Mario from Super Mario Bros."
 }'
 ```

@@ -998,7 +1011,7 @@ Quantize a non-quantized model.
 ```shell
 curl http://localhost:11434/api/create -d '{
  "model": "llama3.1:quantized",
-  "modelfile": "FROM llama3.1:8b-instruct-fp16",
+  "from": "llama3.1:8b-instruct-fp16",
  "quantize": "q4_K_M"
 }'
 ```
@@ -1018,52 +1031,112 @@ A stream of JSON objects is returned:
 {"status":"success"}
 ```

+#### Create a model from GGUF

-### Check if a Blob Exists
+Create a model from a GGUF file. The `files` parameter should be filled out with the file name and SHA256 digest of the GGUF file you wish to use. Use [/api/blobs/:digest](#push-a-blob) to push the GGUF file to the server before calling this API.
+
+
+##### Request
+
+```shell
+curl http://localhost:11434/api/create -d '{
+  "model": "my-gguf-model",
+  "files": {
+    "test.gguf": "sha256:432f310a77f4650a88d0fd59ecdd7cebed8d684bafea53cbff0473542964f0c3"
+  }
+}'
+```
+
+##### Response
+
+A stream of JSON objects is returned:
+
+```
+{"status":"parsing GGUF"}
+{"status":"using existing layer sha256:432f310a77f4650a88d0fd59ecdd7cebed8d684bafea53cbff0473542964f0c3"}
+{"status":"writing manifest"}
+{"status":"success"}
+```
+
+
+#### Create a model from a Safetensors directory
+
+The `files` parameter should include a dictionary of files for the safetensors model which includes the file names and SHA256 digest of each file. Use [/api/blobs/:digest](#push-a-blob) to first push each of the files to the server before calling this API. Files will remain in the cache until the Ollama server is restarted.
+
+##### Request
+
+```shell
+curl http://localhost:11434/api/create -d '{
+  "model": "fred",
+  "files": {
+    "config.json": "sha256:dd3443e529fb2290423a0c65c2d633e67b419d273f170259e27297219828e389",
+    "generation_config.json": "sha256:88effbb63300dbbc7390143fbbdd9d9fa50587b37e8bfd16c8c90d4970a74a36",
+    "special_tokens_map.json": "sha256:b7455f0e8f00539108837bfa586c4fbf424e31f8717819a6798be74bef813d05",
+    "tokenizer.json": "sha256:bbc1904d35169c542dffbe1f7589a5994ec7426d9e5b609d07bab876f32e97ab",
+    "tokenizer_config.json": "sha256:24e8a6dc2547164b7002e3125f10b415105644fcf02bf9ad8b674c87b1eaaed6",
+    "model.safetensors": "sha256:1ff795ff6a07e6a68085d206fb84417da2f083f68391c2843cd2b8ac6df8538f"
+  }
+}'
+```
+
+##### Response
+
+A stream of JSON objects is returned:
+
+```shell
+{"status":"converting model"}
+{"status":"creating new layer sha256:05ca5b813af4a53d2c2922933936e398958855c44ee534858fcfd830940618b6"}
+{"status":"using autodetected template llama3-instruct"}
+{"status":"using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"}
+{"status":"writing manifest"}
+{"status":"success"}
+```
+
+## Check if a Blob Exists

 ```shell
 HEAD /api/blobs/:digest
 ```

-Ensures that the file blob used for a FROM or ADAPTER field exists on the server. This is checking your Ollama server and not ollama.com.
+Ensures that the file blob (Binary Large Object) used with create a model exists on the server. This checks your Ollama server and not ollama.com.

-#### Query Parameters
+### Query Parameters

 - `digest`: the SHA256 digest of the blob

-#### Examples
+### Examples

-##### Request
+#### Request

 ```shell
 curl -I http://localhost:11434/api/blobs/sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2
 ```

-##### Response
+#### Response

 Return 200 OK if the blob exists, 404 Not Found if it does not.

-### Create a Blob
+## Push a Blob

 ```shell
 POST /api/blobs/:digest
 ```

-Create a blob from a file on the server. Returns the server file path.
+Push a file to the Ollama server to create a "blob" (Binary Large Object).

-#### Query Parameters
+### Query Parameters

 - `digest`: the expected SHA256 digest of the file

-#### Examples
+### Examples

-##### Request
+#### Request

 ```shell
-curl -T model.bin -X POST http://localhost:11434/api/blobs/sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2
+curl -T model.gguf -X POST http://localhost:11434/api/blobs/sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2
 ```

-##### Response
+#### Response

 Return 201 Created if the blob was successfully created, 400 Bad Request if the digest used is not expected.

@@ -1526,3 +1599,29 @@ curl http://localhost:11434/api/embeddings -d '{
  ]
 }
 ```
+
+## Version
+
+```shell
+GET /api/version
+```
+
+Retrieve the Ollama version
+
+### Examples
+
+#### Request
+
+```shell
+curl http://localhost:11434/api/version
+```
+
+#### Response
+
+```json
+{
+  "version": "0.5.1"
+}
+```
+
+
--- a/examples/README.md
+++ b/examples/README.md
@@ -12,3 +12,9 @@ Ollama JavaScript examples at [ollama-js/examples](https://github.com/ollama/oll

 ## OpenAI compatibility examples
 Ollama OpenAI compatibility examples at [ollama/examples/openai](../docs/openai.md)
+
+
+## Community examples
+
+- [LangChain Ollama Python](https://python.langchain.com/docs/integrations/chat/ollama/)
+- [LangChain Ollama JS](https://js.langchain.com/docs/integrations/chat/ollama/)
--- a/docs/gpu.md
+++ b/docs/gpu.md
@@ -38,7 +38,7 @@ Numeric IDs may be used, however ordering may vary, so UUIDs are more reliable.
 You can discover the UUID of your GPUs by running `nvidia-smi -L` If you want to
 ignore the GPUs and force CPU usage, use an invalid GPU ID (e.g., "-1")

-### Laptop Suspend Resume
+### Linux Suspend Resume

 On linux, after a suspend/resume cycle, sometimes Ollama will fail to discover
 your NVIDIA GPU, and fallback to running on the CPU.  You can workaround this
--- a/docs/modelfile.md
+++ b/docs/modelfile.md
@@ -67,8 +67,6 @@ To use this:
 3. `ollama run choose-a-model-name`
 4. Start using the model!

-More examples are available in the [examples directory](../examples).
-
 To view the Modelfile of a given model, use the `ollama show --modelfile` command.

  ```bash
@@ -155,7 +153,6 @@ PARAMETER <parameter> <parametervalue>
 | temperature    | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)                                                                                                                                     | float      | temperature 0.7      |
 | seed           | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0)                                                                                       | int        | seed 42              |
 | stop           | Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate `stop` parameters in a modelfile.                                      | string     | stop "AI assistant:" |
-| tfs_z          | Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)                                               | float      | tfs_z 1              |
 | num_predict    | Maximum number of tokens to predict when generating text. (Default: -1, infinite generation)                                                                                                                                   | int        | num_predict 42       |
 | top_k          | Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)                                                                        | int        | top_k 40             |
 | top_p          | Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)                                                                 | float      | top_p 0.9            |
--- a/docs/openai.md
+++ b/docs/openai.md
@@ -204,6 +204,45 @@ curl http://localhost:11434/v1/embeddings \
    }'
 ```

+## Extra arguments
+
+### Setting context length
+- `context_length` parameter can be used to set the context length for the model
+
+#### OpenAI python library 
+- OpenAI python library does not support setting context length, however this can be set for Ollama through the `extra_body` parameter
+
+```py
+completion = client.chat.completions.create(
+    model="llama3.1:8b",
+    messages=[{"role": "user", "content": "Say this is a test"}],
+    extra_body={"context_length": 4096},
+)
+```
+
+#### OpenAI JavaScript library
+- OpenAI JavaScript library does not support setting context length, however this can be set for Ollama by passing `context_length` directly with a `@ts-expect-error` as an undocumented parameter in the OpenAI JavaScript library. [See documentation here](https://github.com/openai/openai-node?tab=readme-ov-file#making-customundocumented-requests)
+
+```ts
+const chatCompletion = await openai.chat.completions.create({
+    messages: [{ role: 'user', content: 'Say this is a test' }],
+    model: 'llama3.2',
+    // @ts-expect-error context_length is an additional parameter 
+    context_length: 4096,
+})
+```
+
+#### `curl`
+```shell
+curl http://localhost:11434/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "llama3.2",
+        "messages": [{"role": "user", "content": "Say this is a test"}],
+        "context_length": 4096
+    }'
+```
+
 ## Endpoints

 ### `/v1/chat/completions`
@@ -213,6 +252,7 @@ curl http://localhost:11434/v1/embeddings \
 - [x] Chat completions
 - [x] Streaming
 - [x] JSON mode
+- [x] Structured outputs
 - [x] Reproducible outputs
 - [x] Vision
 - [x] Tools
@@ -233,6 +273,8 @@ curl http://localhost:11434/v1/embeddings \
 - [x] `seed`
 - [x] `stop`
 - [x] `stream`
+- [x] `stream_options`
+  - [x] `include_usage`
 - [x] `temperature`
 - [x] `top_p`
 - [x] `max_tokens`
@@ -261,6 +303,8 @@ curl http://localhost:11434/v1/embeddings \
 - [x] `seed`
 - [x] `stop`
 - [x] `stream`
+- [x] `stream_options`
+  - [x] `include_usage`
 - [x] `temperature`
 - [x] `top_p`
 - [x] `max_tokens`
@@ -335,27 +379,3 @@ curl http://localhost:11434/v1/chat/completions \
    }'
 ```

-### Setting the context size
-
-The OpenAI API does not have a way of setting the context size for a model. If you need to change the context size, create a `Modelfile` which looks like:
-
-```modelfile
-FROM <some model>
-PARAMETER num_ctx <context size>
-```
-
-Use the `ollama create mymodel` command to create a new model with the updated context size. Call the API with the updated model name:
-
-```shell
-curl http://localhost:11434/v1/chat/completions \
-    -H "Content-Type: application/json" \
-    -d '{
-        "model": "mymodel",
-        "messages": [
-            {
-                "role": "user",
-                "content": "Hello!"
-            }
-        ]
-    }'
-```
--- a/docs/template.md
+++ b/docs/template.md
@@ -111,7 +111,7 @@ Keep the following tips and best practices in mind when working with Go template

 ChatML is a popular template format. It can be used for models such as Databrick's DBRX, Intel's Neural Chat, and Microsoft's Orca 2.

-```gotmpl
+```go
 {{- range .Messages }}<|im_start|>{{ .Role }}
 {{ .Content }}<|im_end|>
 {{ end }}<|im_start|>assistant
@@ -125,7 +125,7 @@ Tools support can be added to a model by adding a `{{ .Tools }}` node to the tem

 Mistral v0.3 and Mixtral 8x22B supports tool calling.

-```gotmpl
+```go
 {{- range $index, $_ := .Messages }}
 {{- if eq .Role "user" }}
 {{- if and (le (len (slice $.Messages $index)) 2) $.Tools }}[AVAILABLE_TOOLS] {{ json $.Tools }}[/AVAILABLE_TOOLS]
@@ -151,7 +151,7 @@ Fill-in-middle support can be added to a model by adding a `{{ .Suffix }}` node

 CodeLlama [7B](https://ollama.com/library/codellama:7b-code) and [13B](https://ollama.com/library/codellama:13b-code) code completion models support fill-in-middle.

