Move quantization to new backend (#10363)

* Move quantization logic to GGML via new backend

This moves the model aware logic to Go code and calls GGMLs quantization code for model creation.

* Remove "add model quantizations"

This is no longer needed now that quantization is implemented in Go+GGML code directly.

cmd/cmd.go

@@ -162,7 +162,11 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		if resp.Digest != "" {
 			bar, ok := bars[resp.Digest]
 			if !ok {
-				bar = progress.NewBar(fmt.Sprintf("pulling %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
+				msg := resp.Status
+				if msg == "" {
+					msg = fmt.Sprintf("pulling %s...", resp.Digest[7:19])
+				}
+				bar = progress.NewBar(msg, resp.Total, resp.Completed)
 				bars[resp.Digest] = bar
 				p.Add(resp.Digest, bar)
 			}

convert/convert.go

@@ -4,9 +4,9 @@ import (
 	"encoding/json"
 	"errors"
 	"fmt"
-	"io"
 	"io/fs"
 	"log/slog"
+	"os"
 	"slices"
 	"strings"
 
@@ -89,7 +89,7 @@ type ModelConverter interface {
 	// KV maps parameters to LLM key-values
 	KV(*Tokenizer) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
-	Tensors([]Tensor) []ggml.Tensor
+	Tensors([]Tensor) []*ggml.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
 	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
 	Replacements() []string
@@ -106,13 +106,13 @@ type AdapterConverter interface {
 	// KV maps parameters to LLM key-values
 	KV(ggml.KV) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
-	Tensors([]Tensor) []ggml.Tensor
+	Tensors([]Tensor) []*ggml.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
 	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
 	Replacements() []string
 }
 
-func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV ggml.KV) error {
+func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
 	bts, err := fs.ReadFile(fsys, "adapter_config.json")
 	if err != nil {
 		return err
@@ -147,14 +147,14 @@ func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV ggml.KV) error {
 		return err
 	}
 
-	return writeFile(ws, conv.KV(baseKV), conv.Tensors(ts))
+	return writeFile(f, conv.KV(baseKV), conv.Tensors(ts))
 }
 
 // Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
 // and files it finds in the input path.
 // Supported input model formats include safetensors.
 // Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
-func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
+func ConvertModel(fsys fs.FS, f *os.File) error {
 	bts, err := fs.ReadFile(fsys, "config.json")
 	if err != nil {
 		return err
@@ -239,13 +239,13 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 		return err
 	}
 
-	return writeFile(ws, conv.KV(t), conv.Tensors(ts))
+	return writeFile(f, conv.KV(t), conv.Tensors(ts))
 }
 
-func writeFile(ws io.WriteSeeker, kv ggml.KV, ts []ggml.Tensor) error {
+func writeFile(f *os.File, kv ggml.KV, ts []*ggml.Tensor) error {
 	for i := range ts {
 		ts[i].Shape = slices.Clone(ts[i].Shape)
 		slices.Reverse(ts[i].Shape)
 	}
-	return ggml.WriteGGUF(ws, kv, ts)
+	return ggml.WriteGGUF(f, kv, ts)
 }

convert/convert_bert.go

@@ -132,8 +132,8 @@ func (p *bertModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *bertModel) Tensors(ts []Tensor) []ggml.Tensor {
+func (p *bertModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []ggml.Tensor
+	var out []*ggml.Tensor
 	for _, t := range ts {
 		if slices.Contains([]string{
 			"embeddings.position_ids",
@@ -143,7 +143,7 @@ func (p *bertModel) Tensors(ts []Tensor) []ggml.Tensor {
 			continue
 		}
 
-		out = append(out, ggml.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_commandr.go

@@ -43,10 +43,10 @@ func (p *commandrModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *commandrModel) Tensors(ts []Tensor) []ggml.Tensor {
+func (p *commandrModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []ggml.Tensor
+	var out []*ggml.Tensor
 	for _, t := range ts {
-		out = append(out, ggml.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma.go

@@ -42,14 +42,14 @@ func (p *gemmaModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *gemmaModel) Tensors(ts []Tensor) []ggml.Tensor {
+func (p *gemmaModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []ggml.Tensor
+	var out []*ggml.Tensor
 	for _, t := range ts {
 		if !strings.HasPrefix(t.Name(), "v.") && strings.HasSuffix(t.Name(), "_norm.weight") {
 			t.SetRepacker(p.addOne)
 		}
 
-		out = append(out, ggml.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma2_adapter.go

@@ -21,8 +21,8 @@ func (p *gemma2Adapter) KV(baseKV ggml.KV) ggml.KV {
 	return kv
 }
 
-func (p *gemma2Adapter) Tensors(ts []Tensor) []ggml.Tensor {
+func (p *gemma2Adapter) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []ggml.Tensor
+	var out []*ggml.Tensor
 	for _, t := range ts {
 		shape := t.Shape()
 		if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
@@ -31,7 +31,7 @@ func (p *gemma2Adapter) Tensors(ts []Tensor) []ggml.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, ggml.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_llama.go

@@ -126,11 +126,11 @@ func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *llamaModel) Tensors(ts []Tensor) []ggml.Tensor {
+func (p *llamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []ggml.Tensor
+	var out []*ggml.Tensor
 
 	if p.RopeScaling.factors != nil {
-		out = append(out, ggml.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     "rope_freqs.weight",
 			Kind:     0,
 			Shape:    []uint64{uint64(len(p.RopeScaling.factors))},
@@ -145,7 +145,7 @@ func (p *llamaModel) Tensors(ts []Tensor) []ggml.Tensor {
 			}
 		}
 
-		out = append(out, ggml.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_llama4.go

@@ -88,13 +88,13 @@ func (p *llama4Model) Replacements() []string {
 }
 
 // Tensors implements ModelConverter.
-func (p *llama4Model) Tensors(ts []Tensor) []ggml.Tensor {
+func (p *llama4Model) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []ggml.Tensor
+	var out []*ggml.Tensor
 
 	var textTensors []Tensor
 	for _, t := range ts {
 		if strings.HasPrefix(t.Name(), "v.") || strings.HasPrefix(t.Name(), "mm.") {
-			out = append(out, ggml.Tensor{
+			out = append(out, &ggml.Tensor{
 				Name:     t.Name(),
 				Kind:     t.Kind(),
 				Shape:    t.Shape(),
@@ -112,7 +112,7 @@ func (p *llama4Model) Tensors(ts []Tensor) []ggml.Tensor {
 				// clone tensor since we need separate repackers
 				tt := t.Clone()
 				tt.SetRepacker(p.repack(nil, nil, tensor.S(i*halfDim, (i+1)*halfDim)))
-				out = append(out, ggml.Tensor{
+				out = append(out, &ggml.Tensor{
 					Name:     strings.ReplaceAll(tt.Name(), "ffn_gate_up_exps", name),
 					Kind:     tt.Kind(),
 					Shape:    newShape,
@@ -125,7 +125,7 @@ func (p *llama4Model) Tensors(ts []Tensor) []ggml.Tensor {
 			t.SetRepacker(p.repack())
 			newShape := slices.Clone(t.Shape())
 			newShape[1], newShape[2] = newShape[2], newShape[1]
-			out = append(out, ggml.Tensor{
+			out = append(out, &ggml.Tensor{
 				Name:     t.Name(),
 				Kind:     t.Kind(),
 				Shape:    newShape,

convert/convert_llama_adapter.go

@@ -29,8 +29,8 @@ func (p *llamaAdapter) KV(baseKV ggml.KV) ggml.KV {
 	return kv
 }
 
-func (p *llamaAdapter) Tensors(ts []Tensor) []ggml.Tensor {
+func (p *llamaAdapter) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []ggml.Tensor
+	var out []*ggml.Tensor
 	for _, t := range ts {
 		shape := t.Shape()
 		if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
@@ -41,7 +41,7 @@ func (p *llamaAdapter) Tensors(ts []Tensor) []ggml.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, ggml.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    shape,

convert/convert_mistral.go

@@ -89,8 +89,8 @@ func (p *mistral3Model) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *mistral3Model) Tensors(ts []Tensor) []ggml.Tensor {
+func (p *mistral3Model) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []ggml.Tensor
+	var out []*ggml.Tensor
 
 	for _, t := range ts {
 		if !strings.HasPrefix(t.Name(), "v.") {
@@ -100,7 +100,7 @@ func (p *mistral3Model) Tensors(ts []Tensor) []ggml.Tensor {
 			}
 		}
 
-		out = append(out, ggml.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_mixtral.go

@@ -29,7 +29,7 @@ func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *mixtralModel) Tensors(ts []Tensor) []ggml.Tensor {
+func (p *mixtralModel) Tensors(ts []Tensor) []*ggml.Tensor {
 	oldnew := []string{
 		"model.layers", "blk",
 		"w1", "ffn_gate_exps",
@@ -56,10 +56,10 @@ func (p *mixtralModel) Tensors(ts []Tensor) []ggml.Tensor {
 		return true
 	})
 
-	var out []ggml.Tensor
+	var out []*ggml.Tensor
 	for n, e := range experts {
 		// TODO(mxyng): sanity check experts
-		out = append(out, ggml.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     n,
 			Kind:     e[0].Kind(),
 			Shape:    append([]uint64{uint64(len(e))}, e[0].Shape()...),

convert/convert_phi3.go

@@ -68,19 +68,19 @@ func (p *phi3Model) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *phi3Model) Tensors(ts []Tensor) []ggml.Tensor {
+func (p *phi3Model) Tensors(ts []Tensor) []*ggml.Tensor {
 	var addRopeFactors sync.Once
 
-	out := make([]ggml.Tensor, 0, len(ts)+2)
+	out := make([]*ggml.Tensor, 0, len(ts)+2)
 	for _, t := range ts {
 		if strings.HasPrefix(t.Name(), "blk.0.") {
 			addRopeFactors.Do(func() {
-				out = append(out, ggml.Tensor{
+				out = append(out, &ggml.Tensor{
 					Name:     "rope_factors_long.weight",
 					Kind:     0,
 					Shape:    []uint64{uint64(len(p.RopeScaling.LongFactor))},
 					WriterTo: p.RopeScaling.LongFactor,
-				}, ggml.Tensor{
+				}, &ggml.Tensor{
 					Name:     "rope_factors_short.weight",
 					Kind:     0,
 					Shape:    []uint64{uint64(len(p.RopeScaling.ShortFactor))},
@@ -89,7 +89,7 @@ func (p *phi3Model) Tensors(ts []Tensor) []ggml.Tensor {
 			})
 		}
 
-		out = append(out, ggml.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_qwen2.go

@@ -45,10 +45,10 @@ func (q *qwen2Model) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (q *qwen2Model) Tensors(ts []Tensor) []ggml.Tensor {
+func (q *qwen2Model) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []ggml.Tensor
+	var out []*ggml.Tensor
 	for _, t := range ts {
-		out = append(out, ggml.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

fs/ggml/ggml.go

@@ -36,12 +36,12 @@ func (kv KV) ParameterCount() uint64 {
 	return keyValue(kv, "general.parameter_count", uint64(0))
 }
 
-func (kv KV) FileType() fileType {
+func (kv KV) FileType() FileType {
 	if t := kv.Uint("general.file_type"); t > 0 {
-		return fileType(t)
+		return FileType(t)
 	}
 
-	return fileTypeUnknown
+	return FileTypeUnknown
 }
 
 func (kv KV) BlockCount() uint64 {
@@ -226,7 +226,11 @@ func (t Tensor) block() (n int) {
 }
 
 func (t Tensor) blockSize() uint64 {
-	switch t.Kind {
+	return (TensorType)(t.Kind).BlockSize()
+}
+
+func (t TensorType) BlockSize() uint64 {
+	switch t {
 	case
 		0,  // F32
 		1,  // F16
@@ -252,73 +256,77 @@ func (t Tensor) blockSize() uint64 {
 }
 
 func (t Tensor) typeSize() uint64 {
-	blockSize := t.blockSize()
+	return TensorType(t.Kind).TypeSize()
+}
 
-	switch t.Kind {
+func (t TensorType) TypeSize() uint64 {
-	case 0: // FP32
+	blockSize := t.BlockSize()
+
+	switch t {
+	case TensorTypeF32:
 		return 4
-	case 1: // FP16
+	case TensorTypeF16:
 		return 2
-	case 2: // Q4_0
+	case TensorTypeQ4_0:
 		return 2 + blockSize/2
-	case 3: // Q4_1
+	case TensorTypeQ4_1:
 		return 2 + 2 + blockSize/2
-	case 6: // Q5_0
+	case TensorTypeQ5_0:
 		return 2 + 4 + blockSize/2
-	case 7: // Q5_1
+	case TensorTypeQ5_1:
 		return 2 + 2 + 4 + blockSize/2
-	case 8: // Q8_0
+	case TensorTypeQ8_0:
 		return 2 + blockSize
-	case 9: // Q8_1
+	case TensorTypeQ8_1:
 		return 2 + 2 + blockSize
-	case 10: // Q2_K
+	case TensorTypeQ2_K:
 		return blockSize/16 + blockSize/4 + 2 + 2
-	case 11: // Q3_K
+	case TensorTypeQ3_K:
 		return blockSize/8 + blockSize/4 + 12 + 2
-	case 12: // Q4_K
+	case TensorTypeQ4_K:
 		return 2 + 2 + 12 + blockSize/2
-	case 13: // Q5_K
+	case TensorTypeQ5_K:
 		return 2 + 2 + 12 + blockSize/8 + blockSize/2
-	case 14: // Q6_K
+	case TensorTypeQ6_K:
 		return blockSize/2 + blockSize/4 + blockSize/16 + 2
-	case 15: // Q8_K
+	case TensorTypeQ8_K:
 		return 4 + blockSize + 2*blockSize/16
-	case 16: // IQ2_XXS
+	case tensorTypeIQ2_XXS:
 		return 2 + 2*blockSize/8
-	case 17: // IQ2_XS
+	case tensorTypeIQ2_XS:
 		return 2 + 2*blockSize/8 + blockSize/32
-	case 18: // IQ3_XXS
+	case tensorTypeIQ3_XXS:
 		return 2 + blockSize/4 + blockSize/8
-	case 19: // IQ1_S
+	case tensorTypeIQ1_S:
 		return 2 + blockSize/8 + blockSize/16
-	case 20: // IQ4_NL
+	case tensorTypeIQ4_NL:
 		return 2 + blockSize/2
-	case 21: // IQ3_S
+	case tensorTypeIQ3_S:
 		return 2 + blockSize/4 + blockSize/8 + blockSize/32 + 4
-	case 22: // IQ2_S
+	case tensorTypeIQ2_S:
 		return 2 + blockSize/4 + blockSize/16
-	case 23: // IQ4_XS
+	case tensorTypeIQ4_XS:
 		return 2 + 2 + blockSize/2 + blockSize/64
-	case 24: // I8
+	case TensorTypeI8:
 		return 1
-	case 25: // I16
+	case TensorTypeI16:
 		return 2
-	case 26: // I32
+	case TensorTypeI32:
 		return 4
-	case 27: // I64
+	case TensorTypeI64:
 		return 8
-	case 28: // F64
+	case TensorTypeF64:
 		return 8
-	case 29: // IQ1_M
+	case tensorTypeIQ1_M:
 		return blockSize/8 + blockSize/16 + blockSize/32
-	case 30: // BF16
+	case TensorTypeBF16:
 		return 2
 	default:
 		return 0
 	}
 }
 
