next

cache/cache.go

@@ -0,0 +1,63 @@
+package cache
+
+import (
+	"github.com/ollama/ollama/ml"
+)
+
+type Options struct {
+	Position int
+}
+
+type Cache interface {
+	Sub(i int) Cache
+	Put(ctx ml.Context, key, value ml.Tensor, opts Options) (ml.Tensor, ml.Tensor)
+}
+
+type Simple struct {
+	DType    ml.DType
+	Capacity int
+
+	keys, values []ml.Tensor
+}
+
+func (c *Simple) Sub(i int) Cache {
+	if i >= len(c.keys) {
+		c.keys = append(c.keys, make([]ml.Tensor, i-len(c.keys)+1)...)
+		c.values = append(c.values, make([]ml.Tensor, i-len(c.values)+1)...)
+	}
+
+	return &Simple{
+		keys:     c.keys[i : i+1],
+		values:   c.values[i : i+1],
+		Capacity: c.Capacity,
+		DType:    c.DType,
+	}
+}
+
+func (c *Simple) Put(ctx ml.Context, key, value ml.Tensor, opts Options) (ml.Tensor, ml.Tensor) {
+	if c.keys[0] == nil || c.values[0] == nil {
+		c.keys[0] = ctx.Zeros(c.DType, int(key.Dim(0)*key.Dim(1))*c.Capacity)
+		c.values[0] = ctx.Zeros(c.DType, int(value.Dim(0)*value.Dim(1))*c.Capacity)
+	}
+
+	ctx.Forward(key.Copy(ctx, c.keys[0].View(ctx, int(key.Stride(2))*opts.Position, int(key.Dim(0)*key.Dim(1)*key.Dim(2)))))
+	ctx.Forward(value.Copy(ctx, c.values[0].View(ctx, int(value.Stride(2))*opts.Position, int(value.Dim(0)*value.Dim(1)*value.Dim(2)))))
+
+	n := min(c.Capacity, int(key.Dim(2))+opts.Position)
+
+	key = c.keys[0].View(ctx, 0,
+		int(key.Dim(0)), int(key.Stride(1)),
+		int(key.Dim(1)), int(key.Stride(2)),
+		n,
+	)
+
+	value = c.values[0].View(ctx, 0,
+		int(value.Dim(0)), int(value.Stride(1)),
+		int(value.Dim(1)), int(value.Stride(2)),
+		n,
+	)
+
+	// TODO shift context if necessary
+
+	return key, value
+}

convert/convert.go

@@ -9,7 +9,7 @@ import (
 	"log/slog"
 	"strings"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type ModelParameters struct {
@@ -27,8 +27,8 @@ type AdapterParameters struct {
 	} `json:"lora_parameters"`
 }
 
-func (ModelParameters) KV(t *Tokenizer) llm.KV {
-	kv := llm.KV{
+func (ModelParameters) KV(t *Tokenizer) ggml.KV {
+	kv := ggml.KV{
 		"general.file_type":            uint32(1),
 		"general.quantization_version": uint32(2),
 		"tokenizer.ggml.pre":           t.Pre,
@@ -54,7 +54,7 @@ func (ModelParameters) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p AdapterParameters) KV() llm.KV {
+func (p AdapterParameters) KV() ggml.KV {
 	var alpha float32
 	if p.LoraParameters.Alpha == 0 {
 		alpha = float32(p.Alpha)
@@ -62,7 +62,7 @@ func (p AdapterParameters) KV() llm.KV {
 		alpha = p.LoraParameters.Alpha
 	}
 
-	kv := llm.KV{
+	kv := ggml.KV{
 		"adapter.lora.alpha": alpha,
 		"adapter.type":       "lora",
 		"general.file_type":  uint32(1),
@@ -79,19 +79,19 @@ func (ModelParameters) specialTokenTypes() []string {
 	}
 }
 
-func (ModelParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
-	return llm.WriteGGUF(ws, kv, ts)
+func (ModelParameters) writeFile(ws io.WriteSeeker, kv ggml.KV, ts []ggml.Tensor) error {
+	return ggml.WriteGGUF(ws, kv, ts)
 }
 
-func (AdapterParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
-	return llm.WriteGGUF(ws, kv, ts)
+func (AdapterParameters) writeFile(ws io.WriteSeeker, kv ggml.KV, ts []ggml.Tensor) error {
+	return ggml.WriteGGUF(ws, kv, ts)
 }
 
 type ModelConverter interface {
 	// KV maps parameters to LLM key-values
-	KV(*Tokenizer) llm.KV
+	KV(*Tokenizer) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
-	Tensors([]Tensor) []llm.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
@@ -99,7 +99,7 @@ type ModelConverter interface {
 	// specialTokenTypes returns any special token types the model uses
 	specialTokenTypes() []string
 	// writeFile writes the model to the provided io.WriteSeeker
-	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
+	writeFile(io.WriteSeeker, ggml.KV, []ggml.Tensor) error
 }
 
 type moreParser interface {
@@ -108,17 +108,17 @@ type moreParser interface {
 
 type AdapterConverter interface {
 	// KV maps parameters to LLM key-values
-	KV(llm.KV) llm.KV
+	KV(ggml.KV) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
-	Tensors([]Tensor) []llm.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
 