-```gotmpl
+```go
 <PRE> {{ .Prompt }} <SUF>{{ .Suffix }} <MID>
 ```

--- a/docs/tutorials.md
+++ b/docs/tutorials.md
@@ -1,9 +0,0 @@
-# Tutorials
-
-Here is a list of ways you can use Ollama with other tools to build interesting applications.
-
- [Using LangChain with Ollama in JavaScript](./tutorials/langchainjs.md)
- [Using LangChain with Ollama in Python](./tutorials/langchainpy.md)
- [Running Ollama on NVIDIA Jetson Devices](./tutorials/nvidia-jetson.md)
-
-Also be sure to check out the [examples](../examples) directory for more ways to use Ollama.
--- a/examples/.gitignore
+++ b/examples/.gitignore
@@ -1,174 +0,0 @@
-node_modules
-bun.lockb
-.vscode
-# OSX
-.DS_STORE
-
-
-# Models
-models/
-
-# Local Chroma db
-.chroma/
-db/
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
--- a/examples/flyio/.gitignore
+++ b/examples/flyio/.gitignore
@@ -1 +0,0 @@
-fly.toml
--- a/examples/flyio/README.md
+++ b/examples/flyio/README.md
@@ -1,67 +0,0 @@
-# Deploy Ollama to Fly.io
-
-> Note: this example exposes a public endpoint and does not configure authentication. Use with care.
-
-## Prerequisites
-
- Ollama: https://ollama.com/download
- Fly.io account. Sign up for a free account: https://fly.io/app/sign-up
-
-## Steps
-
-1. Login to Fly.io
-
-    ```bash
-    fly auth login
-    ```
-
-1. Create a new Fly app
-
-    ```bash
-    fly launch --name <name> --image ollama/ollama --internal-port 11434 --vm-size shared-cpu-8x --now
-    ```
-
-1. Pull and run `orca-mini:3b`
-
-    ```bash
-    OLLAMA_HOST=https://<name>.fly.dev ollama run orca-mini:3b
-    ```
-
-`shared-cpu-8x` is a free-tier eligible machine type. For better performance, switch to a `performance` or `dedicated` machine type or attach a GPU for hardware acceleration (see below).
-
-## (Optional) Persistent Volume
-
-By default Fly Machines use ephemeral storage which is problematic if you want to use the same model across restarts without pulling it again. Create and attach a persistent volume to store the downloaded models:
-
-1. Create the Fly Volume
-
-    ```bash
-    fly volume create ollama
-    ```
-
-1. Update `fly.toml` and add `[mounts]`
-
-    ```toml
-    [mounts]
-      source = "ollama"
-      destination = "/mnt/ollama/models"
-    ```
-
-1. Update `fly.toml` and add `[env]`
-
-    ```toml
-    [env]
-      OLLAMA_MODELS = "/mnt/ollama/models"
-    ```
-
-1. Deploy your app
-
-    ```bash
-    fly deploy
-    ```
-
-## (Optional) Hardware Acceleration
-
-Fly.io GPU is currently in waitlist. Sign up for the waitlist: https://fly.io/gpu
-
-Once you've been accepted, create the app with the additional flags `--vm-gpu-kind a100-pcie-40gb` or `--vm-gpu-kind a100-pcie-80gb`.
--- a/examples/go-http-generate/main.go
+++ b/examples/go-http-generate/main.go
@@ -1,29 +0,0 @@
-package main
-
-import (
-	"bytes"
-	"fmt"
-	"io"
-	"log"
-	"net/http"
-	"os"
-)
-
-func main() {
-	body := []byte(`{"model":"mistral"}`)
-	resp, err := http.Post("http://localhost:11434/api/generate", "application/json", bytes.NewBuffer(body))
-
-	if err != nil {
-		fmt.Print(err.Error())
-		os.Exit(1)
-	}
-
-	defer resp.Body.Close()
-
-	responseData, err := io.ReadAll(resp.Body)
-	if err != nil {
-		log.Fatal(err)
-	}
-	fmt.Println(string(responseData))
-
-}
--- a/examples/jupyter-notebook/README.md
+++ b/examples/jupyter-notebook/README.md
@@ -1,5 +0,0 @@
-# Ollama Jupyter Notebook
-
-This example downloads and installs Ollama in a Jupyter instance such as Google Colab. It will start the Ollama service and expose an endpoint using `ngrok` which can be used to communicate with the Ollama instance remotely.
-
-For best results, use an instance with GPU accelerator.
--- a/examples/jupyter-notebook/ollama.ipynb
+++ b/examples/jupyter-notebook/ollama.ipynb
@@ -1,102 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "93f59dcb-c588-41b8-a792-55d88ade739c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Download and run the Ollama Linux install script\n",
-    "!curl -fsSL https://ollama.com/install.sh | sh\n",
-    "!command -v systemctl >/dev/null && sudo systemctl stop ollama"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "658c147e-c7f8-490e-910e-62b80f577dda",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install aiohttp pyngrok\n",
-    "\n",
-    "import os\n",
-    "import asyncio\n",
-    "from aiohttp import ClientSession\n",
-    "\n",
-    "# Set LD_LIBRARY_PATH so the system NVIDIA library becomes preferred\n",
-    "# over the built-in library. This is particularly important for \n",
-    "# Google Colab which installs older drivers\n",
-    "os.environ.update({'LD_LIBRARY_PATH': '/usr/lib64-nvidia'})\n",
-    "\n",
-    "async def run(cmd):\n",
-    "  '''\n",
-    "  run is a helper function to run subcommands asynchronously.\n",
-    "  '''\n",
-    "  print('>>> starting', *cmd)\n",
-    "  p = await asyncio.subprocess.create_subprocess_exec(\n",
-    "      *cmd,\n",
-    "      stdout=asyncio.subprocess.PIPE,\n",
-    "      stderr=asyncio.subprocess.PIPE,\n",
-    "  )\n",
-    "\n",
-    "  async def pipe(lines):\n",
-    "    async for line in lines:\n",
-    "      print(line.strip().decode('utf-8'))\n",
-    "\n",
-    "  await asyncio.gather(\n",
-    "      pipe(p.stdout),\n",
-    "      pipe(p.stderr),\n",
-    "  )\n",
-    "\n",
-    "\n",
-    "await asyncio.gather(\n",
-    "    run(['ollama', 'serve']),\n",
-    "    run(['ngrok', 'http', '--log', 'stderr', '11434']),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7735a55-9aad-4caf-8683-52e2163ba53b",
-   "metadata": {},
-   "source": [
-    "The previous cell starts two processes, `ollama` and `ngrok`. The log output will show a line like the following which describes the external address.\n",
-    "\n",
-    "```\n",
-    "t=2023-11-12T22:55:56+0000 lvl=info msg=\"started tunnel\" obj=tunnels name=command_line addr=http://localhost:11434 url=https://8249-34-125-179-11.ngrok.io\n",
-    "```\n",
-    "\n",
-    "The external address in this case is `https://8249-34-125-179-11.ngrok.io` which can be passed into `OLLAMA_HOST` to access this instance.\n",
-    "\n",
-    "```bash\n",
-    "export OLLAMA_HOST=https://8249-34-125-179-11.ngrok.io\n",
-    "ollama list\n",
-    "ollama run mistral\n",
-    "```"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--- a/examples/kubernetes/README.md
+++ b/examples/kubernetes/README.md
@@ -1,38 +0,0 @@
-# Deploy Ollama to Kubernetes
-
-## Prerequisites
-
- Ollama: https://ollama.com/download
- Kubernetes cluster. This example will use Google Kubernetes Engine.
-
-## Steps
-
-1. Create the Ollama namespace, deployment, and service
-
-   ```bash
-   kubectl apply -f cpu.yaml
-   ```
-
-## (Optional) Hardware Acceleration
-
-Hardware acceleration in Kubernetes requires NVIDIA's [`k8s-device-plugin`](https://github.com/NVIDIA/k8s-device-plugin) which is deployed in Kubernetes in form of daemonset. Follow the link for more details.
-
-Once configured, create a GPU enabled Ollama deployment.
-
-```bash
-kubectl apply -f gpu.yaml
-```
-
-## Test
-
-1. Port forward the Ollama service to connect and use it locally
-
-   ```bash
-   kubectl -n ollama port-forward service/ollama 11434:80
-   ```
-
-1. Pull and run a model, for example `orca-mini:3b`
-
-   ```bash
-   ollama run orca-mini:3b
-   ```
--- a/examples/kubernetes/cpu.yaml
+++ b/examples/kubernetes/cpu.yaml
@@ -1,42 +0,0 @@
---
-apiVersion: v1
-kind: Namespace
-metadata:
-  name: ollama
---
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: ollama
-  namespace: ollama
-spec:
-  selector:
-    matchLabels:
-      name: ollama
-  template:
-    metadata:
-      labels:
-        name: ollama
-    spec:
-      containers:
-      - name: ollama
-        image: ollama/ollama:latest
-        ports:
-        - name: http
-          containerPort: 11434
-          protocol: TCP
---
-apiVersion: v1
-kind: Service
-metadata:
-  name: ollama
-  namespace: ollama
-spec:
-  type: ClusterIP
-  selector:
-    name: ollama
-  ports:
-  - port: 80
-    name: http
-    targetPort: http
-    protocol: TCP
--- a/examples/kubernetes/gpu.yaml
+++ b/examples/kubernetes/gpu.yaml
@@ -1,58 +0,0 @@
---
-apiVersion: v1
-kind: Namespace
-metadata:
-  name: ollama
---
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: ollama
-  namespace: ollama
-spec:
-  strategy:
-    type: Recreate
-  selector:
-    matchLabels:
-      name: ollama
-  template:
-    metadata:
-      labels:
-        name: ollama
-    spec:
-      containers:
-      - name: ollama
-        image: ollama/ollama:latest
-        env:
-        - name: PATH
-          value: /usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
-        - name: LD_LIBRARY_PATH
-          value: /usr/local/nvidia/lib:/usr/local/nvidia/lib64
-        - name: NVIDIA_DRIVER_CAPABILITIES
-          value: compute,utility
-        ports:
-        - name: http
-          containerPort: 11434
-          protocol: TCP
-        resources:
-          limits:
-            nvidia.com/gpu: 1
-      tolerations:
-      - key: nvidia.com/gpu
-        operator: Exists
-        effect: NoSchedule
---
-apiVersion: v1
-kind: Service
-metadata:
-  name: ollama
-  namespace: ollama
-spec:
-  type: ClusterIP
-  selector:
-    name: ollama
-  ports:
-  - port: 80
-    name: http
-    targetPort: http
-    protocol: TCP
--- a/examples/langchain-python-rag-document/README.md
+++ b/examples/langchain-python-rag-document/README.md
@@ -1,29 +0,0 @@
-# LangChain Document QA
-
-This example provides an interface for asking questions to a PDF document.
-
-## Setup
-
-1. Ensure you have the `llama3.2` model installed:
-
-```
-ollama pull llama3.2
-```
-
-2. Install the Python Requirements.
-
-```
-pip install -r requirements.txt
-```
-
-## Run
-
-```
-python main.py
-```
-
-A prompt will appear, where questions may be asked:
-
-```
-Query: How many locations does WeWork have?
-```
--- a/examples/langchain-python-rag-document/main.py
+++ b/examples/langchain-python-rag-document/main.py
@@ -1,61 +0,0 @@
-from langchain.document_loaders import OnlinePDFLoader
-from langchain.vectorstores import Chroma
-from langchain.embeddings import GPT4AllEmbeddings
-from langchain import PromptTemplate
-from langchain.llms import Ollama
-from langchain.callbacks.manager import CallbackManager
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-from langchain.chains import RetrievalQA
-import sys
-import os
-
-class SuppressStdout:
-    def __enter__(self):
-        self._original_stdout = sys.stdout
-        self._original_stderr = sys.stderr
-        sys.stdout = open(os.devnull, 'w')
-        sys.stderr = open(os.devnull, 'w')
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        sys.stdout.close()
-        sys.stdout = self._original_stdout
-        sys.stderr = self._original_stderr
-
-# load the pdf and split it into chunks
-loader = OnlinePDFLoader("https://d18rn0p25nwr6d.cloudfront.net/CIK-0001813756/975b3e9b-268e-4798-a9e4-2a9a7c92dc10.pdf")
-data = loader.load()
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
-all_splits = text_splitter.split_documents(data)
-
-with SuppressStdout():
-    vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())
-
-while True:
-    query = input("\nQuery: ")
-    if query == "exit":
-        break
-    if query.strip() == "":
-        continue
-
-    # Prompt
-    template = """Use the following pieces of context to answer the question at the end.
-    If you don't know the answer, just say that you don't know, don't try to make up an answer.
-    Use three sentences maximum and keep the answer as concise as possible.
-    {context}
-    Question: {question}
-    Helpful Answer:"""
-    QA_CHAIN_PROMPT = PromptTemplate(
-        input_variables=["context", "question"],
-        template=template,
-    )
-
-    llm = Ollama(model="llama3.2", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
-    qa_chain = RetrievalQA.from_chain_type(
-        llm,
-        retriever=vectorstore.as_retriever(),
-        chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},
-    )
-
-    result = qa_chain({"query": query})
--- a/examples/langchain-python-rag-document/requirements.txt
+++ b/examples/langchain-python-rag-document/requirements.txt
@@ -1,109 +0,0 @@
-absl-py==1.4.0
-aiohttp==3.8.5
-aiosignal==1.3.1
-anyio==3.7.1
-astunparse==1.6.3
-async-timeout==4.0.3
-attrs==23.1.0
-backoff==2.2.1
-beautifulsoup4==4.12.2
-bs4==0.0.1
-cachetools==5.3.1
-certifi==2023.7.22
-cffi==1.15.1
-chardet==5.2.0
-charset-normalizer==3.2.0
-Chroma==0.2.0
-chroma-hnswlib==0.7.2
-chromadb==0.4.5
-click==8.1.6
-coloredlogs==15.0.1
-cryptography==41.0.3
-dataclasses-json==0.5.14
-fastapi==0.99.1
-filetype==1.2.0
-flatbuffers==23.5.26
-frozenlist==1.4.0
-gast==0.4.0
-google-auth==2.22.0
-google-auth-oauthlib==1.0.0
-google-pasta==0.2.0
-gpt4all==1.0.8
-grpcio==1.57.0
-h11==0.14.0
-h5py==3.9.0
-httptools==0.6.0
-humanfriendly==10.0
-idna==3.4
-importlib-resources==6.0.1
-joblib==1.3.2
-keras==2.13.1
-langchain==0.0.261
-langsmith==0.0.21
-libclang==16.0.6
-lxml==4.9.3
-Markdown==3.4.4
-MarkupSafe==2.1.3
-marshmallow==3.20.1
-monotonic==1.6
-mpmath==1.3.0
-multidict==6.0.4
-mypy-extensions==1.0.0
-nltk==3.8.1
-numexpr==2.8.5
-numpy==1.24.3
-oauthlib==3.2.2
-onnxruntime==1.15.1
-openapi-schema-pydantic==1.2.4
-opt-einsum==3.3.0
-overrides==7.4.0
-packaging==23.1
-pdf2image==1.16.3
-pdfminer==20191125
-pdfminer.six==20221105
-Pillow==10.0.0
-posthog==3.0.1
-protobuf==4.24.0
-pulsar-client==3.2.0
-pyasn1==0.5.0
-pyasn1-modules==0.3.0
-pycparser==2.21
-pycryptodome==3.18.0
-pydantic==1.10.12
-PyPika==0.48.9
-python-dateutil==2.8.2
-python-dotenv==1.0.0
-python-magic==0.4.27
-PyYAML==6.0.1
-regex==2023.8.8
-requests==2.31.0
-requests-oauthlib==1.3.1
-rsa==4.9
-six==1.16.0
-sniffio==1.3.0
-soupsieve==2.4.1
-SQLAlchemy==2.0.19
-starlette==0.27.0
-sympy==1.12
-tabulate==0.9.0
-tenacity==8.2.2
-tensorboard==2.13.0
-tensorboard-data-server==0.7.1
-tensorflow==2.13.0
-tensorflow-estimator==2.13.0
-tensorflow-hub==0.14.0
-tensorflow-macos==2.13.0
-termcolor==2.3.0
-tokenizers==0.13.3
-tqdm==4.66.1
-typing-inspect==0.9.0
-typing_extensions==4.5.0
-unstructured==0.9.2
-urllib3==1.26.16
-uvicorn==0.23.2
-uvloop==0.17.0
-watchfiles==0.19.0
-websockets==11.0.3
-Werkzeug==2.3.6
-wrapt==1.15.0
-yarl==1.9.2
--- a/examples/langchain-python-rag-privategpt/.gitignore
+++ b/examples/langchain-python-rag-privategpt/.gitignore
@@ -1,170 +0,0 @@
-# OSX
-.DS_STORE
-
-# Models
-models/
-
-# Local Chroma db
-.chroma/
-db/
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
--- a/examples/langchain-python-rag-privategpt/LICENSE
+++ b/examples/langchain-python-rag-privategpt/LICENSE
@@ -1,201 +0,0 @@
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--- a/examples/langchain-python-rag-privategpt/README.md
+++ b/examples/langchain-python-rag-privategpt/README.md
@@ -1,91 +0,0 @@
-# PrivateGPT with Llama 2 uncensored
-
-https://github.com/ollama/ollama/assets/3325447/20cf8ec6-ff25-42c6-bdd8-9be594e3ce1b
-