-func (t Tensor) parameters() uint64 {
+func (t Tensor) Elements() uint64 {
 	var count uint64 = 1
 	for _, n := range t.Shape {
 		count *= n
@@ -327,11 +335,11 @@ func (t Tensor) parameters() uint64 {
 }
 
 func (t Tensor) Size() uint64 {
-	return t.parameters() * t.typeSize() / t.blockSize()
+	return t.Elements() * t.typeSize() / t.blockSize()
 }
 
 func (t Tensor) Type() string {
-	return fileType(t.Kind).String()
+	return TensorType(t.Kind).String()
 }
 
 type container interface {
@@ -480,7 +488,7 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 		var ropeFreqsCount uint64
 		if ropeFreqs, ok := f.Tensors().GroupLayers()["rope_freqs"]; ok {
 			if ropeFreqsWeights, ok := ropeFreqs["weights"]; ok {
-				ropeFreqsCount = ropeFreqsWeights.parameters()
+				ropeFreqsCount = ropeFreqsWeights.Elements()
 			}
 		}
 

fs/ggml/gguf.go

@@ -9,8 +9,12 @@ import (
 	"io"
 	"log/slog"
 	"maps"
+	"os"
+	"runtime"
 	"slices"
 	"strings"
+
+	"golang.org/x/sync/errgroup"
 )
 
 type containerGGUF struct {
@@ -225,7 +229,7 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 		}
 
 		llm.tensors = append(llm.tensors, &tensor)
-		llm.parameters += tensor.parameters()
+		llm.parameters += tensor.Elements()
 	}
 
 	// patch KV with parameter count
@@ -488,25 +492,38 @@ func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
 		return err
 	}
 
+	if t == ggufTypeString {
+		for _, e := range any(s).([]string) {
+			if err := binary.Write(w, binary.LittleEndian, uint64(len(e))); err != nil {
+				return err
+			}
+
+			if err := binary.Write(w, binary.LittleEndian, []byte(e)); err != nil {
+				return err
+			}
+		}
+		return nil
+	}
+
 	return binary.Write(w, binary.LittleEndian, s)
 }
 
-func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
+func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
 	alignment := kv.Uint("general.alignment", 32)
 
-	if err := binary.Write(ws, binary.LittleEndian, []byte("GGUF")); err != nil {
+	if err := binary.Write(f, binary.LittleEndian, []byte("GGUF")); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, binary.LittleEndian, uint32(3)); err != nil {
+	if err := binary.Write(f, binary.LittleEndian, uint32(3)); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(ts))); err != nil {
+	if err := binary.Write(f, binary.LittleEndian, uint64(len(ts))); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(kv))); err != nil {
+	if err := binary.Write(f, binary.LittleEndian, uint64(len(kv))); err != nil {
 		return err
 	}
 
@@ -514,12 +531,12 @@ func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
 	slices.Sort(keys)
 
 	for _, key := range keys {
-		if err := ggufWriteKV(ws, key, kv[key]); err != nil {
+		if err := ggufWriteKV(f, key, kv[key]); err != nil {
 			return err
 		}
 	}
 
-	slices.SortStableFunc(ts, func(a, b Tensor) int {
+	slices.SortStableFunc(ts, func(a, b *Tensor) int {
 		if i, j := a.block(), b.block(); i < 0 && j > 0 {
 			return 1
 		} else if i > 0 && j < 0 {
@@ -530,22 +547,34 @@ func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
 	})
 
 	var s uint64
-	for _, t := range ts {
+	for i := range ts {
-		t.Offset = s
+		ts[i].Offset = s
-		if err := ggufWriteTensorInfo(ws, t); err != nil {
+		if err := ggufWriteTensorInfo(f, ts[i]); err != nil {
 			return err
 		}
-		s += t.Size()
+		s += ts[i].Size()
 		s += uint64(ggufPadding(int64(s), int64(alignment)))
 	}
 
+	offset, err := f.Seek(0, io.SeekCurrent)
+	if err != nil {
+		return err
+	}
+	offset += ggufPadding(offset, int64(alignment))
+
+	var g errgroup.Group
+	g.SetLimit(runtime.GOMAXPROCS(0))
+	// TODO consider reducing if tensors size * gomaxprocs is larger than free memory
 	for _, t := range ts {
-		if err := ggufWriteTensor(ws, t, int64(alignment)); err != nil {
+		t := t
+		w := io.NewOffsetWriter(f, offset+int64(t.Offset))
+		g.Go(func() error {
+			_, err = t.WriteTo(w)
 			return err
-		}
+		})
 	}
 
-	return nil
+	return g.Wait()
 }
 
 func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
@@ -560,8 +589,10 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
 
 	var err error
 	switch v := v.(type) {
-	case uint32:
+	case uint32, FileType:
 		err = writeGGUF(ws, ggufTypeUint32, v)
+	case uint64:
+		err = writeGGUF(ws, ggufTypeUint64, v)
 	case float32:
 		err = writeGGUF(ws, ggufTypeFloat32, v)
 	case bool:
@@ -570,32 +601,20 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
 		err = writeGGUFString(ws, v)
 	case []int32:
 		err = writeGGUFArray(ws, ggufTypeInt32, v)
+	case *array[int32]:
+		err = writeGGUFArray(ws, ggufTypeInt32, v.values)
 	case []uint32:
 		err = writeGGUFArray(ws, ggufTypeUint32, v)
+	case *array[uint32]:
+		err = writeGGUFArray(ws, ggufTypeUint32, v.values)
 	case []float32:
 		err = writeGGUFArray(ws, ggufTypeFloat32, v)
+	case *array[float32]:
+		err = writeGGUFArray(ws, ggufTypeFloat32, v.values)
 	case []string:
-		if err := binary.Write(ws, binary.LittleEndian, ggufTypeArray); err != nil {
+		err = writeGGUFArray(ws, ggufTypeString, v)
-			return err
+	case *array[string]:
-		}
+		err = writeGGUFArray(ws, ggufTypeString, v.values)
-
-		if err := binary.Write(ws, binary.LittleEndian, ggufTypeString); err != nil {
-			return err
-		}
-
-		if err := binary.Write(ws, binary.LittleEndian, uint64(len(v))); err != nil {
-			return err
-		}
-
-		for _, e := range v {
-			if err := binary.Write(ws, binary.LittleEndian, uint64(len(e))); err != nil {
-				return err
-			}
-
-			if err := binary.Write(ws, binary.LittleEndian, []byte(e)); err != nil {
-				return err
-			}
-		}
 	default:
 		return fmt.Errorf("improper type for '%s'", k)
 	}
@@ -603,7 +622,7 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
 	return err
 }
 
-func ggufWriteTensorInfo(ws io.WriteSeeker, t Tensor) error {
+func ggufWriteTensorInfo(ws io.WriteSeeker, t *Tensor) error {
 	slog.Debug(t.Name, "kind", t.Kind, "shape", t.Shape, "offset", t.Offset)
 	if err := binary.Write(ws, binary.LittleEndian, uint64(len(t.Name))); err != nil {
 		return err
@@ -630,20 +649,6 @@ func ggufWriteTensorInfo(ws io.WriteSeeker, t Tensor) error {
 	return binary.Write(ws, binary.LittleEndian, t.Offset)
 }
 
-func ggufWriteTensor(ws io.WriteSeeker, t Tensor, alignment int64) error {
-	offset, err := ws.Seek(0, io.SeekCurrent)
-	if err != nil {
-		return err
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, bytes.Repeat([]byte{0}, int(ggufPadding(offset, alignment)))); err != nil {
-		return err
-	}
-
-	_, err = t.WriteTo(ws)
-	return err
-}
-
 func ggufPadding(offset, align int64) int64 {
 	return (align - offset%align) % align
 }

fs/ggml/gguf_test.go

@@ -18,7 +18,7 @@ func TestWriteGGUF(t *testing.T) {
 
 	if err := WriteGGUF(w, KV{
 		"general.alignment": uint32(16),
-	}, []Tensor{
+	}, []*Tensor{
 		{Name: "test.0", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
 		{Name: "test.1", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
 		{Name: "test.2", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},

fs/ggml/type.go

@@ -1,185 +1,341 @@
 package ggml
 
-import "fmt"
+import (
-
+	"fmt"
-type fileType uint32
+	"log/slog"
-
+	"strings"
-const (
-	fileTypeF32 fileType = iota
-	fileTypeF16
-	fileTypeQ4_0
-	fileTypeQ4_1
-	fileTypeQ4_1_F16
-	fileTypeQ4_2 // unused
-	fileTypeQ4_3 // unused
-	fileTypeQ8_0
-	fileTypeQ5_0
-	fileTypeQ5_1
-	fileTypeQ2_K
-	fileTypeQ3_K_S
-	fileTypeQ3_K_M
-	fileTypeQ3_K_L
-	fileTypeQ4_K_S
-	fileTypeQ4_K_M
-	fileTypeQ5_K_S
-	fileTypeQ5_K_M
-	fileTypeQ6_K
-	fileTypeIQ2_XXS
-	fileTypeIQ2_XS
-	fileTypeQ2_K_S
-	fileTypeIQ3_XS
-	fileTypeIQ3_XXS
-	fileTypeIQ1_S
-	fileTypeIQ4_NL
-	fileTypeIQ3_S
-	fileTypeIQ3_M
-	fileTypeIQ2_S
-	fileTypeIQ2_M
-	fileTypeIQ4_XS
-	fileTypeIQ1_M
-	fileTypeBF16
-
-	fileTypeUnknown
 )
 
-func ParseFileType(s string) (fileType, error) {
+// FileType is the Go equivalent to llama_ftype used for gguf file typing
+type FileType uint32
+
+const (
+	FileTypeF32 FileType = iota
+	FileTypeF16
+	FileTypeQ4_0
+	FileTypeQ4_1
+	fileTypeQ4_1_F16 // unused by GGML
+	fileTypeQ4_2     // unused by GGML
+	fileTypeQ4_3     // unused by GGML
+	FileTypeQ8_0
+	FileTypeQ5_0
+	FileTypeQ5_1
+	FileTypeQ2_K
+	FileTypeQ3_K_S
+	FileTypeQ3_K_M
+	FileTypeQ3_K_L
+	FileTypeQ4_K_S
+	FileTypeQ4_K_M
+	FileTypeQ5_K_S
+	FileTypeQ5_K_M
+	FileTypeQ6_K
+	fileTypeIQ2_XXS // not supported by ollama
+	fileTypeIQ2_XS  // not supported by ollama
+	FileTypeQ2_K_S
+	fileTypeIQ3_XS  // not supported by ollama
+	fileTypeIQ3_XXS // not supported by ollama
+	fileTypeIQ1_S   // not supported by ollama
+	fileTypeIQ4_NL  // not supported by ollama
+	fileTypeIQ3_S   // not supported by ollama
+	fileTypeIQ3_M   // not supported by ollama
+	fileTypeIQ2_S   // not supported by ollama
+	fileTypeIQ2_M   // not supported by ollama
+	fileTypeIQ4_XS  // not supported by ollama
+	fileTypeIQ1_M   // not supported by ollama
+	FileTypeBF16
+	fileTypeQ4_0_4_4 // unused by GGML
+	fileTypeQ4_0_4_8 // unused by GGML
+	fileTypeQ4_0_8_8 // unused by GGML
+	fileTypeTQ1_0    // not supported by ollama
+	fileTypeTQ2_0    // not supported by ollama
+
+	FileTypeUnknown = 1024
+)
+
+// ParseFileType parses the provided GGUF file type
+// Only Ollama supported types are considered valid
+func ParseFileType(s string) (FileType, error) {
 	switch s {
 	case "F32":
-		return fileTypeF32, nil
+		return FileTypeF32, nil
 	case "F16":
-		return fileTypeF16, nil
+		return FileTypeF16, nil
 	case "Q4_0":
-		return fileTypeQ4_0, nil
+		return FileTypeQ4_0, nil
 	case "Q4_1":
-		return fileTypeQ4_1, nil
+		return FileTypeQ4_1, nil
-	case "Q4_1_F16":
-		return fileTypeQ4_1_F16, nil
 	case "Q8_0":
-		return fileTypeQ8_0, nil
+		return FileTypeQ8_0, nil
 	case "Q5_0":
-		return fileTypeQ5_0, nil
+		return FileTypeQ5_0, nil
 	case "Q5_1":
-		return fileTypeQ5_1, nil
+		return FileTypeQ5_1, nil
 	case "Q2_K":
-		return fileTypeQ2_K, nil
+		return FileTypeQ2_K, nil
 	case "Q3_K_S":
-		return fileTypeQ3_K_S, nil
+		return FileTypeQ3_K_S, nil
 	case "Q3_K_M":
-		return fileTypeQ3_K_M, nil
+		return FileTypeQ3_K_M, nil
 	case "Q3_K_L":
-		return fileTypeQ3_K_L, nil
+		return FileTypeQ3_K_L, nil
 	case "Q4_K_S":
-		return fileTypeQ4_K_S, nil
+		return FileTypeQ4_K_S, nil
-	case "Q4_K_M":
+	case "Q4_K_M", "Q4_K":
-		return fileTypeQ4_K_M, nil
+		return FileTypeQ4_K_M, nil
 	case "Q5_K_S":
-		return fileTypeQ5_K_S, nil
+		return FileTypeQ5_K_S, nil
-	case "Q5_K_M":
+	case "Q5_K_M", "Q5_K":
-		return fileTypeQ5_K_M, nil
+		return FileTypeQ5_K_M, nil
 	case "Q6_K":
-		return fileTypeQ6_K, nil
+		return FileTypeQ6_K, nil
-	case "IQ2_XXS":
-		return fileTypeIQ2_XXS, nil
-	case "IQ2_XS":
-		return fileTypeIQ2_XS, nil
 	case "Q2_K_S":
-		return fileTypeQ2_K_S, nil
+		return FileTypeQ2_K_S, nil
-	case "IQ3_XS":
-		return fileTypeIQ3_XS, nil
-	case "IQ3_XXS":
-		return fileTypeIQ3_XXS, nil
-	case "IQ1_S":
-		return fileTypeIQ1_S, nil
-	case "IQ4_NL":
-		return fileTypeIQ4_NL, nil
-	case "IQ3_S":
-		return fileTypeIQ3_S, nil
-	case "IQ3_M":
-		return fileTypeIQ3_M, nil
-	case "IQ2_S":
-		return fileTypeIQ2_S, nil
-	case "IQ2_M":
-		return fileTypeIQ2_M, nil
-	case "IQ4_XS":
-		return fileTypeIQ4_XS, nil
-	case "IQ1_M":
-		return fileTypeIQ1_M, nil
 	case "BF16":
-		return fileTypeBF16, nil
+		return FileTypeBF16, nil
 	default:
-		return fileTypeUnknown, fmt.Errorf("unknown fileType: %s", s)
+		supportedFileTypes := []FileType{
+			FileTypeF32,
+			FileTypeF16,
+			FileTypeQ4_K_S,
+			FileTypeQ4_K_M,
+			FileTypeQ8_0,
+			// fsggml.FileTypeBF16, // TODO
+		}
+		strs := make([]string, len(supportedFileTypes))
+		for i := range supportedFileTypes {
+			strs[i] = supportedFileTypes[i].String()
+		}
+
+		return FileTypeUnknown, fmt.Errorf("unsupported quantization type %s - supported types are %s", s, strings.Join(strs, ", "))
 	}
 }
 