-	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
+	writeFile(io.WriteSeeker, ggml.KV, []ggml.Tensor) error
 }
 
-func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV llm.KV) error {
+func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV ggml.KV) error {
 	bts, err := fs.ReadFile(fsys, "adapter_config.json")
 	if err != nil {
 		return err

convert/convert_bert.go

@@ -8,7 +8,7 @@ import (
 	"slices"
 	"strings"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type bertModel struct {
@@ -85,7 +85,7 @@ func (p *bertModel) parseMore(fsys fs.FS) error {
 	return nil
 }
 
-func (p *bertModel) KV(t *Tokenizer) llm.KV {
+func (p *bertModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "bert"
 	kv["bert.attention.causal"] = false
@@ -132,8 +132,8 @@ func (p *bertModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *bertModel) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
+func (p *bertModel) Tensors(ts []Tensor) []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) []llm.Tensor {
 			continue
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_commandr.go

@@ -3,7 +3,7 @@ package convert
 import (
 	"cmp"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type commandrModel struct {
@@ -24,7 +24,7 @@ type commandrModel struct {
 
 var _ ModelConverter = (*commandrModel)(nil)
 
-func (p *commandrModel) KV(t *Tokenizer) llm.KV {
+func (p *commandrModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "command-r"
 	kv["general.name"] = "command-r"
@@ -43,10 +43,10 @@ func (p *commandrModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *commandrModel) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
+func (p *commandrModel) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 	for _, t := range ts {
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma.go

@@ -6,7 +6,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type gemmaModel struct {
@@ -23,7 +23,7 @@ type gemmaModel struct {
 
 var _ ModelConverter = (*gemmaModel)(nil)
 
-func (p *gemmaModel) KV(t *Tokenizer) llm.KV {
+func (p *gemmaModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma"
 	kv["gemma.context_length"] = p.MaxPositionEmbeddings
@@ -42,14 +42,14 @@ func (p *gemmaModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *gemmaModel) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
+func (p *gemmaModel) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 	for _, t := range ts {
 		if strings.HasSuffix(t.Name(), "_norm.weight") {
 			t.SetRepacker(p.addOne)
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma2.go

@@ -1,8 +1,6 @@
 package convert
 
-import (
-	"github.com/ollama/ollama/llm"
-)
+import "github.com/ollama/ollama/fs/ggml"
 
 type gemma2Model struct {
 	gemmaModel
@@ -11,7 +9,7 @@ type gemma2Model struct {
 	FinalLogitSoftcap     float32 `json:"final_logit_softcapping"`
 }
 
-func (p *gemma2Model) KV(t *Tokenizer) llm.KV {
+func (p *gemma2Model) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma2"
 	kv["gemma2.context_length"] = p.MaxPositionEmbeddings

convert/convert_gemma2_adapter.go

@@ -6,7 +6,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type gemma2Adapter struct {
@@ -15,14 +15,14 @@ type gemma2Adapter struct {
 
 var _ AdapterConverter = (*gemma2Adapter)(nil)
 
-func (p *gemma2Adapter) KV(baseKV llm.KV) llm.KV {
+func (p *gemma2Adapter) KV(baseKV ggml.KV) ggml.KV {
 	kv := p.AdapterParameters.KV()
 	kv["general.architecture"] = "gemma2"
 	return kv
 }
 
-func (p *gemma2Adapter) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
+func (p *gemma2Adapter) Tensors(ts []Tensor) []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) []llm.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_llama.go

@@ -9,7 +9,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type llamaModel struct {
@@ -46,7 +46,7 @@ type llamaModel struct {
 
 var _ ModelConverter = (*llamaModel)(nil)
 
-func (p *llamaModel) KV(t *Tokenizer) llm.KV {
+func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "llama"
 	kv["llama.vocab_size"] = p.VocabSize
@@ -120,11 +120,11 @@ func (p *llamaModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *llamaModel) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
+func (p *llamaModel) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 
 	if p.RopeScaling.factors != nil {
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     "rope_freqs.weight",
 			Kind:     0,
 			Shape:    []uint64{uint64(len(p.RopeScaling.factors))},
@@ -138,7 +138,7 @@ func (p *llamaModel) Tensors(ts []Tensor) []llm.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_llama_adapter.go

@@ -7,7 +7,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type llamaAdapter struct {
@@ -18,7 +18,7 @@ type llamaAdapter struct {
 
 var _ AdapterConverter = (*llamaAdapter)(nil)
 