-> Note: this example is a slightly modified version of PrivateGPT using models such as Llama 2 Uncensored. All credit for PrivateGPT goes to Iván Martínez who is the creator of it, and you can find his GitHub repo [here](https://github.com/imartinez/privateGPT).
-
-### Setup
-
-Set up a virtual environment (optional):
-
-```
-python3 -m venv .venv
-source .venv/bin/activate
-```
-
-Install the Python dependencies:
-
-```shell
-pip install -r requirements.txt
-```
-
-Pull the model you'd like to use:
-
-```
-ollama pull llama2-uncensored
-```
-
-### Getting WeWork's latest quarterly earnings report (10-Q)
-
-```
-mkdir source_documents
-curl https://d18rn0p25nwr6d.cloudfront.net/CIK-0001813756/975b3e9b-268e-4798-a9e4-2a9a7c92dc10.pdf -o source_documents/wework.pdf
-```
-
-### Ingesting files
-
-```shell
-python ingest.py
-```
-
-Output should look like this:
-
-```shell
-Creating new vectorstore
-Loading documents from source_documents
-Loading new documents: 100%|██████████████████████| 1/1 [00:01<00:00,  1.73s/it]
-Loaded 1 new documents from source_documents
-Split into 90 chunks of text (max. 500 tokens each)
-Creating embeddings. May take some minutes...
-Using embedded DuckDB with persistence: data will be stored in: db
-Ingestion complete! You can now run privateGPT.py to query your documents
-```
-
-### Ask questions
-
-```shell
-python privateGPT.py
-
-Enter a query: How many locations does WeWork have?
-
-> Answer (took 17.7 s.):
-As of June 2023, WeWork has 777 locations worldwide, including 610 Consolidated Locations (as defined in the section entitled Key Performance Indicators).
-```
-
-### Try a different model:
-
-```
-ollama pull llama2:13b
-MODEL=llama2:13b python privateGPT.py
-```
-
-## Adding more files
-
-Put any and all your files into the `source_documents` directory
-
-The supported extensions are:
-
- `.csv`: CSV,
- `.docx`: Word Document,
- `.doc`: Word Document,
- `.enex`: EverNote,
- `.eml`: Email,
- `.epub`: EPub,
- `.html`: HTML File,
- `.md`: Markdown,
- `.msg`: Outlook Message,
- `.odt`: Open Document Text,
- `.pdf`: Portable Document Format (PDF),
- `.pptx` : PowerPoint Document,
- `.ppt` : PowerPoint Document,
- `.txt`: Text file (UTF-8),
--- a/examples/langchain-python-rag-privategpt/constants.py
+++ b/examples/langchain-python-rag-privategpt/constants.py
@@ -1,11 +0,0 @@
-import os
-from chromadb.config import Settings
-
-# Define the folder for storing database
-PERSIST_DIRECTORY = os.environ.get('PERSIST_DIRECTORY', 'db')
-
-# Define the Chroma settings
-CHROMA_SETTINGS = Settings(
-        persist_directory=PERSIST_DIRECTORY,
-        anonymized_telemetry=False
-)
--- a/examples/langchain-python-rag-privategpt/ingest.py
+++ b/examples/langchain-python-rag-privategpt/ingest.py
@@ -1,170 +0,0 @@
-#!/usr/bin/env python3
-import os
-import glob
-from typing import List
-from multiprocessing import Pool
-from tqdm import tqdm
-
-from langchain.document_loaders import (
-    CSVLoader,
-    EverNoteLoader,
-    PyMuPDFLoader,
-    TextLoader,
-    UnstructuredEmailLoader,
-    UnstructuredEPubLoader,
-    UnstructuredHTMLLoader,
-    UnstructuredMarkdownLoader,
-    UnstructuredODTLoader,
-    UnstructuredPowerPointLoader,
-    UnstructuredWordDocumentLoader,
-)
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.docstore.document import Document
-from constants import CHROMA_SETTINGS
-
-
-# Load environment variables
-persist_directory = os.environ.get('PERSIST_DIRECTORY', 'db')
-source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents')
-embeddings_model_name = os.environ.get('EMBEDDINGS_MODEL_NAME', 'all-MiniLM-L6-v2')
-chunk_size = 500
-chunk_overlap = 50
-
-# Custom document loaders
-class MyElmLoader(UnstructuredEmailLoader):
-    """Wrapper to fallback to text/plain when default does not work"""
-
-    def load(self) -> List[Document]:
-        """Wrapper adding fallback for elm without html"""
-        try:
-            try:
-                doc = UnstructuredEmailLoader.load(self)
-            except ValueError as e:
-                if 'text/html content not found in email' in str(e):
-                    # Try plain text
-                    self.unstructured_kwargs["content_source"]="text/plain"
-                    doc = UnstructuredEmailLoader.load(self)
-                else:
-                    raise
-        except Exception as e:
-            # Add file_path to exception message
-            raise type(e)(f"{self.file_path}: {e}") from e
-
-        return doc
-
-
-# Map file extensions to document loaders and their arguments
-LOADER_MAPPING = {
-    ".csv": (CSVLoader, {}),
-    # ".docx": (Docx2txtLoader, {}),
-    ".doc": (UnstructuredWordDocumentLoader, {}),
-    ".docx": (UnstructuredWordDocumentLoader, {}),
-    ".enex": (EverNoteLoader, {}),
-    ".eml": (MyElmLoader, {}),
-    ".epub": (UnstructuredEPubLoader, {}),
-    ".html": (UnstructuredHTMLLoader, {}),
-    ".md": (UnstructuredMarkdownLoader, {}),
-    ".odt": (UnstructuredODTLoader, {}),
-    ".pdf": (PyMuPDFLoader, {}),
-    ".ppt": (UnstructuredPowerPointLoader, {}),
-    ".pptx": (UnstructuredPowerPointLoader, {}),
-    ".txt": (TextLoader, {"encoding": "utf8"}),
-    # Add more mappings for other file extensions and loaders as needed
-}
-
-
-def load_single_document(file_path: str) -> List[Document]:
-    if os.path.getsize(file_path) != 0:
-        filename, ext = os.path.splitext(file_path)
-        if ext in LOADER_MAPPING:
-            loader_class, loader_args = LOADER_MAPPING[ext]
-            try:
-                loader = loader_class(file_path, **loader_args)
-                if loader:
-                    return loader.load()
-            except:
-                print(f"Corrupted file {file_path}. Ignoring it.")
-        else:
-            print(f"Unsupported file {file_path}. Ignoring it.")
-    else:
-        print(f"Empty file {file_path}. Ignoring it.")
-
-
-def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Document]:
-    """
-    Loads all documents from the source documents directory, ignoring specified files
-    """
-    all_files = []
-    for ext in LOADER_MAPPING:
-        all_files.extend(
-            glob.glob(os.path.join(source_dir, f"**/*{ext}"), recursive=True)
-        )
-    filtered_files = [file_path for file_path in all_files if file_path not in ignored_files]
-
-    with Pool(processes=os.cpu_count()) as pool:
-        results = []
-        with tqdm(total=len(filtered_files), desc='Loading new documents', ncols=80) as pbar:
-            for i, docs in enumerate(pool.imap_unordered(load_single_document, filtered_files)):
-                if docs:
-                    results.extend(docs)
-                pbar.update()
-
-    return results
-
-def process_documents(ignored_files: List[str] = []) -> List[Document]:
-    """
-    Load documents and split in chunks
-    """
-    print(f"Loading documents from {source_directory}")
-    documents = load_documents(source_directory, ignored_files)
-    if not documents:
-        print("No new documents to load")
-        exit(0)
-    print(f"Loaded {len(documents)} new documents from {source_directory}")
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
-    texts = text_splitter.split_documents(documents)
-    print(f"Split into {len(texts)} chunks of text (max. {chunk_size} tokens each)")
-    return texts
-
-def does_vectorstore_exist(persist_directory: str) -> bool:
-    """
-    Checks if vectorstore exists
-    """
-    if os.path.exists(os.path.join(persist_directory, 'index')):
-        if os.path.exists(os.path.join(persist_directory, 'chroma-collections.parquet')) and os.path.exists(os.path.join(persist_directory, 'chroma-embeddings.parquet')):
-            list_index_files = glob.glob(os.path.join(persist_directory, 'index/*.bin'))
-            list_index_files += glob.glob(os.path.join(persist_directory, 'index/*.pkl'))
-            # At least 3 documents are needed in a working vectorstore
-            if len(list_index_files) > 3:
-                return True
-    return False
-
-def main():
-    # Create embeddings
-    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
-
-    if does_vectorstore_exist(persist_directory):
-        # Update and store locally vectorstore
-        print(f"Appending to existing vectorstore at {persist_directory}")
-        db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
-        collection = db.get()
-        texts = process_documents([metadata['source'] for metadata in collection['metadatas']])
-        print(f"Creating embeddings. May take some minutes...")
-        db.add_documents(texts)
-    else:
-        # Create and store locally vectorstore
-        print("Creating new vectorstore")
-        texts = process_documents()
-        print(f"Creating embeddings. May take some minutes...")
-        db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory)
-    db.persist()
-    db = None
-
-    print(f"Ingestion complete! You can now run privateGPT.py to query your documents")
-
-
-if __name__ == "__main__":
-    main()
--- a/examples/langchain-python-rag-privategpt/poetry.lock
+++ b/examples/langchain-python-rag-privategpt/poetry.lock
--- a/examples/langchain-python-rag-privategpt/privateGPT.py
+++ b/examples/langchain-python-rag-privategpt/privateGPT.py
@@ -1,74 +0,0 @@
-#!/usr/bin/env python3
-from langchain.chains import RetrievalQA
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-from langchain.vectorstores import Chroma
-from langchain.llms import Ollama
-import chromadb
-import os
-import argparse
-import time
-
-model = os.environ.get("MODEL", "llama2-uncensored")
-# For embeddings model, the example uses a sentence-transformers model
-# https://www.sbert.net/docs/pretrained_models.html 
-# "The all-mpnet-base-v2 model provides the best quality, while all-MiniLM-L6-v2 is 5 times faster and still offers good quality."
-embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME", "all-MiniLM-L6-v2")
-persist_directory = os.environ.get("PERSIST_DIRECTORY", "db")
-target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))
-
-from constants import CHROMA_SETTINGS
-
-def main():
-    # Parse the command line arguments
-    args = parse_arguments()
-    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
-
-    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
-
-    retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
-    # activate/deactivate the streaming StdOut callback for LLMs
-    callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()]
-
-    llm = Ollama(model=model, callbacks=callbacks)
-
-    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents= not args.hide_source)
-    # Interactive questions and answers
-    while True:
-        query = input("\nEnter a query: ")
-        if query == "exit":
-            break
-        if query.strip() == "":
-            continue
-
-        # Get the answer from the chain
-        start = time.time()
-        res = qa(query)
-        answer, docs = res['result'], [] if args.hide_source else res['source_documents']
-        end = time.time()
-
-        # Print the result
-        print("\n\n> Question:")
-        print(query)
-        print(answer)
-
-        # Print the relevant sources used for the answer
-        for document in docs:
-            print("\n> " + document.metadata["source"] + ":")
-            print(document.page_content)
-
-def parse_arguments():
-    parser = argparse.ArgumentParser(description='privateGPT: Ask questions to your documents without an internet connection, '
-                                                 'using the power of LLMs.')
-    parser.add_argument("--hide-source", "-S", action='store_true',
-                        help='Use this flag to disable printing of source documents used for answers.')
-
-    parser.add_argument("--mute-stream", "-M",
-                        action='store_true',
-                        help='Use this flag to disable the streaming StdOut callback for LLMs.')
-
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    main()
--- a/examples/langchain-python-rag-privategpt/pyproject.toml
+++ b/examples/langchain-python-rag-privategpt/pyproject.toml
@@ -1,26 +0,0 @@
-[tool.poetry]
-name = "privategpt"
-version = "0.1.0"
-description = ""
-authors = ["Ivan Martinez <ivanmartit@gmail.com>"]
-license = "Apache Version 2.0"
-readme = "README.md"
-
-[tool.poetry.dependencies]
-python = "^3.10"
-langchain = "0.0.261"
-gpt4all = "^1.0.3"
-chromadb = "^0.3.26"
-PyMuPDF = "^1.22.5"
-python-dotenv = "^1.0.0"
-unstructured = "^0.8.0"
-extract-msg = "^0.41.5"
-tabulate = "^0.9.0"
-pandoc = "^2.3"
-pypandoc = "^1.11"
-tqdm = "^4.65.0"
-sentence-transformers = "^2.2.2"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
--- a/examples/langchain-python-rag-privategpt/requirements.txt
+++ b/examples/langchain-python-rag-privategpt/requirements.txt
@@ -1,15 +0,0 @@
-langchain==0.0.274
-gpt4all==1.0.8
-chromadb==0.5.0
-llama-cpp-python==0.1.81
-urllib3==2.0.4
-PyMuPDF==1.23.5
-python-dotenv==1.0.0
-unstructured==0.10.8
-extract-msg==0.45.0
-tabulate==0.9.0
-pandoc==2.3
-pypandoc==1.11
-tqdm==4.66.1
-sentence_transformers==2.2.2
-numpy>=1.22.2 # not directly required, pinned by Snyk to avoid a vulnerability
--- a/examples/langchain-python-rag-websummary/README.md
+++ b/examples/langchain-python-rag-websummary/README.md
@@ -1,23 +0,0 @@
-# LangChain Web Summarization
-
-This example summarizes the website, [https://ollama.com/blog/run-llama2-uncensored-locally](https://ollama.com/blog/run-llama2-uncensored-locally)
-
-## Running the Example
-
-1. Ensure you have the `llama3.2` model installed:
-
-   ```bash
-   ollama pull llama3.2
-   ```
-
-2. Install the Python Requirements.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the example:
-
-   ```bash
-   python main.py
-   ```
--- a/examples/langchain-python-rag-websummary/main.py
+++ b/examples/langchain-python-rag-websummary/main.py
@@ -1,12 +0,0 @@
-from langchain_community.llms import Ollama
-from langchain_community.document_loaders import WebBaseLoader
-from langchain.chains.summarize import load_summarize_chain
-
-loader = WebBaseLoader("https://ollama.com/blog/run-llama2-uncensored-locally")
-docs = loader.load()
-
-llm = Ollama(model="llama3.2")
-chain = load_summarize_chain(llm, chain_type="stuff")
-
-result = chain.invoke(docs)
-print(result)
--- a/examples/langchain-python-rag-websummary/requirements.txt
+++ b/examples/langchain-python-rag-websummary/requirements.txt
@@ -1 +0,0 @@
-langchain==0.0.259
--- a/examples/langchain-python-simple/README.md
+++ b/examples/langchain-python-simple/README.md
@@ -1,23 +0,0 @@
-# LangChain
-
-This example is a basic "hello world" of using LangChain with Ollama.
-
-## Running the Example
-
-1. Ensure you have the `llama3.2` model installed:
-
-   ```bash
-   ollama pull llama3.2
-   ```
-
-2. Install the Python Requirements.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the example:
-
-   ```bash
-   python main.py
-   ```
--- a/examples/langchain-python-simple/main.py
+++ b/examples/langchain-python-simple/main.py
@@ -1,6 +0,0 @@
-from langchain.llms import Ollama