-func (t fileType) String() string {
+func (t FileType) String() string {
 	switch t {
-	case fileTypeF32:
+	case FileTypeF32:
 		return "F32"
-	case fileTypeF16:
+	case FileTypeF16:
 		return "F16"
-	case fileTypeQ4_0:
+	case FileTypeQ4_0:
 		return "Q4_0"
-	case fileTypeQ4_1:
+	case FileTypeQ4_1:
 		return "Q4_1"
-	case fileTypeQ4_1_F16:
+	case FileTypeQ8_0:
-		return "Q4_1_F16"
-	case fileTypeQ8_0:
 		return "Q8_0"
-	case fileTypeQ5_0:
+	case FileTypeQ5_0:
 		return "Q5_0"
-	case fileTypeQ5_1:
+	case FileTypeQ5_1:
 		return "Q5_1"
-	case fileTypeQ2_K:
+	case FileTypeQ2_K:
 		return "Q2_K"
-	case fileTypeQ3_K_S:
+	case FileTypeQ3_K_S:
 		return "Q3_K_S"
-	case fileTypeQ3_K_M:
+	case FileTypeQ3_K_M:
 		return "Q3_K_M"
-	case fileTypeQ3_K_L:
+	case FileTypeQ3_K_L:
 		return "Q3_K_L"
-	case fileTypeQ4_K_S:
+	case FileTypeQ4_K_S:
 		return "Q4_K_S"
-	case fileTypeQ4_K_M:
+	case FileTypeQ4_K_M:
 		return "Q4_K_M"
-	case fileTypeQ5_K_S:
+	case FileTypeQ5_K_S:
 		return "Q5_K_S"
-	case fileTypeQ5_K_M:
+	case FileTypeQ5_K_M:
 		return "Q5_K_M"
-	case fileTypeQ6_K:
+	case FileTypeQ6_K:
 		return "Q6_K"
-	case fileTypeIQ2_XXS:
+	case FileTypeQ2_K_S:
-		return "IQ2_XXS"
-	case fileTypeIQ2_XS:
-		return "IQ2_XS"
-	case fileTypeQ2_K_S:
 		return "Q2_K_S"
-	case fileTypeIQ3_XS:
+	case FileTypeBF16:
-		return "IQ3_XS"
-	case fileTypeIQ3_XXS:
-		return "IQ3_XXS"
-	case fileTypeIQ1_S:
-		return "IQ1_S"
-	case fileTypeIQ4_NL:
-		return "IQ4_NL"
-	case fileTypeIQ3_S:
-		return "IQ3_S"
-	case fileTypeIQ3_M:
-		return "IQ3_M"
-	case fileTypeIQ2_S:
-		return "IQ2_S"
-	case fileTypeIQ4_XS:
-		return "IQ4_XS"
-	case fileTypeIQ2_M:
-		return "IQ2_M"
-	case fileTypeIQ1_M:
-		return "IQ1_M"
-	case fileTypeBF16:
 		return "BF16"
 	default:
 		return "unknown"
 	}
 }
 
-func (t fileType) Value() uint32 {
+func (t FileType) Value() uint32 {
 	return uint32(t)
 }
+
+func (ftype FileType) ToTensorType() TensorType {
+	switch ftype {
+	case FileTypeF32:
+		return TensorTypeF32
+	case FileTypeF16:
+		return TensorTypeF16
+	case FileTypeQ4_0:
+		return TensorTypeQ4_0
+	case FileTypeQ4_1:
+		return TensorTypeQ4_1
+	case FileTypeQ8_0:
+		return TensorTypeQ8_0
+	case FileTypeQ5_0:
+		return TensorTypeQ5_0
+	case FileTypeQ5_1:
+		return TensorTypeQ5_1
+	case FileTypeQ2_K:
+		return TensorTypeQ2_K
+	case FileTypeQ3_K_S:
+		return TensorTypeQ3_K
+	case FileTypeQ3_K_M:
+		return TensorTypeQ3_K
+	case FileTypeQ3_K_L:
+		return TensorTypeQ3_K
+	case FileTypeQ4_K_S:
+		return TensorTypeQ4_K
+	case FileTypeQ4_K_M:
+		return TensorTypeQ4_K
+	case FileTypeQ5_K_S:
+		return TensorTypeQ5_K
+	case FileTypeQ5_K_M:
+		return TensorTypeQ5_K
+	case FileTypeQ6_K:
+		return TensorTypeQ6_K
+	case FileTypeQ2_K_S:
+		return TensorTypeQ2_K
+	case FileTypeBF16:
+		return TensorTypeBF16
+	default:
+		slog.Warn("unsupported file type", "type", ftype)
+		return 0 // F32
+	}
+}
+
+// TensorType is equivalent to ggml_type for individual tensor types
+// Note: these are not the same as FileType
+type TensorType uint32
+
+const (
+	TensorTypeF32 TensorType = iota
+	TensorTypeF16
+	TensorTypeQ4_0
+	TensorTypeQ4_1
+	tensorTypeQ4_2 // unused by GGML
+	tensorTypeQ4_3 // unused by GGML
+	TensorTypeQ5_0
+	TensorTypeQ5_1
+	TensorTypeQ8_0
+	TensorTypeQ8_1
+	TensorTypeQ2_K
+	TensorTypeQ3_K
+	TensorTypeQ4_K
+	TensorTypeQ5_K
+	TensorTypeQ6_K
+	TensorTypeQ8_K
+	tensorTypeIQ2_XXS // not supported by ollama
+	tensorTypeIQ2_XS  // not supported by ollama
+	tensorTypeIQ3_XXS // not supported by ollama
+	tensorTypeIQ1_S   // not supported by ollama
+	tensorTypeIQ4_NL  // not supported by ollama
+	tensorTypeIQ3_S   // not supported by ollama
+	tensorTypeIQ2_S   // not supported by ollama
+	tensorTypeIQ4_XS  // not supported by ollama
+	TensorTypeI8
+	TensorTypeI16
+	TensorTypeI32
+	TensorTypeI64
+	TensorTypeF64
+	tensorTypeIQ1_M // not supported by ollama
+	TensorTypeBF16
+	tensorTypeQ4_0_4_4   // unused by GGML
+	tensorTypeQ4_0_4_8   // unused by GGML
+	tensorTypeQ4_0_8_8   // unused by GGML
+	tensorTypeTQ1_0      // not supported by ollama
+	tensorTypeTQ2_0      // not supported by ollama
+	tensorTypeIQ4_NL_4_4 // unused by GGML
+	tensorTypeIQ4_NL_4_8 // unused by GGML
+	tensorTypeIQ4_NL_8_8 // unused by GGML
+)
+
+// ParseFileType parses the provided GGUF file type
+// Only Ollama supported types are considered valid
+func ParseTensorType(s string) (TensorType, error) {
+	switch s {
+	case "F32":
+		return TensorTypeF32, nil
+	case "F16":
+		return TensorTypeF16, nil
+	case "Q4_0":
+		return TensorTypeQ4_0, nil
+	case "Q4_1":
+		return TensorTypeQ4_1, nil
+	case "Q5_0":
+		return TensorTypeQ5_0, nil
+	case "Q5_1":
+		return TensorTypeQ5_1, nil
+	case "Q8_0":
+		return TensorTypeQ8_0, nil
+	case "Q8_1":
+		return TensorTypeQ8_1, nil
+	case "Q2_K":
+		return TensorTypeQ2_K, nil
+	case "Q3_K":
+		return TensorTypeQ3_K, nil
+	case "Q4_K":
+		return TensorTypeQ4_K, nil
+	case "Q5_K":
+		return TensorTypeQ5_K, nil
+	case "Q6_K":
+		return TensorTypeQ6_K, nil
+	case "Q8_K":
+		return TensorTypeQ8_K, nil
+	case "F64":
+		return TensorTypeF64, nil
+	case "BF16":
+		return TensorTypeBF16, nil
+	default:
+		return 0, fmt.Errorf("unsupported quantization type %s", s)
+	}
+}
+
+func (t TensorType) IsQuantized() bool {
+	switch t {
+	case TensorTypeF32, TensorTypeF16, TensorTypeBF16:
+		return false
+	default:
+		return true
+	}
+}
+
+func (t TensorType) RowSize(ne uint64) uint64 {
+	return t.TypeSize() * ne / t.BlockSize()
+}
+
+func (t TensorType) String() string {
+	switch t {
+	case TensorTypeF32:
+		return "F32"
+	case TensorTypeF16:
+		return "F16"
+	case TensorTypeQ4_0:
+		return "Q4_0"
+	case TensorTypeQ4_1:
+		return "Q4_1"
+	case TensorTypeQ5_0:
+		return "Q5_0"
+	case TensorTypeQ5_1:
+		return "Q5_1"
+	case TensorTypeQ8_0:
+		return "Q8_0"
+	case TensorTypeQ8_1:
+		return "Q8_1"
+	case TensorTypeQ2_K:
+		return "Q2_K"
+	case TensorTypeQ3_K:
+		return "Q3_K"
+	case TensorTypeQ4_K:
+		return "Q4_K"
+	case TensorTypeQ5_K:
+		return "Q5_K"
+	case TensorTypeQ6_K:
+		return "Q6_K"
+	case TensorTypeQ8_K:
+		return "Q8_K"
+	case TensorTypeF64:
+		return "F64"
+	case TensorTypeBF16:
+		return "BF16"
+	default:
+		return "unknown"
+	}
+}

integration/model_arch_test.go

@@ -48,17 +48,6 @@ var (
 	}
 )
 
-func getTimeouts(t *testing.T) (soft time.Duration, hard time.Duration) {
-	deadline, hasDeadline := t.Deadline()
-	if !hasDeadline {
-		return 8 * time.Minute, 10 * time.Minute
-	} else if deadline.Compare(time.Now().Add(2*time.Minute)) <= 0 {
-		t.Skip("too little time")
-		return time.Duration(0), time.Duration(0)
-	}
-	return -time.Since(deadline.Add(-2 * time.Minute)), -time.Since(deadline.Add(-20 * time.Second))
-}
-
 func TestModelsGenerate(t *testing.T) {
 	softTimeout, hardTimeout := getTimeouts(t)
 	slog.Info("Setting timeouts", "soft", softTimeout, "hard", hardTimeout)

integration/quantization_test.go

@@ -0,0 +1,130 @@
+//go:build integration && models
+
+package integration
+
+import (
+	"bytes"
+	"context"
+	"fmt"
+	"log/slog"
+	"strings"
+	"testing"
+	"time"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestQuantization(t *testing.T) {
+	sourceModels := []string{
+		"qwen2.5:0.5b-instruct-fp16",
+	}
+	quantizations := []string{
+		"Q8_0",
+		"Q4_K_S",
+		"Q4_K_M",
+		"Q4_K",
+	}
+	softTimeout, hardTimeout := getTimeouts(t)
+	started := time.Now()
+	slog.Info("Setting timeouts", "soft", softTimeout, "hard", hardTimeout)
+	ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
+	defer cancel()
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
+
+	for _, base := range sourceModels {
+		if err := PullIfMissing(ctx, client, base); err != nil {
+			t.Fatalf("pull failed %s", err)
+		}
+		for _, quant := range quantizations {
+			newName := fmt.Sprintf("%s__%s", base, quant)
+			t.Run(newName, func(t *testing.T) {
+				if time.Now().Sub(started) > softTimeout {
+					t.Skip("skipping remaining tests to avoid excessive runtime")
+				}
+				req := &api.CreateRequest{
+					Model:        newName,
+					Quantization: quant,
+					From:         base,
+				}
+				fn := func(resp api.ProgressResponse) error {
+					// fmt.Print(".")
+					return nil
+				}
+				t.Logf("quantizing: %s -> %s", base, quant)
+				if err := client.Create(ctx, req, fn); err != nil {
+					t.Fatalf("create failed %s", err)
+				}
+				defer func() {
+					req := &api.DeleteRequest{
+						Model: newName,
+					}
+					t.Logf("deleting: %s -> %s", base, quant)
+					if err := client.Delete(ctx, req); err != nil {
+						t.Logf("failed to clean up %s: %s", req.Model, err)
+					}
+				}()
+				// Check metadata on the model
+				resp, err := client.Show(ctx, &api.ShowRequest{Name: newName})
+				if err != nil {
+					t.Fatalf("unable to show model: %s", err)
+				}
+				if !strings.Contains(resp.Details.QuantizationLevel, quant) {
+					t.Fatalf("unexpected quantization for %s:\ngot: %s", newName, resp.Details.QuantizationLevel)
+				}
+
+				stream := true
+				genReq := api.GenerateRequest{
+					Model:     newName,
+					Prompt:    "why is the sky blue?",
+					KeepAlive: &api.Duration{Duration: 3 * time.Second},
+					Options: map[string]any{
+						"seed":        42,
+						"temperature": 0.0,
+					},
+					Stream: &stream,
+				}
+				t.Logf("verifying: %s -> %s", base, quant)
+
+				// Some smaller quantizations can cause models to have poor quality
+				// or get stuck in repetition loops, so we stop as soon as we have any matches
+				anyResp := []string{"rayleigh", "scattering", "day", "sun", "moon", "color", "nitrogen", "oxygen"}
+				reqCtx, reqCancel := context.WithCancel(ctx)
+				atLeastOne := false
+				var buf bytes.Buffer
+				genfn := func(response api.GenerateResponse) error {
+					buf.Write([]byte(response.Response))
+					fullResp := strings.ToLower(buf.String())
+					for _, resp := range anyResp {
+						if strings.Contains(fullResp, resp) {
+							atLeastOne = true
+							t.Log(fullResp)
+							reqCancel()
+							break
+						}
+					}
+					return nil
+				}
+
+				done := make(chan int)
+				var genErr error
+				go func() {
+					genErr = client.Generate(reqCtx, &genReq, genfn)
+					done <- 0
+				}()
+
+				select {
+				case <-done:
+					if genErr != nil && !atLeastOne {
+						t.Fatalf("failed with %s request prompt %s ", genReq.Model, genReq.Prompt)
+					}
+				case <-ctx.Done():
+					t.Error("outer test context done while waiting for generate")
+				}
+
+				t.Logf("passed")
+
+			})
+		}
+	}
+}

integration/utils_test.go

@@ -359,3 +359,14 @@ func skipUnderMinVRAM(t *testing.T, gb uint64) {
 		}
 	}
 }
+
+func getTimeouts(t *testing.T) (soft time.Duration, hard time.Duration) {
+	deadline, hasDeadline := t.Deadline()
+	if !hasDeadline {
+		return 8 * time.Minute, 10 * time.Minute
+	} else if deadline.Compare(time.Now().Add(2*time.Minute)) <= 0 {
+		t.Skip("too little time")
+		return time.Duration(0), time.Duration(0)
+	}
+	return -time.Since(deadline.Add(-2 * time.Minute)), -time.Since(deadline.Add(-20 * time.Second))
+}