-
-input = input("What is your question?\n> ")
-llm = Ollama(model="llama3.2")
-res = llm.invoke(input)
-print (res)
--- a/examples/langchain-python-simple/requirements.txt
+++ b/examples/langchain-python-simple/requirements.txt
@@ -1 +0,0 @@
-langchain==0.0.259
--- a/examples/langchain-typescript-simple/README.md
+++ b/examples/langchain-typescript-simple/README.md
@@ -1,23 +0,0 @@
-# LangChain
-
-This example is a basic "hello world" of using LangChain with Ollama using Node.js and Typescript.
-
-## Running the Example
-
-1. Install the prerequisites:
-
-   ```bash
-   npm install
-   ```
-
-2. Ensure the `mistral` model is available:
-
-   ```bash
-   ollama pull mistral
-   ```
-
-3. Run the example:
-
-   ```bash
-   npm start
-   ```
--- a/examples/langchain-typescript-simple/main.ts
+++ b/examples/langchain-typescript-simple/main.ts
@@ -1,25 +0,0 @@
-import { Ollama } from 'langchain/llms/ollama';
-import * as readline from "readline";
-
-async function main() {
-  const ollama = new Ollama({
-    model: 'mistral'    
-    // other parameters can be found at https://js.langchain.com/docs/api/llms_ollama/classes/Ollama
-  });
-
-  const rl = readline.createInterface({
-    input: process.stdin,
-    output: process.stdout,
-  });
-
-  rl.question("What is your question: \n", async (user_input) => {
-    const stream = await ollama.stream(user_input);
-  
-    for await (const chunk of stream) {
-      process.stdout.write(chunk);
-    }
-    rl.close();
-  })
-}
-
-main();
--- a/examples/langchain-typescript-simple/package-lock.json
+++ b/examples/langchain-typescript-simple/package-lock.json
@@ -1,997 +0,0 @@
-{
-  "name": "langchain-typescript-simple",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "dependencies": {
-        "langchain": "^0.0.165"
-      },
-      "devDependencies": {
-        "typescript": "^5.2.2"
-      }
-    },
-    "node_modules/@anthropic-ai/sdk": {
-      "version": "0.6.2",
-      "resolved": "https://registry.npmjs.org/@anthropic-ai/sdk/-/sdk-0.6.2.tgz",
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-      "dependencies": {
-        "@types/node": "^18.11.18",
-        "@types/node-fetch": "^2.6.4",
-        "abort-controller": "^3.0.0",
-        "agentkeepalive": "^4.2.1",
-        "digest-fetch": "^1.3.0",
-        "form-data-encoder": "1.7.2",
-        "formdata-node": "^4.3.2",
-        "node-fetch": "^2.6.7"
-      }
-    },
-    "node_modules/@types/node": {
-      "version": "18.18.4",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-18.18.4.tgz",
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--- a/examples/langchain-typescript-simple/package.json
+++ b/examples/langchain-typescript-simple/package.json
@@ -1,13 +0,0 @@
-{
-  "scripts": {
-    "start": "tsx main.ts"
-  },
-  "devDependencies": {
-    "tsx": "^4.6.2",
-    "typescript": "^5.3.3"
-  },
-  "dependencies": {
-    "langchain": "^0.0.165",
-    "readline": "^1.3.0"
-  }
-}
--- a/examples/modelfile-mario/Modelfile
+++ b/examples/modelfile-mario/Modelfile
@@ -1,5 +0,0 @@
-FROM llama3.2
-PARAMETER temperature 1
-SYSTEM """
-You are Mario from super mario bros, acting as an assistant.
-"""
--- a/examples/modelfile-mario/logo.png
+++ b/examples/modelfile-mario/logo.png
--- a/examples/modelfile-mario/readme.md
+++ b/examples/modelfile-mario/readme.md
@@ -1,43 +0,0 @@
-<img src="logo.png" alt="image of Italian plumber" height="200"/>
-
-# Example character: Mario
-
-This example shows how to create a basic character using Llama 3.2 as the base model.
-
-To run this example:
-
-1. Download the Modelfile
-2. `ollama pull llama3.2` to get the base model used in the model file.
-3. `ollama create NAME -f ./Modelfile`
-4. `ollama run NAME`
-
-Ask it some questions like "Who are you?" or "Is Peach in trouble again?"
-
-## Editing this file
-
-What the model file looks like:
-
-```
-FROM llama3.2
-PARAMETER temperature 1
-SYSTEM """
-You are Mario from Super Mario Bros, acting as an assistant.
-"""
-```
-
-What if you want to change its behaviour?
-
- Try changing the prompt
- Try changing the parameters [Docs](https://github.com/ollama/ollama/blob/main/docs/modelfile.md)
- Try changing the model (e.g. An uncensored model by `FROM wizard-vicuna` this is the wizard-vicuna uncensored model )
-
-Once the changes are made,
-
-1. `ollama create NAME -f ./Modelfile`
-2. `ollama run NAME`
-3. Iterate until you are happy with the results.
-
-Notes:
-
- This example is for research purposes only. There is no affiliation with any entity.
- When using an uncensored model, please be aware that it may generate offensive content.
--- a/examples/python-dockerit/Modelfile
+++ b/examples/python-dockerit/Modelfile
@@ -1,20 +0,0 @@
-FROM mistral
-SYSTEM """
-You are an experienced Devops engineer focused on docker. When given specifications for a particular need or application you know the best way to host that within a docker container. For instance if someone tells you they want an nginx server to host files located at /web you will answer as follows
-
---start
-FROM nginx:alpine
-COPY /myweb /usr/share/nginx/html
-EXPOSE 80
---end
-
-Notice that the answer you should give is just the contents of the dockerfile with no explanation and there are three dashes and the word start at the beginning and 3 dashes and the word end. The full output can be piped into a file and run as is. Here is another example. The user will ask to launch a Postgres server with a password of abc123. And the response should be
-
---start
-FROM postgres:latest
-ENV POSTGRES_PASSWORD=abc123
-EXPOSE 5432
---end
-
-Again it's just the contents of the dockerfile and nothing else.
-"""
--- a/examples/python-dockerit/README.md
+++ b/examples/python-dockerit/README.md
@@ -1,31 +0,0 @@
-# DockerIt
-
-DockerIt is a tool to help you build and run your application in a Docker container. It consists of a model that defines the system prompt and model weights to use, along with a python script to then build the container and run the image automatically.
-
-## Running the Example
-
-1. Ensure you have the `mattw/dockerit` model installed:
-
-   ```bash
-   ollama pull mattw/dockerit
-   ```
-
-2. Make sure Docker is running on your machine.
-
-3. Install the Python Requirements.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-4. Run the example:
-
-   ```bash
-   python dockerit.py "simple postgres server with admin password set to 123"
-   ```
-
-5. Enter the name you would like to use for your container image.
-
-## Caveats
-
-This is a simple example. It's assuming the Dockerfile content generated is going to work. In many cases, even with simple web servers, it fails when trying to copy files that don't exist. It's simply an example of what you could possibly do.
--- a/examples/python-dockerit/dockerit.py
+++ b/examples/python-dockerit/dockerit.py
@@ -1,17 +0,0 @@
-import requests, json, docker, io, sys
-inputDescription = " ".join(sys.argv[1:])
-imageName = input("Enter the name of the image: ")
-client = docker.from_env()
-s = requests.Session()
-output=""
-with s.post('http://localhost:11434/api/generate', json={'model': 'mattw/dockerit', 'prompt': inputDescription}, stream=True) as r:
-  for line in r.iter_lines():
-    if line:
-      j = json.loads(line)
-      if "response" in j:
-        output = output +j["response"]
-output = output[output.find("---start")+9:output.find("---end")-1]
-f = io.BytesIO(bytes(output, 'utf-8'))
-client.images.build(fileobj=f, tag=imageName)
-container = client.containers.run(imageName, detach=True)
-print("Container named", container.name, " started with id: ",container.id)
--- a/examples/python-dockerit/requirements.txt
+++ b/examples/python-dockerit/requirements.txt
@@ -1 +0,0 @@
-docker
--- a/examples/python-grounded-factuality-rag-check/README.md
+++ b/examples/python-grounded-factuality-rag-check/README.md
@@ -1,93 +0,0 @@
-# RAG Hallucination Checker using Bespoke-Minicheck
-
-This example allows the user to ask questions related to a document, which can be specified via an article url. Relevant chunks are retrieved from the document and given to `llama3.2` as context to answer the question. Then each sentence in the answer is checked against the retrieved chunks using `bespoke-minicheck` to ensure that the answer does not contain hallucinations.
-
-## Running the Example
-
-1. Ensure `all-minilm` (embedding) `llama3.2` (chat) and `bespoke-minicheck` (check) models installed:
-
-   ```bash
-   ollama pull all-minilm
-   ollama pull llama3.2
-   ollama pull bespoke-minicheck
-   ```
-
-2. Install the dependencies.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the example:
-
-   ```bash
-   python main.py
-   ```
-
-## Expected Output
-
-```text
-Enter the URL of an article you want to chat with, or press Enter for default example:
-
-Loaded, chunked, and embedded text from https://www.theverge.com/2024/9/12/24242439/openai-o1-model-reasoning-strawberry-chatgpt.
-
-Enter your question or type quit: Who is the CEO of openai?
-
-Retrieved chunks:
-OpenAI is releasing a new model called o1 , the first in a planned series of “ reasoning ” models that have been trained to answer more complex questions , faster than a human can . It ’ s being released alongside o1-mini , a smaller , cheaper version . And yes , if you ’ re steeped in AI rumors : this is , in fact , the extremely hyped Strawberry model . For OpenAI , o1 represents a step toward its broader goal of human-like artificial intelligence .
-
-OpenAI is releasing a new model called o1 , the first in a planned series of “ reasoning ” models that have been trained to answer more complex questions , faster than a human can . It ’ s being released alongside o1-mini , a smaller , cheaper version . And yes , if you ’ re steeped in AI rumors : this is , in fact , the extremely hyped Strawberry model . For OpenAI , o1 represents a step toward its broader goal of human-like artificial intelligence . More practically , it does a better job at writing code and solving multistep problems than previous models . But it ’ s also more expensive and slower to use than GPT-4o . OpenAI is calling this release of o1 a “ preview ” to emphasize how nascent it is . ChatGPT Plus and Team users get access to both o1-preview and o1-mini starting today , while Enterprise and Edu users will get access early next week .
-
-More practically , it does a better job at writing code and solving multistep problems than previous models . But it ’ s also more expensive and slower to use than GPT-4o . OpenAI is calling this release of o1 a “ preview ” to emphasize how nascent it is . ChatGPT Plus and Team users get access to both o1-preview and o1-mini starting today , while Enterprise and Edu users will get access early next week . OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn ’ t set a release date yet . Developer access to o1 is really expensive : In the API , o1-preview is $ 15 per 1 million input tokens , or chunks of text parsed by the model , and $ 60 per 1 million output tokens . For comparison , GPT-4o costs $ 5 per 1 million input tokens and $ 15 per 1 million output tokens .
-
-OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn ’ t set a release date yet . Developer access to o1 is really expensive : In the API , o1-preview is $ 15 per 1 million input tokens , or chunks of text parsed by the model , and $ 60 per 1 million output tokens . For comparison , GPT-4o costs $ 5 per 1 million input tokens and $ 15 per 1 million output tokens . The training behind o1 is fundamentally different from its predecessors , OpenAI ’ s research lead , Jerry Tworek , tells me , though the company is being vague about the exact details . He says o1 “ has been trained using a completely new optimization algorithm and a new training dataset specifically tailored for it. ” Image : OpenAI OpenAI taught previous GPT models to mimic patterns from its training data .
-
-LLM Answer:
-The text does not mention the CEO of OpenAI. It only discusses the release of a new model called o1 and some details about it, but does not provide information on the company's leadership.
-
-LLM Claim: The text does not mention the CEO of OpenAI.
-Is this claim supported by the context according to bespoke-minicheck? Yes
-
-LLM Claim: It only discusses the release of a new model called o1 and some details about it, but does not provide information on the company's leadership.
-Is this claim supported by the context according to bespoke-minicheck? No
-```
-
-The second claim is unsupported since the text mentions the research lead. 
-
-Another tricky example:
-
-```text
-
-Enter your question or type quit: what sets o1 apart from gpt-4o?
-
-Retrieved chunks: 
-OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn ’ t set a release date yet . Developer access to o1 is really expensive : In the API , o1-preview is $ 15 per 1 million input tokens , or chunks of text parsed by the model , and $ 60 per 1 million output tokens . For comparison , GPT-4o costs $ 5 per 1 million input tokens and $ 15 per 1 million output tokens . The training behind o1 is fundamentally different from its predecessors , OpenAI ’ s research lead , Jerry Tworek , tells me , though the company is being vague about the exact details . He says o1 “ has been trained using a completely new optimization algorithm and a new training dataset specifically tailored for it. ” Image : OpenAI OpenAI taught previous GPT models to mimic patterns from its training data .
-
-He says OpenAI also tested o1 against a qualifying exam for the International Mathematics Olympiad , and while GPT-4o only correctly solved only 13 percent of problems , o1 scored 83 percent . “ We can ’ t say we solved hallucinations ” In online programming contests known as Codeforces competitions , this new model reached the 89th percentile of participants , and OpenAI claims the next update of this model will perform “ similarly to PhD students on challenging benchmark tasks in physics , chemistry and biology. ” At the same time , o1 is not as capable as GPT-4o in a lot of areas . It doesn ’ t do as well on factual knowledge about the world .
-
-More practically , it does a better job at writing code and solving multistep problems than previous models . But it ’ s also more expensive and slower to use than GPT-4o . OpenAI is calling this release of o1 a “ preview ” to emphasize how nascent it is . ChatGPT Plus and Team users get access to both o1-preview and o1-mini starting today , while Enterprise and Edu users will get access early next week . OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn ’ t set a release date yet . Developer access to o1 is really expensive : In the API , o1-preview is $ 15 per 1 million input tokens , or chunks of text parsed by the model , and $ 60 per 1 million output tokens . For comparison , GPT-4o costs $ 5 per 1 million input tokens and $ 15 per 1 million output tokens .
-
-“ We can ’ t say we solved hallucinations ” In online programming contests known as Codeforces competitions , this new model reached the 89th percentile of participants , and OpenAI claims the next update of this model will perform “ similarly to PhD students on challenging benchmark tasks in physics , chemistry and biology. ” At the same time , o1 is not as capable as GPT-4o in a lot of areas . It doesn ’ t do as well on factual knowledge about the world . It also doesn ’ t have the ability to browse the web or process files and images . Still , the company believes it represents a brand-new class of capabilities . It was named o1 to indicate “ resetting the counter back to 1. ” “ I ’ m gon na be honest : I think we ’ re terrible at naming , traditionally , ” McGrew says .
-LLM Answer: According to the text, several things set o1 apart from GPT-4o:
-
-* In online programming contests (Codeforces competitions), o1 scored 83% correct solutions compared to GPT-4o's 13%.
-* The training behind o1 is "fundamentally different" from its predecessors, including a completely new optimization algorithm and a new training dataset specifically tailored for it.
-* o1 has been shown to perform similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology, while GPT-4o does not have this level of performance.
-* However, o1 is also less capable than GPT-4o in some areas, particularly with regard to factual knowledge about the world.
-
-LLM Claim: According to the text, several things set o1 apart from GPT-4o:
-
-* In online programming contests (Codeforces competitions), o1 scored 83% correct solutions compared to GPT-4o's 13%.
-Is this claim supported by the context according to bespoke-minicheck? Yes
-
-LLM Claim: * The training behind o1 is "fundamentally different" from its predecessors, including a completely new optimization algorithm and a new training dataset specifically tailored for it.
-Is this claim supported by the context according to bespoke-minicheck? Yes
-
-LLM Claim: * o1 has been shown to perform similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology, while GPT-4o does not have this level of performance.
-Is this claim supported by the context according to bespoke-minicheck? No
-
-LLM Claim: * However, o1 is also less capable than GPT-4o in some areas, particularly with regard to factual knowledge about the world.
-Is this claim supported by the context according to bespoke-minicheck? Yes
-```
-
-We see that the third claim "* o1 has been shown to perform similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology, while GPT-4o does not have this level of performance." is not supported by the context. This is because the context only mentions that o1 "is claimed to perform" which is different from "has been shown to perform".
--- a/examples/python-grounded-factuality-rag-check/main.py
+++ b/examples/python-grounded-factuality-rag-check/main.py
@@ -1,137 +0,0 @@
-import ollama
-import warnings
-from mattsollamatools import chunker
-from newspaper import Article
-import numpy as np
-from sklearn.neighbors import NearestNeighbors
-import nltk
-
-warnings.filterwarnings(
-    "ignore", category=FutureWarning, module="transformers.tokenization_utils_base"
-)
-nltk.download("punkt_tab", quiet=True)
-
-
-def getArticleText(url):
-    """Gets the text of an article from a URL.
-
-    Often there are a bunch of ads and menus on pages for a news article.
-    This uses newspaper3k to get just the text of just the article.
-    """
-    article = Article(url)
-    article.download()
-    article.parse()
-    return article.text
-
-
-def knn_search(question_embedding, embeddings, k=5):
-    """Performs K-nearest neighbors (KNN) search"""
-    X = np.array(
-        [item["embedding"] for article in embeddings for item in article["embeddings"]]
-    )
-    source_texts = [
-        item["source"] for article in embeddings for item in article["embeddings"]
-    ]
-
-    # Fit a KNN model on the embeddings
-    knn = NearestNeighbors(n_neighbors=k, metric="cosine")
-    knn.fit(X)
-
-    # Find the indices and distances of the k-nearest neighbors.
-    _, indices = knn.kneighbors(question_embedding, n_neighbors=k)
-
-    # Get the indices and source texts of the best matches
-    best_matches = [(indices[0][i], source_texts[indices[0][i]]) for i in range(k)]
-
-    return best_matches
-
-
-def check(document, claim):
-    """Checks if the claim is supported by the document by calling bespoke-minicheck.
-
-    Returns Yes/yes if the claim is supported by the document, No/no otherwise.
-    Support for logits will be added in the future.
-
-    bespoke-minicheck's system prompt is defined as:
-      'Determine whether the provided claim is consistent with the corresponding
-      document. Consistency in this context implies that all information presented in the claim
-      is substantiated by the document. If not, it should be considered inconsistent. Please
-      assess the claim's consistency with the document by responding with either "Yes" or "No".'
-
-    bespoke-minicheck's user prompt is defined as:
-      "Document: {document}\nClaim: {claim}"
-    """
-    prompt = f"Document: {document}\nClaim: {claim}"
-    response = ollama.generate(
-        model="bespoke-minicheck", prompt=prompt, options={"num_predict": 2, "temperature": 0.0}
-    )
-    return response["response"].strip()
-
-
-if __name__ == "__main__":
-    allEmbeddings = []
-    default_url = "https://www.theverge.com/2024/9/12/24242439/openai-o1-model-reasoning-strawberry-chatgpt"
-    user_input = input(
-        "Enter the URL of an article you want to chat with, or press Enter for default example: "
-    )
-    article_url = user_input.strip() if user_input.strip() else default_url
-    article = {}
-    article["embeddings"] = []
-    article["url"] = article_url
-    text = getArticleText(article_url)
-    chunks = chunker(text)
-
-    # Embed (batch) chunks using ollama
-    embeddings = ollama.embed(model="all-minilm", input=chunks)["embeddings"]
-
-    for chunk, embedding in zip(chunks, embeddings):
-        item = {}
-        item["source"] = chunk
-        item["embedding"] = embedding
-        item["sourcelength"] = len(chunk)
-        article["embeddings"].append(item)
-
-    allEmbeddings.append(article)
-
-    print(f"\nLoaded, chunked, and embedded text from {article_url}.\n")
-
-    while True:
-        # Input a question from the user
-        # For example, "Who is the chief research officer?"
-        question = input("Enter your question or type quit: ")
-
-        if question.lower() == "quit":
-            break
-
-        # Embed the user's question using ollama.embed
-        question_embedding = ollama.embed(model="all-minilm", input=question)[
-            "embeddings"
-        ]
-
-        # Perform KNN search to find the best matches (indices and source text)
-        best_matches = knn_search(question_embedding, allEmbeddings, k=4)
-
-        sourcetext = "\n\n".join([source_text for (_, source_text) in best_matches])
-
-        print(f"\nRetrieved chunks: \n{sourcetext}\n")
-
-        # Give the retrieved chunks and question to the chat model
-        system_prompt = f"Only use the following information to answer the question. Do not use anything else: {sourcetext}"
-
-        ollama_response = ollama.generate(
-            model="llama3.2",
-            prompt=question,
-            system=system_prompt,
-            options={"stream": False},
-        )
-
-        answer = ollama_response["response"]
-        print(f"LLM Answer:\n{answer}\n")
-
-        # Check each sentence in the response for grounded factuality
-        if answer:
-            for claim in nltk.sent_tokenize(answer):
-                print(f"LLM Claim: {claim}")
-                print(
-                    f"Is this claim supported by the context according to bespoke-minicheck? {check(sourcetext, claim)}\n"
-                )
--- a/examples/python-grounded-factuality-rag-check/requirements.txt
+++ b/examples/python-grounded-factuality-rag-check/requirements.txt
@@ -1,8 +0,0 @@
-ollama
-lxml==5.3.0
-lxml_html_clean==0.2.2
-mattsollamatools==0.0.25
-newspaper3k==0.2.8
-nltk==3.9.1
-numpy==1.26.4
-scikit-learn==1.5.2
--- a/examples/python-grounded-factuality-simple-check/main.py
+++ b/examples/python-grounded-factuality-simple-check/main.py
@@ -1,53 +0,0 @@
-"""Simple example to demonstrate how to use the bespoke-minicheck model."""
-
-import ollama
-
-# NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve`
-
-
-def check(document, claim):
-    """Checks if the claim is supported by the document by calling bespoke-minicheck.
-
-    Returns Yes/yes if the claim is supported by the document, No/no otherwise.
-    Support for logits will be added in the future.
-
-    bespoke-minicheck's system prompt is defined as:
-      'Determine whether the provided claim is consistent with the corresponding
-      document. Consistency in this context implies that all information presented in the claim
-      is substantiated by the document. If not, it should be considered inconsistent. Please
-      assess the claim's consistency with the document by responding with either "Yes" or "No".'
-
-    bespoke-minicheck's user prompt is defined as:
-      "Document: {document}\nClaim: {claim}"
-    """
-    prompt = f"Document: {document}\nClaim: {claim}"
-    response = ollama.generate(
-        model="bespoke-minicheck", prompt=prompt, options={"num_predict": 2, "temperature": 0.0}
-    )
-    return response["response"].strip()
-
-
-def get_user_input(prompt):
-    user_input = input(prompt)
-    if not user_input:
-        exit()
-    print()
-    return user_input
-
-
-def main():
-    while True:
-        # Get a document from the user (e.g. "Ryan likes running and biking.")
-        document = get_user_input("Enter a document: ")
-        # Get a claim from the user (e.g. "Ryan likes to run.")
-        claim = get_user_input("Enter a claim: ")
-        # Check if the claim is supported by the document
-        grounded_factuality_check = check(document, claim)
-        print(
-            f"Is the claim supported by the document according to bespoke-minicheck? {grounded_factuality_check}"
-        )
-        print("\n\n")
-
-
-if __name__ == "__main__":
-    main()
--- a/examples/python-grounded-factuality-simple-check/readme.md
+++ b/examples/python-grounded-factuality-simple-check/readme.md
@@ -1,54 +0,0 @@
-# Simple Bespoke-Minicheck Example
-
-`bespoke-minicheck` is a model for checking if a claim is supported by a document. It is used through the **generate** endpoint, which is called in this example with a `prompt` that includes the expected formatting of the user input. 
-
-## Running the Example
-
-1. Ensure you have the `bespoke-minicheck` model installed:
-
-   ```bash
-   ollama pull bespoke-minicheck
-   ```
-
-2. Install the dependencies:
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the program:
-
-   ```bash
-   python main.py
-   ```
-
-4. Enter a document and a claim when prompted:
-
-   ```bash
-   Enter a document: Roses are red.
-
-   Enter a claim: Roses are blue. 
-   ```
-
-   The claim and document are then given to the `bespoke-minicheck` as inputs, which then generates a response (Yes or No) on whether the claim is supported by the document.
-
-   ```bash
-   Is the claim supported by the document according to bespoke-minicheck? No
-   ```
-
-## More Examples
-
-Document ([source](https://en.wikipedia.org/wiki/Apple_I)): 
-> The Apple Computer 1 (Apple-1[a]), later known predominantly as the Apple I(written with a Roman numeral),[b] is an 8-bit motherboard-only personal computer designed by Steve Wozniak[5][6] and released by the Apple Computer Company (now Apple Inc.) in 1976. The company was initially formed to sell the Apple I – its first product – and would later become the world's largest technology company.[7] The idea of starting a company and selling the computer came from Wozniak's friend and Apple co-founder Steve Jobs.[8][9] One of the main innovations of the Apple I was that it included video display terminal circuitry on its circuit board, allowing it to connect to a low-cost composite video monitor or television, instead of an expensive computer terminal, compared to most existing computers at the time.
-
-Claim: 
->The Apple I is a 16-bit computer.
-
-Expected output:
->Is the claim supported by the document according to bespoke-minicheck? **No**
-
-Claim: 
->Apple was originally called the Apple Computer Company.
-
-Expected output:
->Is the claim supported by the document according to bespoke-minicheck? **Yes**
--- a/examples/python-grounded-factuality-simple-check/requirements.txt
+++ b/examples/python-grounded-factuality-simple-check/requirements.txt
@@ -1 +0,0 @@
-ollama
--- a/examples/python-json-datagenerator/predefinedschema.py
+++ b/examples/python-json-datagenerator/predefinedschema.py
@@ -1,31 +0,0 @@
-import requests
-import json
-import random
-
-model = "llama3.2"
-template = {
-  "firstName": "",
-  "lastName": "",
-  "address": {
-    "street": "",
-    "city": "",
-    "state": "",
-    "zipCode": ""
-  },
-  "phoneNumber": ""
-}
-
-prompt = f"generate one realistically believable sample data set of a persons first name, last name, address in the US, and  phone number. \nUse the following template: {json.dumps(template)}."
-
-data = {
-    "prompt": prompt,
-    "model": model,
-    "format": "json",
-    "stream": False,
-    "options": {"temperature": 2.5, "top_p": 0.99, "top_k": 100},
-}
-
-print(f"Generating a sample user")
-response = requests.post("http://localhost:11434/api/generate", json=data, stream=False)
-json_data = json.loads(response.text)
-print(json.dumps(json.loads(json_data["response"]), indent=2))
--- a/examples/python-json-datagenerator/randomaddresses.py
+++ b/examples/python-json-datagenerator/randomaddresses.py
@@ -1,31 +0,0 @@
-import requests
-import json
-import random
-
-countries = [
-    "United States",
-    "United Kingdom",
-    "the Netherlands",
-    "Germany",
-    "Mexico",
-    "Canada",
-    "France",
-]
-country = random.choice(countries)
-model = "llama3.2"
-
-prompt = f"generate one realistically believable sample data set of a persons first name, last name, address in {country}, and phone number. Do not use common names. Respond using JSON. Key names should have no backslashes, values should use plain ascii with no special characters."
-
-data = {
-    "prompt": prompt,
-    "model": model,
-    "format": "json",
-    "stream": False,
-    "options": {"temperature": 2.5, "top_p": 0.99, "top_k": 100},
-}
-
-print(f"Generating a sample user in {country}")
-response = requests.post("http://localhost:11434/api/generate", json=data, stream=False)
-json_data = json.loads(response.text)
-
-print(json.dumps(json.loads(json_data["response"]), indent=2))
--- a/examples/python-json-datagenerator/readme.md
+++ b/examples/python-json-datagenerator/readme.md
@@ -1,60 +0,0 @@
-# JSON Output Example
-
-![llmjson 2023-11-10 15_31_31](https://github.com/ollama/ollama/assets/633681/e599d986-9b4a-4118-81a4-4cfe7e22da25)
-