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

@@ -74,7 +74,6 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
     { LLM_ARCH_PLM,              "plm"              },
     { LLM_ARCH_BAILINGMOE,       "bailingmoe"       },
-    { LLM_ARCH_MISTRAL3,         "mistral3"         },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -1607,22 +1606,6 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
         },
     },
-    {
-        LLM_ARCH_MISTRAL3,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,  "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,   "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,      "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,      "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,      "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,    "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,    "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,    "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_UP,      "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,    "blk.%d.ffn_down" },
-        }
-    },
     {
         LLM_ARCH_UNKNOWN,
         {

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

@@ -76,7 +76,6 @@ enum llm_arch {
     LLM_ARCH_CHAMELEON,
     LLM_ARCH_SOLAR,
     LLM_ARCH_WAVTOKENIZER_DEC,
-    LLM_ARCH_MISTRAL3,
     LLM_ARCH_PLM,
     LLM_ARCH_BAILINGMOE,
     LLM_ARCH_UNKNOWN,

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

@@ -1437,7 +1437,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
-        case LLM_ARCH_MISTRAL3: break;
         default: throw std::runtime_error("unsupported model architecture");
     }
 
@@ -13752,7 +13751,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_CHAMELEON:
         case LLM_ARCH_SOLAR:
         case LLM_ARCH_BAILINGMOE:
-        case LLM_ARCH_MISTRAL3:
             return LLAMA_ROPE_TYPE_NORM;
 
         // the pairs of head values are offset by n_rot/2

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

@@ -744,10 +744,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         // This used to be a regex, but <regex> has an extreme cost to compile times.
         bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
 
-        // don't quantize vision stuff
-        quantize &= name.find("v.") == std::string::npos;
-        quantize &= name.find("mm.") == std::string::npos;
-
         // quantize only 2D and 3D tensors (experts)
         quantize &= (ggml_n_dims(tensor) >= 2);
 

llama/llama.go

@@ -460,24 +460,6 @@ func (m *Model) NEmbd() int {
 	return int(C.llama_model_n_embd(m.c))
 }
 
-func Quantize(infile, outfile string, ftype uint32) error {
-	cinfile := C.CString(infile)
-	defer C.free(unsafe.Pointer(cinfile))
-
-	coutfile := C.CString(outfile)
-	defer C.free(unsafe.Pointer(coutfile))
-
-	params := C.llama_model_quantize_default_params()
-	params.nthread = -1
-	params.ftype = ftype
-
-	if rc := C.llama_model_quantize(cinfile, coutfile, &params); rc != 0 {
-		return fmt.Errorf("llama_model_quantize: %d", rc)
-	}
-
-	return nil
-}
-
 // vision processing
 type ClipContext struct {
 	c *C.struct_clip_ctx

llama/patches/0016-add-model-quantizations.patch

@@ -1,96 +0,0 @@
-From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
-From: jmorganca <jmorganca@gmail.com>
-Date: Tue, 8 Apr 2025 20:39:32 -0700
-Subject: [PATCH] add model quantizations
-
-a temporary patch to add model quantization for
-models not supported in llama.cpp
----
- src/llama-arch.cpp  | 17 +++++++++++++++++
- src/llama-arch.h    |  1 +
- src/llama-model.cpp |  2 ++
- src/llama-quant.cpp |  4 ++++
- 4 files changed, 24 insertions(+)
-
-diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
-index eb7b5325..df42d1a5 100644
---- a/src/llama-arch.cpp
-+++ b/src/llama-arch.cpp
-@@ -74,6 +74,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
-     { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
-     { LLM_ARCH_PLM,              "plm"              },
-     { LLM_ARCH_BAILINGMOE,       "bailingmoe"       },
-+    { LLM_ARCH_MISTRAL3,         "mistral3"         },
-     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
- };
- 
-@@ -1606,6 +1607,22 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
-             { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-         },
-     },
-+    {
-+        LLM_ARCH_MISTRAL3,
-+        {
-+            { LLM_TENSOR_TOKEN_EMBD,  "token_embd" },
-+            { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
-+            { LLM_TENSOR_ATTN_NORM,   "blk.%d.attn_norm" },
-+            { LLM_TENSOR_ATTN_Q,      "blk.%d.attn_q" },
-+            { LLM_TENSOR_ATTN_K,      "blk.%d.attn_k" },
-+            { LLM_TENSOR_ATTN_V,      "blk.%d.attn_v" },
-+            { LLM_TENSOR_ATTN_OUT,    "blk.%d.attn_output" },
-+            { LLM_TENSOR_FFN_NORM,    "blk.%d.ffn_norm" },
-+            { LLM_TENSOR_FFN_GATE,    "blk.%d.ffn_gate" },
-+            { LLM_TENSOR_FFN_UP,      "blk.%d.ffn_up" },
-+            { LLM_TENSOR_FFN_DOWN,    "blk.%d.ffn_down" },
-+        }
-+    },
-     {
-         LLM_ARCH_UNKNOWN,
-         {
-diff --git a/src/llama-arch.h b/src/llama-arch.h
-index bc8a4f0b..bda9d071 100644
---- a/src/llama-arch.h
-+++ b/src/llama-arch.h
-@@ -76,6 +76,7 @@ enum llm_arch {
-     LLM_ARCH_CHAMELEON,
-     LLM_ARCH_SOLAR,
-     LLM_ARCH_WAVTOKENIZER_DEC,
-+    LLM_ARCH_MISTRAL3,
-     LLM_ARCH_PLM,
-     LLM_ARCH_BAILINGMOE,
-     LLM_ARCH_UNKNOWN,
-diff --git a/src/llama-model.cpp b/src/llama-model.cpp
-index 9d099f11..ef70486d 100644
---- a/src/llama-model.cpp
-+++ b/src/llama-model.cpp
-@@ -1437,6 +1437,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
-                     default: type = LLM_TYPE_UNKNOWN;
-                 }
-             } break;
-+        case LLM_ARCH_MISTRAL3: break;
-         default: throw std::runtime_error("unsupported model architecture");
-     }
- 
-@@ -13751,6 +13752,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
-         case LLM_ARCH_CHAMELEON:
-         case LLM_ARCH_SOLAR:
-         case LLM_ARCH_BAILINGMOE:
-+        case LLM_ARCH_MISTRAL3:
-             return LLAMA_ROPE_TYPE_NORM;
- 
-         // the pairs of head values are offset by n_rot/2
-diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
-index 223e1f3f..8ae6dde8 100644
---- a/src/llama-quant.cpp
-+++ b/src/llama-quant.cpp
-@@ -744,6 +744,10 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
-         // This used to be a regex, but <regex> has an extreme cost to compile times.
-         bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
- 
-+        // don't quantize vision stuff
-+        quantize &= name.find("v.") == std::string::npos;
-+        quantize &= name.find("mm.") == std::string::npos;
-+
-         // quantize only 2D and 3D tensors (experts)
-         quantize &= (ggml_n_dims(tensor) >= 2);
- 

llm/memory_test.go

@@ -25,7 +25,7 @@ func TestEstimateGPULayers(t *testing.T) {
 	defer f.Close()
 	inputLayerCount := 5
 
-	tensors := []ggml.Tensor{
+	tensors := []*ggml.Tensor{
 		{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
 		{Name: "blk.1.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
 		{Name: "blk.2.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},

ml/backend/ggml/ggml.go

@@ -312,6 +312,7 @@ func New(ctx context.Context, r *os.File, params ml.BackendParams) (ml.Backend,
 	g, ctx := errgroup.WithContext(ctx)
 	g.SetLimit(runtime.GOMAXPROCS(0))
 	for _, t := range meta.Tensors().Items() {
+		t := t
 		g.Go(func() error {
 			tts := make([]*C.struct_ggml_tensor, max(1, len(targets[t.Name])))
 			for i := range tts {

ml/backend/ggml/quantization.go

@@ -0,0 +1,83 @@
+package ggml
+
+// #cgo CPPFLAGS: -I${SRCDIR}/ggml/src
+// #include <stdlib.h>
+// #include <stdint.h>
+// #include "ggml.h"
+// #include "ggml-cpu.h"
+// #include "ggml-backend.h"
+// #include "ggml-quants.h"
+import "C"
+
+import (
+	"unsafe"
+
+	fsggml "github.com/ollama/ollama/fs/ggml"
+)
+
+// convertToF32 converts (dequantizes) the raw data to F32 so we can then quantize it
+func ConvertToF32(data []byte, dtype uint32, nelements uint64) []float32 {
+	f32s := make([]float32, nelements)
+	elems := C.int64_t(nelements)
+	switch dtype {
+	case C.GGML_TYPE_F16:
+		C.ggml_fp16_to_fp32_row((*C.uint16_t)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
+	case C.GGML_TYPE_Q4_0:
+		C.dequantize_row_q4_0((*C.block_q4_0)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
+	case C.GGML_TYPE_Q4_1:
+		C.dequantize_row_q4_1((*C.block_q4_1)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
+	case C.GGML_TYPE_Q5_0:
+		C.dequantize_row_q5_0((*C.block_q5_0)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
+	case C.GGML_TYPE_Q5_1:
+		C.dequantize_row_q5_1((*C.block_q5_1)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
+	case C.GGML_TYPE_Q8_0:
+		C.dequantize_row_q8_0((*C.block_q8_0)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
+	case C.GGML_TYPE_Q2_K:
+		C.dequantize_row_q2_K((*C.block_q2_K)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
+	case C.GGML_TYPE_Q3_K:
+		C.dequantize_row_q3_K((*C.block_q3_K)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
+	case C.GGML_TYPE_Q4_K:
+		C.dequantize_row_q4_K((*C.block_q4_K)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
+	case C.GGML_TYPE_Q5_K:
+		C.dequantize_row_q5_K((*C.block_q5_K)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
+	case C.GGML_TYPE_Q6_K:
+		C.dequantize_row_q6_K((*C.block_q6_K)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
+	case C.GGML_TYPE_BF16:
+		C.ggml_bf16_to_fp32_row((*C.ggml_bf16_t)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
+	default:
+		panic("unsupported quantization format")
+	}
+	return f32s
+}
+
+func Quantize(newType fsggml.TensorType, f32s []float32, shape []uint64) []byte {
+	buf := make([]byte, len(f32s)*4) // upper bound on size
+	nPerRow := C.int64_t(shape[0])
+	nrows := C.int64_t(1)
+	if len(shape) > 1 {
+		nrows = C.int64_t(shape[1])
+	}
+	shape2 := C.int64_t(1)
+	if len(shape) > 2 {
+		shape2 = C.int64_t(shape[2])
+	}
+	nelements_matrix := nPerRow * nrows
+	newSize := C.size_t(0)
+	for i03 := C.int64_t(0); i03 < shape2; i03++ {
+		f32s_03 := i03 * nelements_matrix
+		buf_03 := C.int64_t(C.ggml_row_size(uint32(newType), nPerRow)) * i03 * nrows
+		newSize += C.ggml_quantize_chunk(
+			uint32(newType),
+			(*C.float)(&f32s[f32s_03]),
+			unsafe.Pointer((uintptr)(unsafe.Pointer(&buf[0]))+uintptr(buf_03)),
+			0,
+			nrows,
+			nPerRow,
+			nil)
+	}
+	return buf[:newSize]
+}
+
+func QuantizationVersion() uint32 {
+	return uint32(C.GGML_QNT_VERSION)
+}

parser/parser_test.go

@@ -765,7 +765,7 @@ func getSHA256Digest(t *testing.T, r io.Reader) (string, int64) {
 	return fmt.Sprintf("sha256:%x", h.Sum(nil)), n
 }
 
-func createBinFile(t *testing.T, kv map[string]any, ti []ggml.Tensor) (string, string) {
+func createBinFile(t *testing.T, kv map[string]any, ti []*ggml.Tensor) (string, string) {
 	t.Helper()
 
 	f, err := os.CreateTemp(t.TempDir(), "testbin.*.gguf")

server/create.go

@@ -15,6 +15,7 @@ import (
 	"path/filepath"
 	"slices"
 	"strings"
+	"sync/atomic"
 
 	"github.com/gin-gonic/gin"
 
@@ -23,7 +24,6 @@ import (
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/fs/ggml"
-	"github.com/ollama/ollama/llama"
 	"github.com/ollama/ollama/template"
 	"github.com/ollama/ollama/types/errtypes"
 	"github.com/ollama/ollama/types/model"
@@ -425,9 +425,14 @@ func createModel(r api.CreateRequest, name model.Name, baseLayers []*layerGGML,
 
 func quantizeLayer(layer *layerGGML, quantizeType string, fn func(resp api.ProgressResponse)) (*layerGGML, error) {
 	ft := layer.GGML.KV().FileType()
-	fn(api.ProgressResponse{Status: fmt.Sprintf("quantizing %s model to %s", ft, quantizeType)})
+	var doneBytes atomic.Uint64
-
+	totalBytes := uint64(layer.Size) - layer.GGML.Tensors().Offset
-	want, err := ggml.ParseFileType(quantizeType)
+	fnWrap := func(n uint64) {
+		done := doneBytes.Add(n)
+		progress := float32(done) / float32(totalBytes)
+		fn(api.ProgressResponse{Status: fmt.Sprintf("quantizing %s model to %s", ft, quantizeType), Digest: "0", Total: layer.Size, Completed: int64(progress * float32(layer.Size))})
+	}
+	ftype, err := ggml.ParseFileType(quantizeType)
 	if err != nil {
 		return nil, err
 	}
@@ -436,6 +441,11 @@ func quantizeLayer(layer *layerGGML, quantizeType string, fn func(resp api.Progr
 	if err != nil {
 		return nil, err
 	}
+	fp, err := os.Open(blob)
+	if err != nil {
+		return nil, err
+	}
+	defer fp.Close()
 
 	temp, err := os.CreateTemp(filepath.Dir(blob), quantizeType)
 	if err != nil {
@@ -444,15 +454,15 @@ func quantizeLayer(layer *layerGGML, quantizeType string, fn func(resp api.Progr
 	defer temp.Close()
 	defer os.Remove(temp.Name())
 
-	if err := llama.Quantize(blob, temp.Name(), uint32(want)); err != nil {
+	if err := quantize(fp, temp, layer.GGML, ftype, fnWrap); err != nil {
 		return nil, err
 	}
-
+	temp.Seek(0, io.SeekStart)
+	fn(api.ProgressResponse{Status: "verifying conversion"})
 	newLayer, err := NewLayer(temp, layer.MediaType)
 	if err != nil {
 		return nil, err
 	}
-
 	if _, err := temp.Seek(0, io.SeekStart); err != nil {
 		return nil, err
 	}
@@ -462,7 +472,6 @@ func quantizeLayer(layer *layerGGML, quantizeType string, fn func(resp api.Progr
 		slog.Error(fmt.Sprintf("error decoding ggml: %s\n", err))
 		return nil, err
 	}
-
 	return &layerGGML{newLayer, f}, nil
 }
 

server/model.go

@@ -64,7 +64,7 @@ func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressRe
 			}
 			defer blob.Close()
 