-There are two python scripts in this example. `randomaddresses.py` generates random addresses from different countries. `predefinedschema.py` sets a template for the model to fill in.
-
-## Running the Example
-
-1. Ensure you have the `llama3.2` model installed:
-
-   ```bash
-   ollama pull llama3.2
-   ```
-
-2. Install the Python Requirements.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the Random Addresses example:
-
-   ```bash
-   python randomaddresses.py
-   ```
-
-4. Run the Predefined Schema example:
-
-   ```bash
-   python predefinedschema.py
-   ```
-
-## Review the Code
-
-Both programs are basically the same, with a different prompt for each, demonstrating two different ideas. The key part of getting JSON out of a model is to state in the prompt or system prompt that it should respond using JSON, and specifying the `format` as `json` in the data body.
-
-```python
-prompt = f"generate one realistically believable sample data set of a persons first name, last name, address in {country}, and  phone number. Do not use common names. Respond using JSON. Key names should with no backslashes, values should use plain ascii with no special characters."
-
-data = {
-    "prompt": prompt,
-    "model": model,
-    "format": "json",
-    "stream": False,
-    "options": {"temperature": 2.5, "top_p": 0.99, "top_k": 100},
-}
-```
-
-When running `randomaddresses.py` you will see that the schema changes and adapts to the chosen country.
-
-In `predefinedschema.py`, a template has been specified in the prompt as well. It's been defined as JSON and then dumped into the prompt string to make it easier to work with.
-
-Both examples turn streaming off so that we end up with the completed JSON all at once. We need to convert the `response.text` to JSON so that when we output it as a string we can set the indent spacing to make the output easy to read.
-
-```python
-response = requests.post("http://localhost:11434/api/generate", json=data, stream=False)
-json_data = json.loads(response.text)
-
-print(json.dumps(json.loads(json_data["response"]), indent=2))
-```
--- a/examples/python-json-datagenerator/requirements.txt
+++ b/examples/python-json-datagenerator/requirements.txt
@@ -1 +0,0 @@
-Requests==2.31.0
--- a/examples/python-loganalysis/Modelfile
+++ b/examples/python-loganalysis/Modelfile
@@ -1,8 +0,0 @@
-FROM codebooga:latest
-
-SYSTEM """
-You are a log file analyzer. You will receive a set of lines from a log file for some software application, find the errors and other interesting aspects of the logs, and explain them so a new user can understand what they mean. If there are any steps they can do to resolve them, list the steps in your answer.
-"""
-
-PARAMETER temperature 0.3
-
--- a/examples/python-loganalysis/loganalysis.py
+++ b/examples/python-loganalysis/loganalysis.py
@@ -1,41 +0,0 @@
-import sys
-import re
-import requests
-import json
-
-# prelines and postlines represent the number of lines of context to include in the output around the error
-prelines = 10
-postlines = 10
-
-def find_errors_in_log_file():
-  if len(sys.argv) < 2:
-    print("Usage: python loganalysis.py <filename>")
-    return
-
-  log_file_path = sys.argv[1]
-  with open(log_file_path, 'r') as log_file:
-    log_lines = log_file.readlines()
-
-  error_logs = []
-  for i, line in enumerate(log_lines):
-      if "error" in line.lower():
-          start_index = max(0, i - prelines)
-          end_index = min(len(log_lines), i + postlines + 1)
-          error_logs.extend(log_lines[start_index:end_index])
-
-  return error_logs
-
-error_logs = find_errors_in_log_file()
-
-data = {
-  "prompt": "\n".join(error_logs), 
-  "model": "mattw/loganalyzer"
-}
-
-response = requests.post("http://localhost:11434/api/generate", json=data, stream=True)
-for line in response.iter_lines():
-  if line:
-    json_data = json.loads(line)
-    if json_data['done'] == False:
-      print(json_data['response'], end='', flush=True)
-
--- a/examples/python-loganalysis/logtest.logfile
+++ b/examples/python-loganalysis/logtest.logfile
@@ -1,32 +0,0 @@
-2023-11-10 07:17:40 /docker-entrypoint.sh: /docker-entrypoint.d/ is not empty, will attempt to perform configuration
-2023-11-10 07:17:40 /docker-entrypoint.sh: Looking for shell scripts in /docker-entrypoint.d/
-2023-11-10 07:17:40 /docker-entrypoint.sh: Launching /docker-entrypoint.d/10-listen-on-ipv6-by-default.sh
-2023-11-10 07:17:40 10-listen-on-ipv6-by-default.sh: info: Getting the checksum of /etc/nginx/conf.d/default.conf
-2023-11-10 07:17:40 10-listen-on-ipv6-by-default.sh: info: Enabled listen on IPv6 in /etc/nginx/conf.d/default.conf
-2023-11-10 07:17:40 /docker-entrypoint.sh: Sourcing /docker-entrypoint.d/15-local-resolvers.envsh
-2023-11-10 07:17:40 /docker-entrypoint.sh: Launching /docker-entrypoint.d/20-envsubst-on-templates.sh
-2023-11-10 07:17:40 /docker-entrypoint.sh: Launching /docker-entrypoint.d/30-tune-worker-processes.sh
-2023-11-10 07:17:40 /docker-entrypoint.sh: Configuration complete; ready for start up
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: using the "epoll" event method
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: nginx/1.25.3
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: built by gcc 12.2.0 (Debian 12.2.0-14) 
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: OS: Linux 6.4.16-linuxkit
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: getrlimit(RLIMIT_NOFILE): 1048576:1048576
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker processes
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 29
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 30
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 31
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 32
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 33
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 34
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 35
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 36
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 37
-2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 38
-2023-11-10 07:17:44 192.168.65.1 - - [10/Nov/2023:13:17:43 +0000] "GET / HTTP/1.1" 200 615 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" "-"
-2023-11-10 07:17:44 2023/11/10 13:17:44 [error] 29#29: *1 open() "/usr/share/nginx/html/favicon.ico" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /favicon.ico HTTP/1.1", host: "localhost:8080", referrer: "http://localhost:8080/"
-2023-11-10 07:17:44 192.168.65.1 - - [10/Nov/2023:13:17:44 +0000] "GET /favicon.ico HTTP/1.1" 404 555 "http://localhost:8080/" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" "-"
-2023-11-10 07:17:50 2023/11/10 13:17:50 [error] 29#29: *1 open() "/usr/share/nginx/html/ahstat" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /ahstat HTTP/1.1", host: "localhost:8080"
-2023-11-10 07:17:50 192.168.65.1 - - [10/Nov/2023:13:17:50 +0000] "GET /ahstat HTTP/1.1" 404 555 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" "-"
-2023-11-10 07:18:53 2023/11/10 13:18:53 [error] 29#29: *1 open() "/usr/share/nginx/html/ahstat" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /ahstat HTTP/1.1", host: "localhost:8080"
-2023-11-10 07:18:53 192.168.65.1 - - [10/Nov/2023:13:18:53 +0000] "GET /ahstat HTTP/1.1" 404 555 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" "-"
--- a/examples/python-loganalysis/readme.md
+++ b/examples/python-loganalysis/readme.md
@@ -1,72 +0,0 @@
-# Log Analysis example
-
-![loganalyzer 2023-11-10 08_53_29](https://github.com/ollama/ollama/assets/633681/ad30f1fc-321f-4953-8914-e30e24db9921)
-
-This example shows one possible way to create a log file analyzer. It uses the model **mattw/loganalyzer** which is based on **codebooga**, a 34b parameter model.
-
-To use it, run:
-
-`python loganalysis.py <logfile>`
-
-You can try this with the `logtest.logfile` file included in this directory.
-
-## Running the Example
-
-1. Ensure you have the `mattw/loganalyzer` model installed:
-
-   ```bash
-   ollama pull mattw/loganalyzer
-   ```
-
-2. Install the Python Requirements.
-
-   ```bash
-   python3 -m venv .venv
-   source .venv/bin/activate
-   pip install -r requirements.txt
-   ```
-
-3. Run the example:
-
-   ```bash
-   python loganalysis.py logtest.logfile
-   ```
-
-## Review the code
-
-The first part of this example is a Modelfile that takes `codebooga` and applies a new System Prompt:
-
-```plaintext
-SYSTEM """
-You are a log file analyzer. You will receive a set of lines from a log file for some software application, find the errors and other interesting aspects of the logs, and explain them so a new user can understand what they mean. If there are any steps they can do to resolve them, list the steps in your answer.
-"""
-```
-
-This model is available at https://ollama.com/mattw/loganalyzer. You can customize it and add to your own namespace using the command `ollama create <namespace/modelname> -f <path-to-modelfile>` then `ollama push <namespace/modelname>`.
-
-Then loganalysis.py scans all the lines in the given log file and searches for the word 'error'. When the word is found, the 10 lines before and after are set as the prompt for a call to the Generate API.
-
-```python
-data = {
-  "prompt": "\n".join(error_logs),
-  "model": "mattw/loganalyzer"
-}
-```
-
-Finally, the streamed output is parsed and the response field in the output is printed to the line.
-
-```python
-response = requests.post("http://localhost:11434/api/generate", json=data, stream=True)
-for line in response.iter_lines():
-  if line:
-    json_data = json.loads(line)
-    if json_data['done'] == False:
-      print(json_data['response'], end='')
-
-```
-
-## Next Steps
-
-There is a lot more that can be done here. This is a simple way to detect errors, looking for the word error. Perhaps it would be interesting to find anomalous activity in the logs. It could be interesting to create embeddings for each line and compare them, looking for similar lines. Or look into applying Levenshtein Distance algorithms to find similar lines to help identify the anomalous lines.
-
-Try different models and different prompts to analyze the data. You could consider adding retrieval augmented generation (RAG) to this to help understand newer log formats.
--- a/examples/python-loganalysis/requirements.txt
+++ b/examples/python-loganalysis/requirements.txt
@@ -1 +0,0 @@
-Requests>=2.32.3
--- a/examples/python-rag-newssummary/README.md
+++ b/examples/python-rag-newssummary/README.md
@@ -1,35 +0,0 @@
-# News Summarizer
-
-This example goes through a series of steps:
-
-  1. You choose a topic area (e.g., "news", "NVidia", "music", etc.).
-  2. Gets the most recent articles on that topic from various sources.
-  3. Uses Ollama to summarize each article.
-  4. Creates chunks of sentences from each article.
-  5. Uses Sentence Transformers to generate embeddings for each of those chunks.
-  6. You enter a question regarding the summaries shown.
-  7. Uses Sentence Transformers to generate an embedding for that question.
-  8. Uses the embedded question to find the most similar chunks.
-  9. Feeds all that to Ollama to generate a good answer to your question based on these news articles.
-
-This example lets you pick from a few different topic areas, then summarize the most recent x articles for that topic. It then creates chunks of sentences from each article and then generates embeddings for each of those chunks.
-
-## Running the Example
-
-1. Ensure you have the `mistral-openorca` model installed:
-
-   ```bash
-   ollama pull mistral-openorca
-   ```
-
-2. Install the Python Requirements.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the example:
-
-   ```bash
-   python summ.py
-   ```
--- a/examples/python-rag-newssummary/requirements.txt
+++ b/examples/python-rag-newssummary/requirements.txt
@@ -1,9 +0,0 @@
-beautifulsoup4==4.12.2
-feedparser==6.0.10
-mattsollamatools==0.0.8
-newspaper3k==0.2.8
-nltk==3.8.1
-numpy==1.24.3
-Requests==2.31.0
-scikit_learn==1.3.0
-sentence_transformers==2.2.2
--- a/examples/python-rag-newssummary/summ.py
+++ b/examples/python-rag-newssummary/summ.py
@@ -1,86 +0,0 @@
-import curses
-import json
-from utils import get_url_for_topic, topic_urls, menu, getUrls, get_summary, getArticleText, knn_search
-import requests
-from sentence_transformers import SentenceTransformer
-from mattsollamatools import chunker
-
-if __name__ == "__main__":
-    chosen_topic = curses.wrapper(menu)
-    print("Here is your news summary:\n")
-    urls = getUrls(chosen_topic, n=5)
-    model = SentenceTransformer('all-MiniLM-L6-v2')
-    allEmbeddings = []
-
-    for url in urls:
-      article={}
-      article['embeddings'] = []
-      article['url'] = url
-      text = getArticleText(url)
-      summary = get_summary(text)
-      chunks = chunker(text)  # Use the chunk_text function from web_utils
-      embeddings = model.encode(chunks)
-      for (chunk, embedding) in zip(chunks, embeddings):
-        item = {}
-        item['source'] = chunk
-        item['embedding'] = embedding.tolist()  # Convert NumPy array to list
-        item['sourcelength'] = len(chunk)
-        article['embeddings'].append(item)
-    
-      allEmbeddings.append(article)
-
-      print(f"{summary}\n")
-
-    
-    while True:
-      context = []
-      # Input a question from the user
-      question = input("Enter your question about the news, or type quit: ")
-
-      if question.lower() == 'quit':
-        break
-
-      # Embed the user's question
-      question_embedding = model.encode([question])
-
-      # Perform KNN search to find the best matches (indices and source text)
-      best_matches = knn_search(question_embedding, allEmbeddings, k=10)
-
-
-      sourcetext=""
-      for i, (index, source_text) in enumerate(best_matches, start=1):
-          sourcetext += f"{i}. Index: {index}, Source Text: {source_text}"
-
-      systemPrompt = f"Only use the following information to answer the question. Do not use anything else: {sourcetext}"
-
-      url = "http://localhost:11434/api/generate"
-
-      payload = {
-      "model": "mistral-openorca",
-      "prompt": question, 
-      "system": systemPrompt,
-      "stream": False, 
-      "context": context
-      }
-
-      # Convert the payload to a JSON string
-      payload_json = json.dumps(payload)
-
-      # Set the headers to specify JSON content
-      headers = {
-          "Content-Type": "application/json"
-      }
-
-      # Send the POST request
-      response = requests.post(url, data=payload_json, headers=headers)
-
-      # Check the response
-      if response.status_code == 200:
-          output = json.loads(response.text)
-          context = output['context']
-          print(output['response']+ "\n")
-          
-
-      else:
-          print(f"Request failed with status code {response.status_code}")
-
--- a/examples/python-rag-newssummary/utils.py
+++ b/examples/python-rag-newssummary/utils.py
@@ -1,108 +0,0 @@
-import curses
-import feedparser
-import requests
-import unicodedata
-import json
-from newspaper import Article
-from bs4 import BeautifulSoup
-from nltk.tokenize import sent_tokenize, word_tokenize
-import numpy as np
-from sklearn.neighbors import NearestNeighbors
-from mattsollamatools import chunker
-
-# Create a dictionary to store topics and their URLs
-topic_urls = {
-    "Mac": "https://9to5mac.com/guides/mac/feed",
-    "News": "http://www.npr.org/rss/rss.php?id=1001",
-    "Nvidia": "https://nvidianews.nvidia.com/releases.xml",
-    "Raspberry Pi": "https://www.raspberrypi.com/news/feed/", 
-    "Music": "https://www.billboard.com/c/music/music-news/feed/"
-}
-
-# Use curses to create a menu of topics
-def menu(stdscr):
-    chosen_topic = get_url_for_topic(stdscr)  
-    url = topic_urls[chosen_topic] if chosen_topic in topic_urls else "Topic not found"
-    
-    stdscr.addstr(len(topic_urls) + 3, 0, f"Selected URL for {chosen_topic}: {url}")
-    stdscr.refresh()
-    
-    return chosen_topic
-
-# You have chosen a topic. Now return the url for that topic
-def get_url_for_topic(stdscr):
-    curses.curs_set(0)  # Hide the cursor
-    stdscr.clear()
-
-    stdscr.addstr(0, 0, "Choose a topic using the arrow keys (Press Enter to select):")
-
-    # Create a list of topics
-    topics = list(topic_urls.keys())
-    current_topic = 0
-
-    while True:
-        for i, topic in enumerate(topics):
-            if i == current_topic:
-                stdscr.addstr(i + 2, 2, f"> {topic}")
-            else:
-                stdscr.addstr(i + 2, 2, f"  {topic}")
-
-        stdscr.refresh()
-
-        key = stdscr.getch()
-
-        if key == curses.KEY_DOWN and current_topic < len(topics) - 1:
-            current_topic += 1
-        elif key == curses.KEY_UP and current_topic > 0:
-            current_topic -= 1
-        elif key == 10:  # Enter key
-            return topic_urls[topics[current_topic]]
-
-# Get the last N URLs from an RSS feed
-def getUrls(feed_url, n=20):
-    feed = feedparser.parse(feed_url)
-    entries = feed.entries[-n:]
-    urls = [entry.link for entry in entries]
-    return urls
-
-# Often there are a bunch of ads and menus on pages for a news article. This uses newspaper3k to get just the text of just the article.
-def getArticleText(url):
-  article = Article(url)
-  article.download()
-  article.parse()
-  return article.text
-
-def get_summary(text):
-  systemPrompt = "Write a concise summary of the text, return your responses with 5 lines that cover the key points of the text given."
-  prompt = text
-  
-  url = "http://localhost:11434/api/generate"
-
-  payload = {
-    "model": "mistral-openorca",
-    "prompt": prompt, 
-    "system": systemPrompt,
-    "stream": False
-  }
-  payload_json = json.dumps(payload)
-  headers = {"Content-Type": "application/json"}
-  response = requests.post(url, data=payload_json, headers=headers)
-
-  return json.loads(response.text)["response"]
-
-# Perform K-nearest neighbors (KNN) search
-def knn_search(question_embedding, embeddings, k=5):
-    X = np.array([item['embedding'] for article in embeddings for item in article['embeddings']])
-    source_texts = [item['source'] for article in embeddings for item in article['embeddings']]
-    
-    # Fit a KNN model on the embeddings
-    knn = NearestNeighbors(n_neighbors=k, metric='cosine')
-    knn.fit(X)
-    
-    # Find the indices and distances of the k-nearest neighbors
-    distances, indices = knn.kneighbors(question_embedding, n_neighbors=k)
-    
-    # Get the indices and source texts of the best matches
-    best_matches = [(indices[0][i], source_texts[indices[0][i]]) for i in range(k)]
-    
-    return best_matches
--- a/examples/python-simplechat/client.py
+++ b/examples/python-simplechat/client.py
@@ -1,48 +0,0 @@
-import json
-import requests
-
-# NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve`
-model = "llama3.2"  # TODO: update this for whatever model you wish to use
-
-
-def chat(messages):
-    r = requests.post(
-        "http://0.0.0.0:11434/api/chat",
-        json={"model": model, "messages": messages, "stream": True},
-	stream=True
-    )
-    r.raise_for_status()
-    output = ""
-
-    for line in r.iter_lines():
-        body = json.loads(line)
-        if "error" in body:
-            raise Exception(body["error"])
-        if body.get("done") is False:
-            message = body.get("message", "")
-            content = message.get("content", "")
-            output += content
-            # the response streams one token at a time, print that as we receive it
-            print(content, end="", flush=True)
-
-        if body.get("done", False):
-            message["content"] = output
-            return message
-
-
-def main():
-    messages = []
-
-    while True:
-        user_input = input("Enter a prompt: ")
-        if not user_input:
-            exit()
-        print()
-        messages.append({"role": "user", "content": user_input})
-        message = chat(messages)
-        messages.append(message)
-        print("\n\n")
-
-
-if __name__ == "__main__":
-    main()
--- a/examples/python-simplechat/readme.md
+++ b/examples/python-simplechat/readme.md
@@ -1,44 +0,0 @@
-# Simple Chat Example
-
-The **chat** endpoint is one of two ways to generate text from an LLM with Ollama, and is introduced in version 0.1.14. At a high level, you provide the endpoint an array of objects with a role and content specified. Then with each output and prompt, you add more of those role/content objects, which builds up the history.
-
-## Running the Example
-
-1. Ensure you have the `llama3.2` model installed:
-
-   ```bash
-   ollama pull llama3.2
-   ```
-
-2. Install the Python Requirements.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the example:
-
-   ```bash
-   python client.py
-   ```
-
-## Review the Code
-
-You can see in the **chat** function that actually calling the endpoint is done simply with:
-
-```python
-r = requests.post(
-  "http://0.0.0.0:11434/api/chat",
-  json={"model": model, "messages": messages, "stream": True},
-)
-```
-
-With the **generate** endpoint, you need to provide a `prompt`. But with **chat**, you provide `messages`. And the resulting stream of responses includes a `message` object with a `content` field.
-
-The final JSON object doesn't provide the full content, so you will need to build the content yourself.
-
-In the **main** function, we collect `user_input` and add it as a message to our messages and that is passed to the chat function. When the LLM is done responding the output is added as another message.
-
-## Next Steps
-
-In this example, all generations are kept. You might want to experiment with summarizing everything older than 10 conversations to enable longer history with less context being used.
--- a/examples/python-simplechat/requirements.txt
+++ b/examples/python-simplechat/requirements.txt
@@ -1 +0,0 @@
-Requests==2.31.0
--- a/examples/python-simplegenerate/README.md
+++ b/examples/python-simplegenerate/README.md
@@ -1,29 +0,0 @@
-# Simple Generate Example
-
-This is a simple example using the **Generate** endpoint.
-
-## Running the Example
-
-1. Ensure you have the `stablelm-zephyr` model installed:
-
-   ```bash
-   ollama pull stablelm-zephyr
-   ```
-
-2. Install the Python Requirements.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the example:
-
-   ```bash
-   python client.py
-   ```
-
-## Review the Code
-
-The **main** function simply asks for input, then passes that to the generate function. The output from generate is then passed back to generate on the next run.
-
-The **generate** function uses `requests.post` to call `/api/generate`, passing the model, prompt, and context. The `generate` endpoint returns a stream of JSON blobs that are then iterated through, looking for the response values. That is then printed out. The final JSON object includes the full context of the conversation so far, and that is the return value from the function.
--- a/examples/python-simplegenerate/client.py
+++ b/examples/python-simplegenerate/client.py
@@ -1,40 +0,0 @@
-import json
-import requests
-
-# NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve`
-model = 'stablelm-zephyr' # TODO: update this for whatever model you wish to use
-
-def generate(prompt, context):
-    r = requests.post('http://localhost:11434/api/generate',
-                      json={
-                          'model': model,
-                          'prompt': prompt,
-                          'context': context,
-                      },
-                      stream=True)
-    r.raise_for_status()
-
-    for line in r.iter_lines():
-        body = json.loads(line)
-        response_part = body.get('response', '')
-        # the response streams one token at a time, print that as we receive it
-        print(response_part, end='', flush=True)
-
-        if 'error' in body:
-            raise Exception(body['error'])
-
-        if body.get('done', False):
-            return body['context']
-
-def main():
-    context = [] # the context stores a conversation history, you can use this to make the model more context aware
-    while True:
-        user_input = input("Enter a prompt: ")
-        if not user_input:
-            exit()
-        print()
-        context = generate(user_input, context)
-        print()
-
-if __name__ == "__main__":
-    main()
--- a/examples/python-simplegenerate/requirements.txt
+++ b/examples/python-simplegenerate/requirements.txt
@@ -1 +0,0 @@
-Requests==2.31.0
--- a/examples/typescript-functioncalling/extractemail.ts
+++ b/examples/typescript-functioncalling/extractemail.ts
@@ -1,118 +0,0 @@
-import { Ollama } from "ollama-node";
-import { readFile } from "fs/promises";
-
-// function to be called on events
-function reportEvents(name: string, date: string, location: string) {
-  const nameString = name ? `${name}` : `an event`;
-  const dateString = date ? ` on ${date}` : ``;
-  const locationString = location ? ` at ${location}` : ``;
-  console.log(`You have an event: ${nameString}${dateString}${locationString}`)
-}
-
-// function to be called on addresses
-function reportAddresses(address) {
-  for (const field in address) {
-    if (address[field]) {
-      if (field === "city") {
-        const city = address.city;
-        const state = address.state ? `, ${address.state}` : '';
-        const zip = address.zip ? ` ${address.zip}` : '';
-        console.log(`${city}${state}${zip}`);
-        break;
-      } else {
-        console.log(`${address[field]}`);
-      }
-    }
-  }
-  console.log(``);
-}
-
-async function main() {
-
-  const ollama = new Ollama();
-
-  const systemprompt = `You will be given a text along with a prompt and a schema. You will have to extract the information requested in the prompt from the text and generate output in JSON observing the schema provided. If the schema shows a type of integer or number, you must only show a integer for that field. A string should always be a valid string. If a value is unknown, leave it empty. Output the JSON with extra spaces to ensure that it pretty prints.`
-
-  const schema = {
-    "eventsQuantity": {
-      "type": "integer",
-      "description": "The number of events in the source text"
-    },
-    "addressesQuantity": {
-      "type": "integer",
-      "description": "The number of addresses in the source text"
-    },
-    "events": [{
-      name: {
-        "type": "string",
-        description: "Name of the event"
-      },
-      "date": {
-        "type": "string",
-        "description": "Date of the event"
-      },
-      "location": {
-        "type": "string",
-        "description": "Location of the event"
-      },
-      "extraInfo": {
-        "type": "string",
-        "description": "Any extra information that is provided about the event."
-      }
-    }],
-    "people": [{
-      "name": {
-        "type": "string",
-        "description": "Name of the person"
-      },
-      "company": {
-        "type": "string",
-        "description": "Name of the company where they work"
-      },
-      "street": {
-        "type": "string",
-        "description": "Street address of the person or company. This is only the street name and the numerical address. Do not include city, state, or zip of the address in this field."
-      },
-      "city": {
-        "type": "string",
-        "description": "City portion of the address of the person or company"
-      },
-      "state": {
-        "type": "string",
-        "description": "State portion of the address of the person or company"
-      },
-      "zip": {
-        "type": "string",
-        "description": "Zip code of the person or company"
-      },
-      "extraInfo": {
-        "type": "string",
-        "description": "Any extra information that is provided about the location."
-      }
-    }]
-  }
-
-  const textcontent = await readFile("./info.txt", "utf-8").then((text) => text.split(" ").slice(0, 2000).join(" "));
-
-  const prompt = `The source text is a series of emails that have been put into a single file. They are separated by three dashes. Review the source text and determine the full address of the person sending each of the emails as well as any events that we need to track. If they provide a company address use that. If any extra info is provided, such as a description of the place, or a floor, add it to extraInfo. The first field in the address JSON is quantity of events and should be set to the number of events tracked and the second field should be set to the number of addresses tracked in the file. Don't stuff an event into the output that isn't an event. Only add data to the mostly appropriate field. Don't make up fields that aren't in the schema. If there isn't a value for a field, use null. Output should be in JSON.\n\nSchema: \n${JSON.stringify(schema, null, 2)}\n\nSource Text:\n${textcontent}`
-
-  await ollama.setModel("neural-chat");
-  ollama.setSystemPrompt(systemprompt);
-  ollama.setJSONFormat(true);
-  const data = await ollama.generate(prompt);
-  const output = JSON.parse(data.output);
-  const events = output.events;
-  const addresses = output.people;
-
-  console.log(`Here are your ${output.eventsQuantity} events:`);
-  for (const event of events) {
-    reportEvents(event.name, event.date, event.location);
-  }
-
-  console.log(`\n\nHere are your ${output.addressesQuantity} addresses:`);
-  for (const address of addresses) {
-    reportAddresses(address);
-  }
-}
-
-main();
--- a/examples/typescript-functioncalling/extractwp.ts
+++ b/examples/typescript-functioncalling/extractwp.ts
@@ -1,38 +0,0 @@
-import { Ollama } from "ollama-node";
-import { readFile } from "fs/promises";
-
-async function main() {
-
-  const ollama = new Ollama();
-
-  // Set the system prompt to prepare the model to receive a prompt and a schema and set some rules for the output.