-			f, _, err := ggml.Decode(blob, 1024)
+			f, _, err := ggml.Decode(blob, -1)
 			if err != nil {
 				return nil, err
 			}

server/quantization.go

@@ -0,0 +1,274 @@
+package server
+
+import (
+	"fmt"
+	"io"
+	"log/slog"
+	"maps"
+	"os"
+	"strings"
+	"unsafe"
+
+	fsggml "github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/ml/backend/ggml"
+)
+
+type quantizer struct {
+	*os.File
+	offset     uint64
+	from, to   *fsggml.Tensor
+	progressFn func(n uint64)
+}
+
+func (q quantizer) WriteTo(w io.Writer) (int64, error) {
+	quantize := q.from.Kind != q.to.Kind
+	sr := io.NewSectionReader(q, int64(q.offset), int64(q.from.Size()))
+	if !quantize {
+		n, err := io.Copy(w, sr)
+		q.progressFn(q.from.Size())
+		return n, err
+	}
+	data, err := io.ReadAll(sr)
+	if err != nil {
+		slog.Warn("file read error", "tensor", q.from.Name, "file", q.Name(), "error", err)
+		return 0, fmt.Errorf("unable to read tensor %s from %s: %s", q.from.Name, q.Name(), err)
+	}
+	var f32s []float32
+	newType := fsggml.TensorType(q.to.Kind)
+	if fsggml.TensorType(q.from.Kind) == fsggml.TensorTypeF32 {
+		f32s = unsafe.Slice((*float32)(unsafe.Pointer(&data[0])), q.from.Elements())
+	} else {
+		f32s = ggml.ConvertToF32(data, q.from.Kind, q.from.Elements())
+	}
+	data = ggml.Quantize(newType, f32s, q.from.Shape)
+	n, err := w.Write(data)
+	q.progressFn(q.from.Size())
+	return int64(n), err
+}
+
+type quantizeState struct {
+	nAttnV    int  // Number of attn_*v* weight tensors
+	nFfnDown  int  // Number of ffn_down tensors
+	iAttnV    int  // Running counter of number of attn_v tensors that have been processed
+	iFfnDown  int  // Running counter of number of ffn_down tensors that have been processed
+	hasOutput bool // used to figure out if a model shares tok_embd with the output weight
+}
+
+func useMoreBits(iLayer, nLayers int) bool {
+	return iLayer < (nLayers/8) || iLayer >= 7*nLayers/8 || (iLayer-nLayers/8)%3 == 2
+}
+
+func getTensorNewType(kv fsggml.KV, qs *quantizeState, newType fsggml.TensorType, name string, shape []uint64, ftype fsggml.FileType) fsggml.TensorType {
+	// Ported from llama_tensor_get_type, removed unsupported quantization types
+	nExperts := max(1, kv.Uint("expert_count", 0))
+	if name == "output.weight" || name == "output_norm.weight" || (!qs.hasOutput && name == "token_embd.weight") {
+		nx := shape[0]
+		qk_k := newType.BlockSize()
+		if nx%qk_k != 0 {
+			newType = fsggml.TensorTypeQ8_0
+		} else if newType != fsggml.TensorTypeQ8_0 {
+			newType = fsggml.TensorTypeQ6_K
+		}
+	} else if strings.Contains(name, "attn_v.weight") {
+		if ftype == fsggml.FileTypeQ2_K {
+			if kv.GQA() >= 4 {
+				newType = fsggml.TensorTypeQ4_K
+			} else {
+				newType = fsggml.TensorTypeQ3_K
+			}
+		} else if ftype == fsggml.FileTypeQ2_K_S && kv.GQA() >= 4 {
+			newType = fsggml.TensorTypeQ4_K
+		} else if ftype == fsggml.FileTypeQ3_K_M {
+			if qs.iAttnV < 2 {
+				newType = fsggml.TensorTypeQ5_K
+			} else {
+				newType = fsggml.TensorTypeQ4_K
+			}
+		} else if ftype == fsggml.FileTypeQ3_K_L {
+			newType = fsggml.TensorTypeQ5_K
+		} else if (ftype == fsggml.FileTypeQ4_K_M || ftype == fsggml.FileTypeQ5_K_M) &&
+			useMoreBits(qs.iAttnV, qs.nAttnV) {
+			newType = fsggml.TensorTypeQ6_K
+		} else if ftype == fsggml.FileTypeQ4_K_S && qs.iAttnV < 4 {
+			newType = fsggml.TensorTypeQ5_K
+		}
+
+		// TODO
+		// if (qs.model.type == LLM_TYPE_70B) {
+		// 	// In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
+		// 	// 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
+		// 	// nearly negligible increase in model size by quantizing this tensor with more bits:
+		// 	if (newType == GGML_TYPE_Q3_K || newType == GGML_TYPE_Q4_K) newType = GGML_TYPE_Q5_K;
+		// }
+
+		if nExperts == 8 {
+			// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
+			newType = fsggml.TensorTypeQ8_0
+		}
+		qs.iAttnV++
+	} else if strings.Contains(name, "attn_k.weight") {
+		if nExperts == 8 {
+			// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
+			newType = fsggml.TensorTypeQ8_0
+		}
+	} else if strings.Contains(name, "ffn_down") {
+		iLayer := qs.iFfnDown
+		n_layer := qs.nFfnDown
+		if ftype == fsggml.FileTypeQ2_K {
+			newType = fsggml.TensorTypeQ3_K
+		} else if ftype == fsggml.FileTypeQ2_K_S {
+			if iLayer < n_layer/8 {
+				newType = fsggml.TensorTypeQ4_K
+			}
+		} else if ftype == fsggml.FileTypeQ3_K_M {
+			if iLayer < n_layer/16 {
+				newType = fsggml.TensorTypeQ5_K
+			} else if useMoreBits(iLayer, n_layer) {
+				newType = fsggml.TensorTypeQ4_K
+			} else {
+				newType = fsggml.TensorTypeQ3_K
+			}
+		} else if ftype == fsggml.FileTypeQ3_K_L {
+			newType = fsggml.TensorTypeQ5_K
+		} else if ftype == fsggml.FileTypeQ4_K_M {
+			if useMoreBits(iLayer, n_layer) {
+				newType = fsggml.TensorTypeQ6_K
+			}
+		} else if ftype == fsggml.FileTypeQ5_K_M && useMoreBits(iLayer, n_layer) {
+			newType = fsggml.TensorTypeQ6_K
+		} else if ftype == fsggml.FileTypeQ4_K_S && iLayer < n_layer/8 {
+			newType = fsggml.TensorTypeQ5_K
+		}
+		qs.iFfnDown++
+	} else if strings.Contains(name, "attn_output.weight") {
+		if nExperts == 8 {
+			if ftype == fsggml.FileTypeQ2_K || ftype == fsggml.FileTypeQ3_K_S || ftype == fsggml.FileTypeQ3_K_M ||
+				ftype == fsggml.FileTypeQ4_K_S || ftype == fsggml.FileTypeQ4_K_M {
+				newType = fsggml.TensorTypeQ5_K
+			}
+		} else {
+			if ftype == fsggml.FileTypeQ2_K {
+				newType = fsggml.TensorTypeQ3_K
+			} else if ftype == fsggml.FileTypeQ3_K_M {
+				newType = fsggml.TensorTypeQ4_K
+			} else if ftype == fsggml.FileTypeQ3_K_L {
+				newType = fsggml.TensorTypeQ5_K
+			}
+		}
+	} else if strings.Contains(name, "attn_qkv.weight") {
+		if ftype == fsggml.FileTypeQ3_K_M || ftype == fsggml.FileTypeQ3_K_L {
+			newType = fsggml.TensorTypeQ4_K
+		} else if ftype == fsggml.FileTypeQ4_K_M {
+			newType = fsggml.TensorTypeQ5_K
+		} else if ftype == fsggml.FileTypeQ5_K_M {
+			newType = fsggml.TensorTypeQ6_K
+		}
+	}
+
+	if newType.IsQuantized() {
+		nx := shape[0]
+		ny := uint64(1)
+		if len(shape) > 1 {
+			ny = shape[1]
+		}
+		qk_k := newType.BlockSize()
+		if nx%qk_k != 0 {
+			slog.Warn(fmt.Sprintf("tensor cols %d x %d are not divisible by %d, required for %s.  Falling back to quantization %s", nx, ny, qk_k, newType.String(), fsggml.TensorTypeF16.String()))
+			newType = fsggml.TensorTypeF16
+		}
+	}
+	return newType
+}
+
+func quantize(in, out *os.File, orig *fsggml.GGML, newFileType fsggml.FileType, progressFn func(n uint64)) error {
+	kv := maps.Clone(orig.KV())
+	kv["general.file_type"] = newFileType
+	// kv["general.quantization_version"] = ggml.QuantizationVersion()
+	qs := &quantizeState{}
+	// Build up the quantize state so newType can adjust types
+	layerCount := 0
+	for k, l := range orig.Tensors().GroupLayers() {
+		if strings.HasPrefix(k, "blk.") {
+			layerCount++
+		}
+		for _, tensor := range l {
+			if strings.Contains(tensor.Name, "attn_v.weight") ||
+				strings.Contains(tensor.Name, "attn_qkv.weight") ||
+				strings.Contains(tensor.Name, "attn_kv_b.weight") {
+				qs.nAttnV++
+			} else if tensor.Name == "output.weight" {
+				qs.hasOutput = true
+			}
+		}
+	}
+	qs.nFfnDown = layerCount
+
+	origTensors := orig.Tensors().Items()
+	outputTensors := make([]*fsggml.Tensor, len(origTensors))
+	for i, tensor := range origTensors {
+		tensor := tensor
+		newType := newType(tensor, kv, qs, newFileType)
+		newTensor := &fsggml.Tensor{
+			Name:  tensor.Name,
+			Shape: tensor.Shape,
+			Kind:  uint32(newType),
+		}
+		outputTensors[i] = newTensor
+		outputTensors[i].WriterTo = quantizer{
+			File:       in,
+			offset:     orig.Tensors().Offset + tensor.Offset,
+			from:       tensor,
+			to:         newTensor,
+			progressFn: progressFn,
+		}
+	}
+	return fsggml.WriteGGUF(out, kv, outputTensors)
+}
+
+func newType(t *fsggml.Tensor, kv fsggml.KV, qs *quantizeState, ftype fsggml.FileType) fsggml.TensorType {
+	defaultType := ftype.ToTensorType()
+	name := t.Name
+	quantize := strings.HasSuffix(name, "weight")
+
+	// don't quantize vision stuff
+	quantize = quantize && (!strings.Contains(name, "v.") || strings.Contains(name, "_v."))
+	quantize = quantize && !strings.Contains(name, "mm.")
+
+	// quantize only 2D and 3D tensors (experts)
+	quantize = quantize && (len(t.Shape) >= 2)
+
+	// do not quantize norm tensors
+	quantize = quantize && !strings.Contains(name, "_norm.weight")
+
+	// do not quantize expert gating tensors
+	quantize = quantize && !strings.Contains(name, "ffn_gate_inp.weight")
+
+	// do not quantize positional embeddings and token types (BERT)
+	quantize = quantize && (name != "position_embd.weight")
+	quantize = quantize && (name != "token_types.weight")
+
+	// do not quantize Mamba's small yet 2D weights
+	// NOTE: can't use LLM_TN here because the layer number is not known
+	quantize = quantize && !strings.Contains(name, "ssm_conv1d.weight")
+
+	// do not quantize RWKV's time_mix_first tensors
+	quantize = quantize && !strings.Contains(name, "time_mix_first.weight")
+	quantize = quantize && !strings.Contains(name, "time_mix_w1.weight")
+	quantize = quantize && !strings.Contains(name, "time_mix_w2.weight")
+	quantize = quantize && !strings.Contains(name, "time_mix_decay_w1.weight")
+	quantize = quantize && !strings.Contains(name, "time_mix_decay_w2.weight")
+	quantize = quantize && !strings.Contains(name, "time_mix_lerp_fused.weight")
+
+	// do not quantize relative position bias (T5)
+	quantize = quantize && !strings.Contains(name, "attn_rel_b.weight")
+
+	newType := fsggml.TensorType(t.Kind)
+	if quantize {
+		// get more optimal quantization type based on the tensor shape, layer, etc.
+		newType = getTensorNewType(kv, qs, defaultType, t.Name, t.Shape, ftype)
+		if newType != defaultType {
+			slog.Debug("tensor quantization adjusted for better quality", "name", t.Name, "requested", defaultType, "quantization", newType)
+		}
+	}
+	return newType
+}

server/quantization_test.go

@@ -0,0 +1,882 @@
+package server
+
+import (
+	"bytes"
+	"fmt"
+	"math"
+	"os"
+	"strings"
+	"testing"
+
+	fsggml "github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/ml/backend/ggml"
+)
+
+func TestGetTensorNewType(t *testing.T) {
+	cases := []struct {
+		name          string
+		kv            map[string]any
+		qs            quantizeState
+		newType       fsggml.TensorType
+		tensor_name   string
+		shape         []uint64
+		ftype         fsggml.FileType
+		expected      fsggml.TensorType
+		expectedPanic string
+	}{
+		{
+			name:        "output_unsupported",
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "output.weight",
+			shape:       []uint64{100, 100},
+			ftype:       fsggml.FileTypeF32,
+			expected:    fsggml.TensorTypeF16,
+		},
+		{
+			name:        "output_Q8",
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "output.weight",
+			shape:       []uint64{1024, 1024},
+			ftype:       fsggml.FileTypeF32,
+			expected:    fsggml.TensorTypeQ6_K,
+		},
+		{
+			name: "attn_v.weight_q4_k",
+			kv: map[string]any{
+				"general.architecture":        "foo",
+				"foo.attention.head_count":    uint32(4),
+				"foo.attention.head_count_kv": uint32(1),
+			},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_v.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ2_K,
+			expected:    fsggml.TensorTypeQ4_K,
+		},
+		{
+			name:        "attn_v.weight_q3_k",
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_v.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ2_K,
+			expected:    fsggml.TensorTypeQ3_K,
+		},
+		{
+			name: "attn_v.weight_q2_k_s_q4_k",
+			kv: map[string]any{
+				"general.architecture":        "foo",
+				"foo.attention.head_count":    uint32(4),
+				"foo.attention.head_count_kv": uint32(1),
+			},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_v.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ2_K_S,
+			expected:    fsggml.TensorTypeQ4_K,
+		},
+		{
+			name:        "attn_v.weight_q3_k_m",
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_v.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ3_K_M,
+			expected:    fsggml.TensorTypeQ5_K,
+		},
+		{
+			name: "attn_v.weight_q3_k_m_i",
+			qs: quantizeState{
+				iAttnV: 2,
+			},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_v.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ3_K_M,
+			expected:    fsggml.TensorTypeQ4_K,
+		},
+		{
+			name:        "attn_v.weight_q3_k_l",
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_v.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ3_K_L,
+			expected:    fsggml.TensorTypeQ5_K,
+		},
+		{
+			name: "attn_v.weight_q4_k_m",
+			qs: quantizeState{
+				iAttnV: 2,
+				nAttnV: 3 * 8,
+			},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_v.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ4_K_M,
+			expected:    fsggml.TensorTypeQ6_K,
+		},
+		{
+			name:        "attn_v.weight_q4_k_s",
+			qs:          quantizeState{},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_v.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ4_K_S,
+			expected:    fsggml.TensorTypeQ5_K,
+		},
+		{
+			name: "attn_v.weight_8_expert",
+			qs:   quantizeState{},
+			kv: map[string]any{
+				"general.architecture": "foo",
+				"foo.expert_count":     uint32(8),
+			},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_v.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeF32,
+			expected:    fsggml.TensorTypeQ8_0,
+		},
+		{
+			name: "attn_k.weight_8_expert",
+			qs:   quantizeState{},
+			kv: map[string]any{
+				"general.architecture": "foo",
+				"foo.expert_count":     uint32(8),
+			},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_k.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeF32,
+			expected:    fsggml.TensorTypeQ8_0,
+		},
+		{
+			name:        "ffn_down_q2_k",
+			qs:          quantizeState{},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "ffn_down",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ2_K,
+			expected:    fsggml.TensorTypeQ3_K,
+		},
+		{
+			name:        "ffn_down_q2_k_s",
+			qs:          quantizeState{},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "ffn_down",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ2_K_S,
+			expected:    fsggml.TensorTypeQ4_0,
+		},
+		{
+			name: "ffn_down_q2_k_s_layers",
+			qs: quantizeState{
+				iFfnDown: 2,
+				nFfnDown: 3 * 8,
+			},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "ffn_down",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ2_K_S,
+			expected:    fsggml.TensorTypeQ4_K,
+		},
+		{
+			name: "ffn_down_q3_k_m_base",
+			qs: quantizeState{
+				iFfnDown: 1,
+				nFfnDown: 8,
+			},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "ffn_down",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ3_K_M,
+			expected:    fsggml.TensorTypeQ3_K,
+		},
+		{
+			name: "ffn_down_q3_k_m_16",
+			qs: quantizeState{
+				iFfnDown: 2,
+				nFfnDown: 3 * 16,
+			},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "ffn_down",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ3_K_M,
+			expected:    fsggml.TensorTypeQ5_K,
+		},
+		{
+			name: "ffn_down_q3_k_m_8",
+			qs: quantizeState{
+				iFfnDown: 2,
+				nFfnDown: 3 * 8,
+			},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "ffn_down",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ3_K_M,
+			expected:    fsggml.TensorTypeQ4_K,
+		},
+		{
+			name:        "ffn_down_q3_k_l",
+			qs:          quantizeState{},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "ffn_down",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ3_K_L,
+			expected:    fsggml.TensorTypeQ5_K,
+		},
+		{
+			name: "ffn_down_q4_k_m",
+			qs: quantizeState{
+				iFfnDown: 1,
+				nFfnDown: 8,
+			},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "ffn_down",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ4_K_M,
+			expected:    fsggml.TensorTypeQ4_0,
+		},
+		{
+			name: "ffn_down_q4_k_m_6",
+			qs: quantizeState{
+				iFfnDown: 2,
+				nFfnDown: 3 * 8,
+			},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "ffn_down",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ4_K_M,
+			expected:    fsggml.TensorTypeQ6_K,
+		},
+		{
+			name: "ffn_down_q5_k_m",
+			qs: quantizeState{
+				iFfnDown: 2,
+				nFfnDown: 3 * 8,
+			},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "ffn_down",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ5_K_M,
+			expected:    fsggml.TensorTypeQ6_K,
+		},
+		{
+			name: "ffn_down_q4_k_s",
+			qs: quantizeState{
+				iFfnDown: 2,
+				nFfnDown: 3 * 8,
+			},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "ffn_down",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ4_K_S,
+			expected:    fsggml.TensorTypeQ5_K,
+		},
+		{
+			name: "attn_output.weight_8_expert",
+			qs:   quantizeState{},
+			kv: map[string]any{
+				"general.architecture": "foo",
+				"foo.expert_count":     uint32(8),
+			},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_output.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ2_K,
+			expected:    fsggml.TensorTypeQ5_K,
+		},
+		{
+			name:        "attn_output.weight_q2",
+			qs:          quantizeState{},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_output.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ2_K,
+			expected:    fsggml.TensorTypeQ3_K,
+		},
+		{
+			name:        "attn_output.weight_q3_k_m",
+			qs:          quantizeState{},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_output.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ3_K_M,
+			expected:    fsggml.TensorTypeQ4_K,
+		},
+		{
+			name:        "attn_output.weight_q3_k_l",
+			qs:          quantizeState{},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_output.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ3_K_L,
+			expected:    fsggml.TensorTypeQ5_K,
+		},
+		{
+			name:        "attn_qkv.weight_q3_k_m",
+			qs:          quantizeState{},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_qkv.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ3_K_M,
+			expected:    fsggml.TensorTypeQ4_K,
+		},
+		{
+			name:        "attn_qkv.weight_q4_k_m",
+			qs:          quantizeState{},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_qkv.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ4_K_M,
+			expected:    fsggml.TensorTypeQ5_K,
+		},
+		{
+			name:        "attn_qkv.weight_q5_k_m",
+			qs:          quantizeState{},
+			kv:          map[string]any{},
+			newType:     fsggml.TensorTypeQ4_0,
+			tensor_name: "blk.0.attn_qkv.weight",
+			shape:       []uint64{256},
+			ftype:       fsggml.FileTypeQ5_K_M,
+			expected:    fsggml.TensorTypeQ6_K,
+		},
+	}
+	for _, tt := range cases {
+		t.Run(tt.name, func(t *testing.T) {
+			if tt.expectedPanic != "" {
+				defer func() {
+					e := recover()
+					if !strings.Contains(fmt.Sprintf("%v", e), tt.expectedPanic) {
+						t.Fatalf("incorrect panic\ngot: %v\nexpected: %s", e, tt.expectedPanic)
+					}
+				}()
+			} else {
+				defer func() {
+					e := recover()
+					if e != nil {
+						t.Fatalf("hit unexpected panic %v", e)
+					}
+				}()
+			}
+			ret := getTensorNewType(tt.kv, &tt.qs, tt.newType, tt.tensor_name, tt.shape, tt.ftype)
+			if ret != tt.expected {
+				t.Fatalf("incorrect type returned\ngot: %d\nexpected: %d", ret, tt.expected)
+			}
+		})
+	}
+}
+
+func TestQuantizeModel(t *testing.T) {
+	cases := []struct {
+		name                string
+		kv                  map[string]any
+		tensors             []*fsggml.Tensor
+		newType             string
+		expectedTensorTypes map[string]fsggml.TensorType
+	}{
+		{
+			name: "f16_q4_k",
+			kv: map[string]any{
+				"general.architecture": "foo",
+			},
+			tensors: []*fsggml.Tensor{
+				{
+					Name: "blk.0.attn.weight", Kind: uint32(fsggml.TensorTypeF16),
+					Offset: uint64(0), Shape: []uint64{512, 2},
+					WriterTo: bytes.NewReader(
+						append(append(append(quantBytes[fsggml.TensorTypeF16], quantBytes[fsggml.TensorTypeF16]...), quantBytes[fsggml.TensorTypeF16]...), quantBytes[fsggml.TensorTypeF16]...),
+					),
+				},
+				{
+					Name: "output.weight", Kind: uint32(fsggml.TensorTypeF16),
+					Offset: uint64(0), Shape: []uint64{256, 4},
+					WriterTo: bytes.NewReader(
+						append(append(append(quantBytes[fsggml.TensorTypeF16], quantBytes[fsggml.TensorTypeF16]...), quantBytes[fsggml.TensorTypeF16]...), quantBytes[fsggml.TensorTypeF16]...),
+					),
+				},
+			},
+			newType: "Q4_K",
+			expectedTensorTypes: map[string]fsggml.TensorType{
+				"blk.0.attn.weight": fsggml.TensorTypeQ4_K,
+				"output.weight":     fsggml.TensorTypeQ6_K,
+			},
+		},
+		{
+			name: "f32_q4_k",
+			kv: map[string]any{
+				"general.architecture": "foo",
+			},
+			tensors: []*fsggml.Tensor{
+				{
+					Name: "blk.0.attn_v.weight", Kind: uint32(fsggml.TensorTypeF32),
+					Offset: uint64(0), Shape: []uint64{512, 2},
+					WriterTo: bytes.NewReader(
+						append(append(append(quantBytes[fsggml.TensorTypeF32], quantBytes[fsggml.TensorTypeF32]...), quantBytes[fsggml.TensorTypeF32]...), quantBytes[fsggml.TensorTypeF32]...),
+					),
+				},
+				{
+					Name: "output.weight", Kind: uint32(fsggml.TensorTypeF32),
+					Offset: uint64(0), Shape: []uint64{512},
+					WriterTo: bytes.NewReader(append(quantBytes[fsggml.TensorTypeF32], quantBytes[fsggml.TensorTypeF32]...)),
+				},
+			},
+			newType: "Q4_K",
+			expectedTensorTypes: map[string]fsggml.TensorType{
+				"blk.0.attn_v.weight": fsggml.TensorTypeQ6_K,
+				"output.weight":       fsggml.TensorTypeF32,
+			},
+		},
+		{
+			name: "f16_q8_0",
+			kv: map[string]any{
+				"general.architecture": "foo",
+			},
+			tensors: []*fsggml.Tensor{
+				{
+					Name: "blk.0.attn.weight", Kind: uint32(fsggml.TensorTypeF16),
+					Offset: uint64(0), Shape: []uint64{32, 16, 2},
+					WriterTo: bytes.NewReader(
+						append(append(append(quantBytes[fsggml.TensorTypeF16], quantBytes[fsggml.TensorTypeF16]...), quantBytes[fsggml.TensorTypeF16]...), quantBytes[fsggml.TensorTypeF16]...),
+					),
+				},
+				{
+					Name: "output.weight", Kind: uint32(fsggml.TensorTypeF16),
+					Offset: uint64(0), Shape: []uint64{256, 4},
+					WriterTo: bytes.NewReader(
+						append(append(append(quantBytes[fsggml.TensorTypeF16], quantBytes[fsggml.TensorTypeF16]...), quantBytes[fsggml.TensorTypeF16]...), quantBytes[fsggml.TensorTypeF16]...),
+					),
+				},
+			},
+			newType: "Q8_0",
+			expectedTensorTypes: map[string]fsggml.TensorType{
+				"blk.0.attn.weight": fsggml.TensorTypeQ8_0,
+				"output.weight":     fsggml.TensorTypeQ8_0,
+			},
+		},
+	}
+
+	for _, tt := range cases {
+		t.Run(tt.name, func(t *testing.T) {
+			f, err := os.CreateTemp(t.TempDir(), tt.name)
+			if err != nil {
+				t.Fatal(err.Error())
+			}
+			defer f.Close()
+			err = fsggml.WriteGGUF(f, tt.kv, tt.tensors)
+			if err != nil {
+				t.Fatalf("failed to create initial model: %s", err)
+			}
+			fp, err := os.Open(f.Name())
+			if err != nil {
+				t.Fatal(err.Error())
+			}
+			defer fp.Close()
+			meta, _, err := fsggml.Decode(fp, -1)
+			if err != nil {
+				t.Fatal(err.Error())
+			}
+			progressCalled := false
+			progress := func(n uint64) {
+				// fmt.Fprintf(os.Stderr, "progress: %f\n", p)
+				progressCalled = true
+			}
+			tmp, err := os.CreateTemp(t.TempDir(), tt.name+".out")
+			if err != nil {
+				t.Fatal(err.Error())
+			}
+			defer tmp.Close()
+			ftype, err := fsggml.ParseFileType(tt.newType)
+			if err != nil {
+				t.Fatal(err.Error())
+			}
+
+			err = quantize(fp, tmp, meta, ftype, progress)
+			if err != nil {
+				t.Fatalf("error during quantize: %s", err)
+			}
+			if !progressCalled {
+				t.Fatalf("progress was not reported")
+			}
+			// Now attempt to load it back and make sure types match expected
+			fpNew, err := os.Open(tmp.Name())
+			if err != nil {
+				t.Fatalf("failed to load the quantized model %s: %s", tmp.Name(), err)
+			}
+			defer fpNew.Close()
+			newMeta, _, err := fsggml.Decode(fpNew, -1)
+			if err != nil {
+				t.Fatalf("failed to load the quantized model %s: %s", tmp.Name(), err)
+			}
+			tensors := newMeta.Tensors()
+			for _, l := range tensors.GroupLayers() {
+				for _, tensor := range l {
+					if fsggml.TensorType(tensor.Kind) != tt.expectedTensorTypes[tensor.Name] {
+						t.Fatalf("incorrect output type for %s\ngot:%s\nexpected:%s", tensor.Name, fsggml.TensorType(tensor.Kind), tt.expectedTensorTypes[tensor.Name])
+					}
+				}
+			}
+		})
+	}
+}
+
+func TestConvertToF32(t *testing.T) {
+	expected := make([]float32, 256)
+	for i := range expected {
+		expected[i] = float32(i)
+	}
+	for dtype, data := range quantBytes {
+		// Skip the no-op
+		if dtype == fsggml.TensorTypeF32 {
+			continue
+		}
+		t.Run(dtype.String(), func(t *testing.T) {
+			fp32 := ggml.ConvertToF32(data, uint32(dtype), 256)
+			similarity := cosineSimilarity(expected, fp32)
+			if similarity < 0.999 {
+				t.Fatalf("Results not similar enough: %s %f", dtype.String(), similarity)
+			}
+		})
+	}
+}
+
+func dotProduct[V float32 | float64](v1, v2 []V) V {
+	var result V = 0
+	for i := range v1 {
+		result += v1[i] * v2[i]
+	}
+	return result
+}
+
+func magnitude[V float32 | float64](v []V) V {
+	var result V = 0
+	for _, val := range v {
+		result += val * val
+	}
+	return V(math.Sqrt(float64(result)))
+}
+
+func cosineSimilarity[V float32 | float64](v1, v2 []V) V {
+	return dotProduct(v1, v2) / (magnitude(v1) * magnitude(v2))
+}
+
+// Precomputed quantized data - arange 256
+// # For gguf-py supported types
+// import gguf
+// import numpy as np
+// print(repr(gguf.quantize(np.arange(256, dtype=np.float16), gguf.GGMLQuantizationType.Q4_0)))
+//
+// For types not supported by gguf-py converted via ggml_fp32_to_fp16_row and quantize_XXX
+//
+//	data := make([]byte, 256*2)
+//	fp32 := make([]float32, 256)
+//	for i := range 256 {
+//		fp32[i] = float32(i)
+//	}
+//	l := C.quantize_q6_K((*C.float)(&fp32[0]), unsafe.Pointer(&data[0]), 1, 256, nil)
+//	for i := range data[:int(l)] {
+//		fmt.Printf("%d, ", data[i])
+//	}
+var (
+	quantBytes = map[fsggml.TensorType][]byte{
+		fsggml.TensorTypeQ4_0: {
+			192, 195, 72, 72, 55, 55, 55, 55, 38, 38, 38, 38, 21,
+			21, 21, 21, 4, 4, 224, 199, 36, 36, 36, 36, 19, 19,
+			19, 19, 19, 19, 19, 19, 2, 2, 2, 2, 240, 201, 19,
+			19, 18, 18, 18, 18, 18, 18, 18, 18, 2, 2, 2, 2,
+			1, 1, 240, 203, 18, 18, 18, 18, 18, 18, 18, 18, 1,