-  const systemprompt = `You will be given a text along with a prompt and a schema. You will have to extract the information requested in the prompt from the text and generate output in JSON observing the schema provided. If the schema shows a type of integer or number, you must only show a integer for that field. A string should always be a valid string. If a value is unknown, leave it empty. Output the JSON with extra spaces to ensure that it pretty prints.`
-
-  const schema = {
-    "people": [{
-      "name": {
-        "type": "string",
-        "description": "Name of the person"
-      },
-      "title": {
-        "type": "string",
-        "description": "Title of the person"
-      }
-    }],
-  }
-
-  // Depending on the model chosen, you may be limited by the size of the context window, so limit the context to 2000 words.
-  const textcontent = await readFile("./wp.txt", "utf-8").then((text) => text.split(" ").slice(0, 2000).join(" "));
-
-  // Specific instructions for this task
-  const prompt = `Review the source text and determine the 10 most important people to focus on. Then extract the name and title for those people. Output should be in JSON.\n\nSchema: \n${JSON.stringify(schema, null, 2)}\n\nSource Text:\n${textcontent}`
-
-  await ollama.setModel("neural-chat");
-  ollama.setSystemPrompt(systemprompt);
-
-  // setJSONFormat is the equivalent of setting 'format: json' in the API
-  ollama.setJSONFormat(true);
-  await ollama.streamingGenerate(prompt, (word) => { process.stdout.write(word) })
-}
-
-main();
--- a/examples/typescript-functioncalling/info.txt
+++ b/examples/typescript-functioncalling/info.txt
@@ -1,17 +0,0 @@
---
-Hi matt, 
-
-thanks for letting me know that you are going to come today, November 16, for my tea party. My address is 123 Falk St on Bainbridge Island. I live in the house with the red door. I will be home all day so just come by whenever you want.
-
-Fred
-
---
-Great, send the check to our office at 1917 1st St, Seattle, WA 98101. I will let you know when we receive it.
-
-Mark Richardson
-Big Corp
---
-We are looking forward to seeing you at our Local AI Meetup. It will be held on December 3. It will be at the offices of Enormous Co. Our address is 344 1st Ave, Seattle, WA 98101. We will be meeting in the conference room on the 3rd floor.
-
-Barbara Reilly
-Enormous Co.
--- a/examples/typescript-functioncalling/package-lock.json
+++ b/examples/typescript-functioncalling/package-lock.json
@@ -1,519 +0,0 @@
-{
-  "name": "typescript-functioncalling",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "dependencies": {
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-      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA=="
-    }
-  }
-}
--- a/examples/typescript-functioncalling/package.json
+++ b/examples/typescript-functioncalling/package.json
@@ -1,9 +0,0 @@
-{
-  "dependencies": {
-    "ollama-node": "^0.1.27"
-  },
-  "devDependencies": {
-    "tsx": "^4.1.2",
-    "typescript": "^5.2.2"
-  }
-}
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
ParthSareen	b4de2e9189	change name to context_length	2025-02-07 11:50:38 -08:00
ParthSareen	61a5254115	context_window and addressing comments	2025-02-05 11:26:55 -08:00
ParthSareen	53d2cf37d2	update docs	2025-02-04 15:17:16 -08:00
ParthSareen	75f88e7aac	Update docs	2025-02-04 10:47:32 -08:00
ParthSareen	4982089c84	Fix formatting	2025-01-30 13:53:24 -08:00
Parth Sareen	8c231b0826	Update openai/openai.go Co-authored-by: Michael Yang <mxyng@pm.me>	2025-01-30 13:50:25 -08:00
ParthSareen	16abd181a9	remove context shifting with max tokens and update docs	2025-01-30 13:48:24 -08:00
ParthSareen	5c2f35d846	Add tests	2025-01-30 13:16:15 -08:00
ParthSareen	6de3227841	Cleanup api	2025-01-30 13:15:57 -08:00
ParthSareen	35e97db03b	set num_ctx through extra body	2025-01-29 13:13:11 -08:00
Xiaofu Huang	2ef3c803a1	readme: add AI Toolkit for VSCode to community integrations (#8604 )	2025-01-27 00:36:23 -08:00
Matěj Štágl	453e4d090b	readme: add LlmTornado to community integrations (#8551 )	2025-01-25 01:04:07 -08:00
Daniel Jalkut	ca2f9843c8	docs: remove reference to the deleted examples folder (#8524 )	2025-01-22 22:52:15 -08:00
frob	294b6f5a22	docs: remove tfs_z option from documentation (#8515 )	2025-01-21 09:28:59 -08:00
EndoTheDev	7bb356c680	docs: update suspend header in gpu.md (#8487 )	2025-01-19 18:45:35 -08:00
Jannik Maierhöfer	021817e59a	readme: add link to Langfuse (#8455 )	2025-01-16 22:41:12 -08:00
Patrick Devine	a420a453b4	fix default modelfile for create (#8452 )	2025-01-16 01:14:04 -08:00
Jeffrey Morgan	42cf4db601	parser: fix parsing Modelfiles with multiple FROM commands (#8449 )	2025-01-16 00:14:04 -08:00
Josh	93a8daf285	convert: import support for command-r models from safetensors (#6063 ) --------- Co-authored-by: Patrick Devine <patrick@infrahq.com>	2025-01-15 16:31:22 -08:00
Gloryjaw	a041b4df7c	docs: fix path to examples (#8438 )	2025-01-15 11:49:12 -08:00
Patrick Devine	2539f2dbf9	Fix absolute path names + gguf detection (#8428 )	2025-01-14 19:01:24 -08:00
Jeffrey Morgan	61676fb506	llama: move grammar tests to llama_test.go (#8411 )	2025-01-14 12:55:45 -08:00
Bruce MacDonald	f6f3713001	convert: qwen2 from safetensors (#8408 ) Add native support for converting Qwen2 family models (including Qwen2.5) from safetensors to gguf format so we can run it.	2025-01-14 10:34:37 -08:00
Steve Berdy	a30f347201	readme: add LangChain for .NET to community integrations (#8352 )	2025-01-14 09:37:35 -08:00
Jeffrey Morgan	74ea4fb604	remove .prettierrc.json (#8413 )	2025-01-14 09:30:34 -08:00
Jeffrey Morgan	6982e9cc96	readme: remove link to missing page	2025-01-13 18:56:31 -08:00
Patrick Devine	ab39872cb4	add new create api doc (#8388 )	2025-01-13 17:30:24 -08:00
Parth Sareen	84a2314463	examples: remove codified examples (#8267 )	2025-01-13 11:26:22 -08:00
Jeffrey Morgan	17fcdea698	readme: move discord link	2025-01-12 22:45:47 -08:00
Patrick Devine	32bd37adf8	make the modelfile path relative for `ollama create` (#8380 )	2025-01-10 16:14:08 -08:00
Michael Yang	9446c2c902	Merge pull request #8196 from ollama/mxyng/gods-v2 chore: upgrade to gods v2	2025-01-10 13:50:11 -08:00
Jeffrey Morgan	9aa141d023	readme: remove discord badge image for now	2025-01-09 22:02:18 -08:00
Patrick Devine	8bccae4f92	show a more descriptive error in the client if it is newer than the server (#8351 )	2025-01-09 10:12:30 -08:00
isamu arimoto	6ae2adc1af	openai: accept additional headers to fix CORS errors (#8343 )	2025-01-08 11:28:11 -08:00
Jeffrey Morgan	1deafd8254	llama: update vendored code to commit 46e3556 (#8308 )	2025-01-08 11:22:01 -08:00
Michael	57f038ec7b	readme: add phi4 model (#8350 )	2025-01-08 11:21:39 -08:00
frob	cdf3a181dc	Add CUSTOM_CPU_FLAGS to Dockerfile. (#8284 ) * Add CUSTOM_CPU_FLAGS. * fix golangci-lint error. --------- Co-authored-by: Richard Lyons <rick@frob.com.au>	2025-01-06 09:17:19 -08:00
Ubaldo Porcheddu	3919f4ba3d	llama: fix runner api example url in README.md (#8307 )	2025-01-04 15:45:16 -08:00
Bruce MacDonald	2d33c4e97d	discover: remove leading new-line for linter	2025-01-03 12:03:58 -08:00
Bruce MacDonald	29a8975c66	api: remove unused create fields These fields are deprecated, but specifying them will not do anything. Removing them as the other deprecated fields will still work, but these do not, so they dont match our existing pattern.	2025-01-03 12:03:58 -08:00
Patrick Devine	86a622cbdc	Update the /api/create endpoint to use JSON (#7935 ) Replaces `POST /api/create` to use JSON instead of a Modelfile. This is a breaking change.	2024-12-31 18:02:30 -08:00
Jeffrey Morgan	459d822b51	readme: link header to ollama.com	2024-12-29 17:36:07 -05:00
Simon Schampijer	844899440a	examples: updated deprecated imports (#3602 )	2024-12-29 14:36:25 -05:00
Anas Khan	103db4216d	docs: add /api/version endpoint documentation (#8082 ) Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>	2024-12-29 14:33:44 -05:00
Jeffrey Morgan	6daddcde01	readme: update import header	2024-12-29 14:12:23 -05:00
Emilien Lancelot	07f7e69b36	readme: add Yacana multi-agent framework to community integrations (#7259 )	2024-12-28 15:05:57 -05:00
CIIDMike	b68e8e5727	docs: add syntax highlighting on Go template code blocks (#8215 )	2024-12-27 13:17:49 -05:00
Adarsh Mishra	369fb529e2	readme: add TextLLaMA to community integrations	2024-12-27 13:16:06 -05:00
Jared Donnell	023e4bca14	readme: add neollama to terminal section of community integrations (#8242 )	2024-12-25 17:16:11 -05:00
aritra saha	51af455f62	readme: add alpaca client application to community integrations (#8227 )	2024-12-24 23:05:35 -05:00
Emanuil Rusev	ffe3549064	readme: add IntelliBar to community integrations (#7950 )	2024-12-23 12:04:18 -05:00
湛露先生	928de9050e	server: reuse InvalidModelNameErrMsg type (#8163 )	2024-12-23 10:38:34 -05:00
ItzCrazyKns	36aea6154a	readme: add Perplexica to community-integrations (#8198 )	2024-12-22 20:04:01 -05:00
Patrick Devine	dd352ab27f	fix crash bug with /save when quotes are used (#8208 )	2024-12-21 22:31:37 -08:00
Michael Yang	cb40d60469	chore: upgrade to gods v2 gods v2 uses go generics rather than interfaces which simplifies the code considerably	2024-12-21 00:05:16 -08:00
Patrick Devine	d8bab8ea44	remove tutorials.md which pointed to removed tutorials (#8189 )	2024-12-20 14:04:20 -08:00
Squishedmac	9ab62eb96f	update golang.org/x dependencies (#8172 )	2024-12-20 09:29:30 -08:00
Parth Sareen	290cf2040a	llama: test key order preservation in schema_to_grammar (#8078 ) This change adds a test to catch a regression in schema_to_grammar where the order of keys in the JSON schema is not preserved in the generated grammar, which is critical for step-by-step reasoning.	2024-12-18 19:44:50 -08:00
Jeffrey Morgan	a72f2dce45	scripts: sign renamed macOS binary (#8131 )	2024-12-17 18:03:49 -08:00
Jesse Gross	08a832b482	llama: Ensure KV cache is fully defragmented. Sometimes the KV cache requires defragmentation even without triggering the threshold heuristic. In this case, decoding will not being able to find a KV cache slot. This is particularly difficult for the caller to handle if it happens in between ubatches. To avoid this, we should immediately trigger a defrag. In addition, a heavily fragmented cache can require more than max_moves to defragment. Currently, we stop when we hit the limit but this can leave a cache that still does not have adequate space even after defragmentation is triggered. Instead, we should do multiple batches of processing until everything is complete. Fixes #7949	2024-12-17 14:01:19 -08:00
Blake Mizerany	2ddc32d5c5	llm: do not error on "null" format (#8139 ) This fixes another regression in the previous commit that fixed other known bugs.	2024-12-17 09:49:37 -08:00
Jascha Beste	2cde4b8817	readme: change getting started guide link for pgai (#8119 )	2024-12-16 22:13:23 -08:00
Blake Mizerany	87f0a49fe6	llm: do not silently fail for supplied, but invalid formats (#8130 ) Changes in #8002 introduced fixes for bugs with mangling JSON Schemas. It also fixed a bug where the server would silently fail when clients requested invalid formats. It also, unfortunately, introduced a bug where the server would reject requests with an empty format, which should be allowed. The change in #8127 updated the code to allow the empty format, but also reintroduced the regression where the server would silently fail when the format was set, but invalid. This commit fixes both regressions. The server does not reject the empty format, but it does reject invalid formats. It also adds tests to help us catch regressions in the future. Also, the updated code provides a more detailed error message when a client sends a non-empty, but invalid format, echoing the invalid format in the response. This commits also takes the opportunity to remove superfluous linter checks.	2024-12-16 21:57:49 -08:00
Jeffrey Morgan	0f06a6daa7	llm: loosen format check to default to no format (#8127 )	2024-12-16 18:45:46 -08:00
Daniel Hiltgen	8f805dd74b	darwin: restore multiple runners for x86 (#8125 ) In 0.5.2 we simplified packaging to have avx only for macos x86. It looks like there may still be some non-AVX systems out there, so this puts back the prior logic of building no-AVX for the primary binary, and now 2 runners for avx and avx2. These will be packaged in the App bundle only, so the stand-alone binary will now be without AVX support on macos. On arm, we'll also see these runners reported as available in the log, but they're dormant and will never be used at runtime.	2024-12-16 18:45:02 -08:00
Michael	89d5e2f2fd	readme: example/get started guide for pgai with Ollama (#8115 ) readme: example/get started guide for pgai with Ollama	2024-12-16 17:14:37 +08:00
Jascha Beste	297ada6c87	readme: add pgai to readme for semantic search (#8028 ) * docs: switch around database integrations order and link to quickstart * docs: link to blog post in example readme * chore: link to main readme * readme: removing example to link externally readme: removing example to link externally so we don't have to keep this example up-to-date ---------	2024-12-16 17:02:28 +08:00
Patrick Devine	8c9fb8eb73	imageproc mllama refactor (#7537 ) Refactor mllama image processing code, and add pixtral and qwen2vl	2024-12-14 19:50:15 -08:00
Daniel Hiltgen	b75ccfc5ec	ci: be more aggressive on parallelism in build (#8102 )	2024-12-14 14:56:05 -08:00
Jeffrey Morgan	7a81daf026	llama: update vendor code to commit ba1cb19c (#8101 )	2024-12-14 14:55:51 -08:00
Daniel Hiltgen	60f75560a2	runner: switch logging back to stderr (#8091 ) This puts the low-level runner logging back on stderr for consistency with prior releases	2024-12-13 14:36:50 -08:00
Anuraag (Rag) Agrawal	e28f2d4900	openai: return usage as final chunk for streams (#6784 ) * openai: return usage as final chunk for streams --------- Co-authored-by: ParthSareen <parth.sareen@ollama.com>	2024-12-12 17:09:30 -08:00
Pascal Patry	c216850523	llama: parse JSON schema using nlohmann::ordered_json to maintain ordering (#8071 )	2024-12-12 09:57:28 -08:00
Parth Sareen	18f6a98bd6	llama: enable JSON schema key ordering for generating grammars (#8055 )	2024-12-11 17:17:36 -08:00
Blake Mizerany	b1fd7fef86	server: more support for mixed-case model names (#8017 ) Fixes #7944	2024-12-11 15:29:59 -08:00
Daniel Hiltgen	36d111e788	ci: fix linux version (#8054 ) Pass through the version override so the makefiles use it	2024-12-11 14:09:57 -08:00
Blake Mizerany	9039c821a2	llama: preserve field order in user-defined JSON schemas (#8002 ) Previously we decoded and re-encoded JSON schemas during validation, which served no purpose since json.RawMessage already validates JSON syntax. Worse, the re-encoding lost field ordering from the original schema, which affects inference quality during step-by-step reasoning. While fixing this ordering issue by using json.RawMessage directly, testing revealed that schema_to_grammar (from llama.cpp) also fails to preserve field order during grammar generation. This appears to be the root cause of inference degradation. This change prevents us from mangling the user's original schema order, but we still need to address the ordering issue in schema_to_grammar. That will be a separate change. Updates #7978	2024-12-11 14:07:30 -08:00