+			1, 1, 1, 1, 1, 1, 1, 248, 204, 18, 18, 17, 17,
+			17, 17, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 248,
+			205, 17, 17, 17, 17, 1, 1, 1, 1, 1, 1, 1, 1,
+			1, 1, 1, 1, 248, 206, 17, 17, 1, 1, 1, 1, 1,
+			1, 1, 1, 1, 1, 1, 1, 1, 1, 248, 207, 1, 1,
+			1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+			1,
+		},
+		fsggml.TensorTypeQ4_1: {
+			34, 64, 0, 0, 128, 128, 145, 145, 162, 162, 179, 179, 196,
+			196, 213, 213, 230, 230, 247, 247, 34, 64, 0, 80, 128, 128,
+			145, 145, 162, 162, 179, 179, 196, 196, 213, 213, 230, 230, 247,
+			247, 34, 64, 0, 84, 128, 128, 145, 145, 162, 162, 179, 179,
+			196, 196, 213, 213, 230, 230, 247, 247, 34, 64, 0, 86, 128,
+			128, 145, 145, 162, 162, 179, 179, 196, 196, 213, 213, 230, 230,
+			247, 247, 34, 64, 0, 88, 128, 128, 145, 145, 162, 162, 179,
+			179, 196, 196, 213, 213, 230, 230, 247, 247, 34, 64, 0, 89,
+			128, 128, 145, 145, 162, 162, 179, 179, 196, 196, 213, 213, 230,
+			230, 247, 247, 34, 64, 0, 90, 128, 128, 145, 145, 162, 162,
+			179, 179, 196, 196, 213, 213, 230, 230, 247, 247, 34, 64, 0,
+			91, 128, 128, 145, 145, 162, 162, 179, 179, 196, 196, 213, 213,
+			230, 230, 247, 247,
+		},
+		fsggml.TensorTypeQ5_0: {
+			192, 191, 1, 0, 0, 0, 128, 127, 127, 110, 110, 93, 93,
+			76, 76, 59, 59, 42, 42, 25, 25, 8, 224, 195, 0, 0,
+			0, 0, 72, 72, 55, 55, 55, 55, 38, 38, 38, 38, 21,
+			21, 21, 21, 4, 4, 240, 197, 0, 0, 0, 0, 53, 37,
+			37, 37, 37, 36, 36, 20, 20, 20, 20, 19, 19, 3, 3,
+			3, 240, 199, 0, 0, 0, 0, 36, 36, 36, 36, 19, 19,
+			19, 19, 19, 19, 19, 19, 2, 2, 2, 2, 248, 200, 0,
+			0, 0, 0, 35, 19, 19, 19, 19, 19, 19, 18, 18, 18,
+			18, 2, 2, 2, 2, 2, 248, 201, 0, 0, 0, 0, 19,
+			19, 18, 18, 18, 18, 18, 18, 18, 18, 2, 2, 2, 2,
+			1, 1, 248, 202, 0, 0, 0, 0, 18, 18, 18, 18, 18,
+			18, 18, 18, 18, 2, 2, 1, 1, 1, 1, 1, 248, 203,
+			0, 0, 0, 0, 18, 18, 18, 18, 18, 18, 18, 18, 1,
+			1, 1, 1, 1, 1, 1, 1,
+		},
+		fsggml.TensorTypeQ5_1: {
+			0, 60, 0, 0, 0, 0, 255, 255, 0, 17, 34, 51, 68,
+			85, 102, 119, 136, 153, 170, 187, 204, 221, 238, 255, 0, 60,
+			0, 80, 0, 0, 255, 255, 0, 17, 34, 51, 68, 85, 102,
+			119, 136, 153, 170, 187, 204, 221, 238, 255, 0, 60, 0, 84,
+			0, 0, 255, 255, 0, 17, 34, 51, 68, 85, 102, 119, 136,
+			153, 170, 187, 204, 221, 238, 255, 0, 60, 0, 86, 0, 0,
+			255, 255, 0, 17, 34, 51, 68, 85, 102, 119, 136, 153, 170,
+			187, 204, 221, 238, 255, 0, 60, 0, 88, 0, 0, 255, 255,
+			0, 17, 34, 51, 68, 85, 102, 119, 136, 153, 170, 187, 204,
+			221, 238, 255, 0, 60, 0, 89, 0, 0, 255, 255, 0, 17,
+			34, 51, 68, 85, 102, 119, 136, 153, 170, 187, 204, 221, 238,
+			255, 0, 60, 0, 90, 0, 0, 255, 255, 0, 17, 34, 51,
+			68, 85, 102, 119, 136, 153, 170, 187, 204, 221, 238, 255, 0,
+			60, 0, 91, 0, 0, 255, 255, 0, 17, 34, 51, 68, 85,
+			102, 119, 136, 153, 170, 187, 204, 221, 238, 255,
+		},
+		fsggml.TensorTypeQ8_0: {
+			208, 51, 0, 4, 8, 12, 16, 20, 25, 29, 33, 37, 41,
+			45, 49, 53, 57, 61, 66, 70, 74, 78, 82, 86, 90, 94,
+			98, 102, 107, 111, 115, 119, 123, 127, 240, 55, 65, 67, 69,
+			71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95,
+			97, 99, 101, 103, 105, 107, 109, 111, 113, 115, 117, 119, 121,
+			123, 125, 127, 252, 57, 86, 87, 88, 90, 91, 92, 94, 95,
+			96, 98, 99, 100, 102, 103, 104, 106, 107, 108, 110, 111, 112,
+			114, 115, 116, 118, 119, 120, 122, 123, 124, 126, 127, 0, 60,
+			96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
+			109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
+			122, 123, 124, 125, 126, 127, 2, 61, 102, 103, 104, 105, 105,
+			106, 107, 108, 109, 109, 110, 111, 112, 113, 113, 114, 115, 116,
+			117, 117, 118, 119, 120, 121, 121, 122, 123, 124, 125, 125, 126,
+			127, 4, 62, 106, 107, 108, 108, 109, 110, 110, 111, 112, 112,
+			113, 114, 114, 115, 116, 116, 117, 118, 118, 119, 120, 120, 121,
+			122, 122, 123, 124, 124, 125, 126, 126, 127, 6, 63, 109, 110,
+			110, 111, 112, 112, 113, 113, 114, 114, 115, 116, 116, 117, 117,
+			118, 118, 119, 120, 120, 121, 121, 122, 122, 123, 124, 124, 125,
+			125, 126, 126, 127, 4, 64, 112, 112, 113, 113, 114, 114, 115,
+			115, 116, 116, 117, 117, 118, 118, 119, 119, 120, 120, 121, 121,
+			122, 122, 123, 123, 124, 124, 125, 125, 126, 126, 127, 127,
+		},
+		fsggml.TensorTypeBF16: {
+			0, 0, 128, 63, 0, 64, 64, 64, 128, 64, 160, 64, 192,
+			64, 224, 64, 0, 65, 16, 65, 32, 65, 48, 65, 64, 65,
+			80, 65, 96, 65, 112, 65, 128, 65, 136, 65, 144, 65, 152,
+			65, 160, 65, 168, 65, 176, 65, 184, 65, 192, 65, 200, 65,
+			208, 65, 216, 65, 224, 65, 232, 65, 240, 65, 248, 65, 0,
+			66, 4, 66, 8, 66, 12, 66, 16, 66, 20, 66, 24, 66,
+			28, 66, 32, 66, 36, 66, 40, 66, 44, 66, 48, 66, 52,
+			66, 56, 66, 60, 66, 64, 66, 68, 66, 72, 66, 76, 66,
+			80, 66, 84, 66, 88, 66, 92, 66, 96, 66, 100, 66, 104,
+			66, 108, 66, 112, 66, 116, 66, 120, 66, 124, 66, 128, 66,
+			130, 66, 132, 66, 134, 66, 136, 66, 138, 66, 140, 66, 142,
+			66, 144, 66, 146, 66, 148, 66, 150, 66, 152, 66, 154, 66,
+			156, 66, 158, 66, 160, 66, 162, 66, 164, 66, 166, 66, 168,
+			66, 170, 66, 172, 66, 174, 66, 176, 66, 178, 66, 180, 66,
+			182, 66, 184, 66, 186, 66, 188, 66, 190, 66, 192, 66, 194,
+			66, 196, 66, 198, 66, 200, 66, 202, 66, 204, 66, 206, 66,
+			208, 66, 210, 66, 212, 66, 214, 66, 216, 66, 218, 66, 220,
+			66, 222, 66, 224, 66, 226, 66, 228, 66, 230, 66, 232, 66,
+			234, 66, 236, 66, 238, 66, 240, 66, 242, 66, 244, 66, 246,
+			66, 248, 66, 250, 66, 252, 66, 254, 66, 0, 67, 1, 67,
+			2, 67, 3, 67, 4, 67, 5, 67, 6, 67, 7, 67, 8,
+			67, 9, 67, 10, 67, 11, 67, 12, 67, 13, 67, 14, 67,
+			15, 67, 16, 67, 17, 67, 18, 67, 19, 67, 20, 67, 21,
+			67, 22, 67, 23, 67, 24, 67, 25, 67, 26, 67, 27, 67,
+			28, 67, 29, 67, 30, 67, 31, 67, 32, 67, 33, 67, 34,
+			67, 35, 67, 36, 67, 37, 67, 38, 67, 39, 67, 40, 67,
+			41, 67, 42, 67, 43, 67, 44, 67, 45, 67, 46, 67, 47,
+			67, 48, 67, 49, 67, 50, 67, 51, 67, 52, 67, 53, 67,
+			54, 67, 55, 67, 56, 67, 57, 67, 58, 67, 59, 67, 60,
+			67, 61, 67, 62, 67, 63, 67, 64, 67, 65, 67, 66, 67,
+			67, 67, 68, 67, 69, 67, 70, 67, 71, 67, 72, 67, 73,
+			67, 74, 67, 75, 67, 76, 67, 77, 67, 78, 67, 79, 67,
+			80, 67, 81, 67, 82, 67, 83, 67, 84, 67, 85, 67, 86,
+			67, 87, 67, 88, 67, 89, 67, 90, 67, 91, 67, 92, 67,
+			93, 67, 94, 67, 95, 67, 96, 67, 97, 67, 98, 67, 99,
+			67, 100, 67, 101, 67, 102, 67, 103, 67, 104, 67, 105, 67,
+			106, 67, 107, 67, 108, 67, 109, 67, 110, 67, 111, 67, 112,
+			67, 113, 67, 114, 67, 115, 67, 116, 67, 117, 67, 118, 67,
+			119, 67, 120, 67, 121, 67, 122, 67, 123, 67, 124, 67, 125,
+			67, 126, 67, 127, 67,
+		},
+		fsggml.TensorTypeF16: {
+			0, 0, 0, 60, 0, 64, 0, 66, 0, 68, 0, 69, 0, 70, 0, 71, 0,
+			72, 128, 72, 0, 73, 128, 73, 0, 74, 128, 74, 0, 75, 128, 75,
+			0, 76, 64, 76, 128, 76, 192, 76, 0, 77, 64, 77, 128, 77, 192,
+			77, 0, 78, 64, 78, 128, 78, 192, 78, 0, 79, 64, 79, 128, 79,
+			192, 79, 0, 80, 32, 80, 64, 80, 96, 80, 128, 80, 160, 80,
+			192, 80, 224, 80, 0, 81, 32, 81, 64, 81, 96, 81, 128, 81,
+			160, 81, 192, 81, 224, 81, 0, 82, 32, 82, 64, 82, 96, 82,
+			128, 82, 160, 82, 192, 82, 224, 82, 0, 83, 32, 83, 64, 83,
+			96, 83, 128, 83, 160, 83, 192, 83, 224, 83, 0, 84, 16, 84,
+			32, 84, 48, 84, 64, 84, 80, 84, 96, 84, 112, 84, 128, 84,
+			144, 84, 160, 84, 176, 84, 192, 84, 208, 84, 224, 84, 240,
+			84, 0, 85, 16, 85, 32, 85, 48, 85, 64, 85, 80, 85, 96, 85,
+			112, 85, 128, 85, 144, 85, 160, 85, 176, 85, 192, 85, 208,
+			85, 224, 85, 240, 85, 0, 86, 16, 86, 32, 86, 48, 86, 64,
+			86, 80, 86, 96, 86, 112, 86, 128, 86, 144, 86, 160, 86,
+			176, 86, 192, 86, 208, 86, 224, 86, 240, 86, 0, 87, 16,
+			87, 32, 87, 48, 87, 64, 87, 80, 87, 96, 87, 112, 87, 128,
+			87, 144, 87, 160, 87, 176, 87, 192, 87, 208, 87, 224, 87,
+			240, 87, 0, 88, 8, 88, 16, 88, 24, 88, 32, 88, 40, 88,
+			48, 88, 56, 88, 64, 88, 72, 88, 80, 88, 88, 88, 96, 88,
+			104, 88, 112, 88, 120, 88, 128, 88, 136, 88, 144, 88, 152,
+			88, 160, 88, 168, 88, 176, 88, 184, 88, 192, 88, 200, 88,
+			208, 88, 216, 88, 224, 88, 232, 88, 240, 88, 248, 88, 0,
+			89, 8, 89, 16, 89, 24, 89, 32, 89, 40, 89, 48, 89, 56, 89,
+			64, 89, 72, 89, 80, 89, 88, 89, 96, 89, 104, 89, 112, 89,
+			120, 89, 128, 89, 136, 89, 144, 89, 152, 89, 160, 89, 168,
+			89, 176, 89, 184, 89, 192, 89, 200, 89, 208, 89, 216, 89,
+			224, 89, 232, 89, 240, 89, 248, 89, 0, 90, 8, 90, 16, 90,
+			24, 90, 32, 90, 40, 90, 48, 90, 56, 90, 64, 90, 72, 90, 80,
+			90, 88, 90, 96, 90, 104, 90, 112, 90, 120, 90, 128, 90,
+			136, 90, 144, 90, 152, 90, 160, 90, 168, 90, 176, 90, 184,
+			90, 192, 90, 200, 90, 208, 90, 216, 90, 224, 90, 232, 90,
+			240, 90, 248, 90, 0, 91, 8, 91, 16, 91, 24, 91, 32, 91, 40,
+			91, 48, 91, 56, 91, 64, 91, 72, 91, 80, 91, 88, 91, 96, 91,
+			104, 91, 112, 91, 120, 91, 128, 91, 136, 91, 144, 91, 152,
+			91, 160, 91, 168, 91, 176, 91, 184, 91, 192, 91, 200, 91,
+			208, 91, 216, 91, 224, 91, 232, 91, 240, 91, 248, 91,
+		},
+		fsggml.TensorTypeF32: {
+			0, 0, 0, 0, 0, 0, 128, 63, 0, 0, 0, 64, 0, 0, 64, 64, 0, 0, 128,
+			64, 0, 0, 160, 64, 0, 0, 192, 64, 0, 0, 224, 64, 0, 0, 0, 65, 0,
+			0, 16, 65, 0, 0, 32, 65, 0, 0, 48, 65, 0, 0, 64, 65, 0, 0, 80, 65,
+			0, 0, 96, 65, 0, 0, 112, 65, 0, 0, 128, 65, 0, 0, 136, 65, 0, 0,
+			144, 65, 0, 0, 152, 65, 0, 0, 160, 65, 0, 0, 168, 65, 0, 0, 176,
+			65, 0, 0, 184, 65, 0, 0, 192, 65, 0, 0, 200, 65, 0, 0, 208, 65, 0,
+			0, 216, 65, 0, 0, 224, 65, 0, 0, 232, 65, 0, 0, 240, 65, 0, 0, 248,
+			65, 0, 0, 0, 66, 0, 0, 4, 66, 0, 0, 8, 66, 0, 0, 12, 66, 0, 0, 16,
+			66, 0, 0, 20, 66, 0, 0, 24, 66, 0, 0, 28, 66, 0, 0, 32, 66, 0, 0,
+			36, 66, 0, 0, 40, 66, 0, 0, 44, 66, 0, 0, 48, 66, 0, 0, 52, 66, 0,
+			0, 56, 66, 0, 0, 60, 66, 0, 0, 64, 66, 0, 0, 68, 66, 0, 0, 72, 66,
+			0, 0, 76, 66, 0, 0, 80, 66, 0, 0, 84, 66, 0, 0, 88, 66, 0, 0, 92, 66,
+			0, 0, 96, 66, 0, 0, 100, 66, 0, 0, 104, 66, 0, 0, 108, 66, 0, 0, 112,
+			66, 0, 0, 116, 66, 0, 0, 120, 66, 0, 0, 124, 66, 0, 0, 128, 66, 0, 0,
+			130, 66, 0, 0, 132, 66, 0, 0, 134, 66, 0, 0, 136, 66, 0, 0, 138, 66,
+			0, 0, 140, 66, 0, 0, 142, 66, 0, 0, 144, 66, 0, 0, 146, 66, 0, 0, 148,
+			66, 0, 0, 150, 66, 0, 0, 152, 66, 0, 0, 154, 66, 0, 0, 156, 66, 0, 0,
+			158, 66, 0, 0, 160, 66, 0, 0, 162, 66, 0, 0, 164, 66, 0, 0, 166, 66,
+			0, 0, 168, 66, 0, 0, 170, 66, 0, 0, 172, 66, 0, 0, 174, 66, 0, 0, 176,
+			66, 0, 0, 178, 66, 0, 0, 180, 66, 0, 0, 182, 66, 0, 0, 184, 66, 0, 0,
+			186, 66, 0, 0, 188, 66, 0, 0, 190, 66, 0, 0, 192, 66, 0, 0, 194, 66, 0,
+			0, 196, 66, 0, 0, 198, 66, 0, 0, 200, 66, 0, 0, 202, 66, 0, 0, 204, 66,
+			0, 0, 206, 66, 0, 0, 208, 66, 0, 0, 210, 66, 0, 0, 212, 66, 0, 0, 214, 66,
+			0, 0, 216, 66, 0, 0, 218, 66, 0, 0, 220, 66, 0, 0, 222, 66, 0, 0, 224, 66,
+			0, 0, 226, 66, 0, 0, 228, 66, 0, 0, 230, 66, 0, 0, 232, 66, 0, 0, 234, 66,
+			0, 0, 236, 66, 0, 0, 238, 66, 0, 0, 240, 66, 0, 0, 242, 66, 0, 0, 244, 66,
+			0, 0, 246, 66, 0, 0, 248, 66, 0, 0, 250, 66, 0, 0, 252, 66, 0, 0, 254, 66,
+			0, 0, 0, 67, 0, 0, 1, 67, 0, 0, 2, 67, 0, 0, 3, 67, 0, 0, 4, 67, 0, 0, 5, 67,
+			0, 0, 6, 67, 0, 0, 7, 67, 0, 0, 8, 67, 0, 0, 9, 67, 0, 0, 10, 67, 0, 0, 11,
+			67, 0, 0, 12, 67, 0, 0, 13, 67, 0, 0, 14, 67, 0, 0, 15, 67, 0, 0, 16, 67,
+			0, 0, 17, 67, 0, 0, 18, 67, 0, 0, 19, 67, 0, 0, 20, 67, 0, 0, 21, 67, 0, 0,
+			22, 67, 0, 0, 23, 67, 0, 0, 24, 67, 0, 0, 25, 67, 0, 0, 26, 67, 0, 0, 27,
+			67, 0, 0, 28, 67, 0, 0, 29, 67, 0, 0, 30, 67, 0, 0, 31, 67, 0, 0, 32, 67,
+			0, 0, 33, 67, 0, 0, 34, 67, 0, 0, 35, 67, 0, 0, 36, 67, 0, 0, 37, 67, 0, 0,
+			38, 67, 0, 0, 39, 67, 0, 0, 40, 67, 0, 0, 41, 67, 0, 0, 42, 67, 0, 0, 43, 67,
+			0, 0, 44, 67, 0, 0, 45, 67, 0, 0, 46, 67, 0, 0, 47, 67, 0, 0, 48, 67, 0, 0,
+			49, 67, 0, 0, 50, 67, 0, 0, 51, 67, 0, 0, 52, 67, 0, 0, 53, 67, 0, 0, 54, 67,
+			0, 0, 55, 67, 0, 0, 56, 67, 0, 0, 57, 67, 0, 0, 58, 67, 0, 0, 59, 67, 0, 0,
+			60, 67, 0, 0, 61, 67, 0, 0, 62, 67, 0, 0, 63, 67, 0, 0, 64, 67, 0, 0, 65, 67,
+			0, 0, 66, 67, 0, 0, 67, 67, 0, 0, 68, 67, 0, 0, 69, 67, 0, 0, 70, 67, 0, 0, 71,
+			67, 0, 0, 72, 67, 0, 0, 73, 67, 0, 0, 74, 67, 0, 0, 75, 67, 0, 0, 76, 67, 0,
+			0, 77, 67, 0, 0, 78, 67, 0, 0, 79, 67, 0, 0, 80, 67, 0, 0, 81, 67, 0, 0, 82,
+			67, 0, 0, 83, 67, 0, 0, 84, 67, 0, 0, 85, 67, 0, 0, 86, 67, 0, 0, 87, 67, 0,
+			0, 88, 67, 0, 0, 89, 67, 0, 0, 90, 67, 0, 0, 91, 67, 0, 0, 92, 67, 0, 0, 93,
+			67, 0, 0, 94, 67, 0, 0, 95, 67, 0, 0, 96, 67, 0, 0, 97, 67, 0, 0, 98, 67, 0,
+			0, 99, 67, 0, 0, 100, 67, 0, 0, 101, 67, 0, 0, 102, 67, 0, 0, 103, 67, 0, 0,
+			104, 67, 0, 0, 105, 67, 0, 0, 106, 67, 0, 0, 107, 67, 0, 0, 108, 67, 0, 0, 109,
+			67, 0, 0, 110, 67, 0, 0, 111, 67, 0, 0, 112, 67, 0, 0, 113, 67, 0, 0, 114, 67,
+			0, 0, 115, 67, 0, 0, 116, 67, 0, 0, 117, 67, 0, 0, 118, 67, 0, 0, 119, 67, 0,
+			0, 120, 67, 0, 0, 121, 67, 0, 0, 122, 67, 0, 0, 123, 67, 0, 0, 124, 67, 0, 0,
+			125, 67, 0, 0, 126, 67, 0, 0, 127, 67,
+		},
+		fsggml.TensorTypeQ4_K: {
+			52, 52, 0, 0, 136, 208, 216, 223, 0, 0, 0, 0, 8, 0, 8, 15, 128,
+			128, 129, 129, 146, 146, 147, 147, 164, 164, 165, 165, 166, 182,
+			183, 183, 184, 200, 201, 201, 202, 218, 218, 219, 219, 236, 236,
+			237, 237, 254, 254, 255, 202, 202, 202, 203, 203, 203, 219, 219,
+			219, 220, 220, 220, 220, 220, 236, 237, 237, 237, 237, 237,
+			237, 237, 238, 254, 254, 254, 254, 254, 255, 255, 255, 255, 220,
+			220, 220, 220, 221, 221, 221, 221, 221, 221, 221, 237, 237, 237,
+			238, 238, 238, 238, 238, 238, 238, 238, 238, 254, 254, 255, 255,
+			255, 255, 255, 255, 255, 237, 237, 237, 237, 237, 237, 237, 238,
+			238, 238, 238, 238, 238, 238, 238, 238, 254, 254, 254, 254, 254,
+			254, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
+		},
+		fsggml.TensorTypeQ2_K: {
+			1, 2, 3, 3, 4, 5, 7, 7, 8, 9, 10, 11, 12, 13, 14, 15, 184, 184,
+			184, 185, 249, 249, 249, 249, 249, 250, 250, 254, 254, 254, 254,
+			255, 253, 253, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254,
+			254, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
+			255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
+			255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 171, 69, 0, 0,
+		},
+		fsggml.TensorTypeQ5_K: {
+			32, 48, 0, 0, 136, 208, 216, 223, 0, 0, 0, 0, 8, 0, 7, 15, 254,
+			254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254, 254,
+			254, 254, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,
+			255, 255, 255, 255, 255, 0, 1, 2, 19, 20, 37, 38, 55, 56, 73, 74,
+			91, 92, 109, 110, 127, 112, 128, 129, 146, 147, 164, 165, 182, 183,
+			200, 201, 218, 219, 236, 237, 254, 133, 133, 149, 150, 150, 150,
+			167, 167, 167, 168, 184, 184, 185, 185, 201, 202, 202, 202, 219,
+			219, 219, 219, 236, 236, 236, 237, 253, 253, 254, 254, 254, 255,
+			169, 169, 169, 169, 186, 186, 186, 186, 186, 187, 187, 203, 203,
+			203, 204, 204, 204, 220, 220, 221, 221, 221, 221, 237, 237, 238,
+			238, 238, 238, 254, 255, 255, 203, 203, 203, 204, 204, 204, 204,
+			204, 220, 220, 220, 221, 221, 221, 221, 221, 237, 237, 238, 238,
+			238, 238, 238, 238, 254, 255, 255, 255, 255, 255, 255, 255,
+		},
+		fsggml.TensorTypeQ6_K: {
+			96, 110, 92, 90, 88, 70, 68, 50, 48, 46, 44, 42, 24, 22, 4, 2, 80,
+			95, 78, 77, 76, 59, 58, 57, 40, 39, 38, 21, 20, 19, 2, 1, 75, 75,
+			74, 57, 57, 56, 55, 39, 38, 37, 21, 20, 20, 19, 2, 2, 72, 55, 55,
+			54, 54, 37, 37, 36, 36, 19, 19, 18, 18, 1, 1, 0, 35, 35, 35, 35,
+			34, 18, 18, 18, 17, 17, 17, 1, 1, 0, 0, 0, 35, 35, 34, 34, 18,
+			18, 18, 17, 17, 17, 17, 1, 0, 0, 0, 0, 35, 35, 35, 19, 19, 18, 18,
+			18, 18, 18, 1, 1, 1, 1, 1, 1, 34, 34, 18, 18, 18, 18, 17, 17, 17,
+			17, 1, 1, 0, 0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
+			0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+			0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+			0, 0, 0, 0, 0, 248, 240, 231, 224, 216, 208, 200, 192, 184, 176,
+			166, 160, 152, 144, 136, 128, 235, 43,
+		},
+		fsggml.TensorTypeQ3_K: {
+			1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+			0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 20, 23, 23, 7, 7, 6, 6, 6, 2,
+			1, 1, 1, 1, 0, 0, 22, 22, 6, 6, 5, 5, 5, 1, 1, 1, 1, 1, 0, 0, 0,
+			0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+			0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 238, 204, 170, 136, 102, 68,
+			34, 1, 5, 5, 5, 5, 189, 63,
+		},
+	}
+)

server/routes_create_test.go

@@ -24,7 +24,7 @@ import (
 
 var stream bool = false
 
-func createBinFile(t *testing.T, kv map[string]any, ti []ggml.Tensor) (string, string) {
+func createBinFile(t *testing.T, kv map[string]any, ti []*ggml.Tensor) (string, string) {
 	t.Helper()
 	t.Setenv("OLLAMA_MODELS", cmp.Or(os.Getenv("OLLAMA_MODELS"), t.TempDir()))
 

server/routes_generate_test.go

@@ -99,7 +99,7 @@ func TestGenerateChat(t *testing.T) {
 		"tokenizer.ggml.tokens":         []string{""},
 		"tokenizer.ggml.scores":         []float32{0},
 		"tokenizer.ggml.token_type":     []int32{0},
-	}, []ggml.Tensor{
+	}, []*ggml.Tensor{
 		{Name: "token_embd.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
 		{Name: "blk.0.attn_norm.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
 		{Name: "blk.0.ffn_down.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
@@ -158,7 +158,7 @@ func TestGenerateChat(t *testing.T) {
 		_, digest := createBinFile(t, ggml.KV{
 			"general.architecture": "bert",
 			"bert.pooling_type":    uint32(0),
-		}, []ggml.Tensor{})
+		}, []*ggml.Tensor{})
 		w := createRequest(t, s.CreateHandler, api.CreateRequest{
 			Model:  "bert",
 			Files:  map[string]string{"bert.gguf": digest},
@@ -643,7 +643,7 @@ func TestGenerate(t *testing.T) {
 		"tokenizer.ggml.tokens":         []string{""},
 		"tokenizer.ggml.scores":         []float32{0},
 		"tokenizer.ggml.token_type":     []int32{0},
-	}, []ggml.Tensor{
+	}, []*ggml.Tensor{
 		{Name: "token_embd.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
 		{Name: "blk.0.attn_norm.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
 		{Name: "blk.0.ffn_down.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
@@ -698,7 +698,7 @@ func TestGenerate(t *testing.T) {
 		_, digest := createBinFile(t, ggml.KV{
 			"general.architecture": "bert",
 			"bert.pooling_type":    uint32(0),
-		}, []ggml.Tensor{})
+		}, []*ggml.Tensor{})
 
 		w := createRequest(t, s.CreateHandler, api.CreateRequest{
 			Model:  "bert",

server/sched_test.go

@@ -126,7 +126,7 @@ func newScenarioRequest(t *testing.T, ctx context.Context, modelName string, est
 		"tokenizer.ggml.tokens":         []string{" "},
 		"tokenizer.ggml.scores":         []float32{0},
 		"tokenizer.ggml.token_type":     []int32{0},
-	}, []ggml.Tensor{
+	}, []*ggml.Tensor{
 		{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
 		{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
 	}))