-func (p *llamaAdapter) KV(baseKV llm.KV) llm.KV {
+func (p *llamaAdapter) KV(baseKV ggml.KV) ggml.KV {
 	kv := p.AdapterParameters.KV()
 	kv["general.architecture"] = "llama"
 	kv["llama.attention.head_count"] = baseKV["llama.attention.head_count"]
@@ -29,8 +29,8 @@ func (p *llamaAdapter) KV(baseKV llm.KV) llm.KV {
 	return kv
 }
 
-func (p *llamaAdapter) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
+func (p *llamaAdapter) Tensors(ts []Tensor) []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) []llm.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    shape,

convert/convert_mixtral.go

@@ -6,7 +6,7 @@ import (
 	"slices"
 	"strings"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type mixtralModel struct {
@@ -15,7 +15,7 @@ type mixtralModel struct {
 	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
 }
 
-func (p *mixtralModel) KV(t *Tokenizer) llm.KV {
+func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.llamaModel.KV(t)
 
 	if p.NumLocalExperts > 0 {
@@ -29,7 +29,7 @@ func (p *mixtralModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *mixtralModel) Tensors(ts []Tensor) []llm.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) []llm.Tensor {
 		return true
 	})
 
-	var out []llm.Tensor
+	var out []ggml.Tensor
 	for n, e := range experts {
 		// TODO(mxyng): sanity check experts
-		out = append(out, llm.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

@@ -8,7 +8,7 @@ import (
 	"strings"
 	"sync"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type phi3Model struct {
@@ -37,7 +37,7 @@ type phi3Model struct {
 
 var _ ModelConverter = (*phi3Model)(nil)
 
-func (p *phi3Model) KV(t *Tokenizer) llm.KV {
+func (p *phi3Model) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "phi3"
 	kv["phi3.context_length"] = p.MaxPositionEmbeddings
@@ -68,19 +68,19 @@ func (p *phi3Model) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *phi3Model) Tensors(ts []Tensor) []llm.Tensor {
+func (p *phi3Model) Tensors(ts []Tensor) []ggml.Tensor {
 	var addRopeFactors sync.Once
 
-	out := make([]llm.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, llm.Tensor{
+				out = append(out, ggml.Tensor{
 					Name:     "rope_factors_long.weight",
 					Kind:     0,
 					Shape:    []uint64{uint64(len(p.RopeScaling.LongFactor))},
 					WriterTo: p.RopeScaling.LongFactor,
-				}, llm.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) []llm.Tensor {
 			})
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_qwen2.go

@@ -1,6 +1,7 @@
 package convert
 
-import "github.com/ollama/ollama/llm"
+import "github.com/ollama/ollama/fs/ggml"
+
 
 type qwen2Model struct {
 	ModelParameters
@@ -21,7 +22,7 @@ type qwen2Model struct {
 
 var _ ModelConverter = (*qwen2Model)(nil)
 
-func (q *qwen2Model) KV(t *Tokenizer) llm.KV {
+func (q *qwen2Model) KV(t *Tokenizer) ggml.KV {
 	kv := q.ModelParameters.KV(t)
 	kv["general.architecture"] = "qwen2"
 	kv["qwen2.block_count"] = q.HiddenLayers
@@ -45,10 +46,10 @@ func (q *qwen2Model) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (q *qwen2Model) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
+func (q *qwen2Model) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 	for _, t := range ts {
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_test.go

@@ -20,7 +20,7 @@ import (
 
 	"golang.org/x/exp/maps"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type tensorData struct {
@@ -29,7 +29,7 @@ type tensorData struct {
 	Shape   []int  `json:"shape"`
 }
 
-func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
+func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
 	t.Helper()
 
 	f, err := os.CreateTemp(t.TempDir(), "f16")
@@ -48,7 +48,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
 	}
 	t.Cleanup(func() { r.Close() })
 
-	m, _, err := llm.DecodeGGML(r, math.MaxInt)
+	m, _, err := ggml.Decode(r, math.MaxInt)
 	if err != nil {
 		t.Fatal(err)
 	}
@@ -60,7 +60,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
 	return r, m.KV(), m.Tensors()
 }
 
-func generateResultsJSON(t *testing.T, f *os.File, kv llm.KV, tensors *llm.Tensors) map[string]string {
+func generateResultsJSON(t *testing.T, f *os.File, kv ggml.KV, tensors ggml.Tensors) map[string]string {
 	actual := make(map[string]string)
 	for k, v := range kv {
 		if s, ok := v.(json.Marshaler); !ok {
@@ -75,7 +75,7 @@ func generateResultsJSON(t *testing.T, f *os.File, kv llm.KV, tensors *llm.Tenso
 		}
 	}
 
-	for _, tensor := range tensors.Items {
+	for _, tensor := range tensors.Items() {
 		sha256sum := sha256.New()
 		sr := io.NewSectionReader(f, int64(tensors.Offset+tensor.Offset), int64(tensor.Size()))
 		if _, err := io.Copy(sha256sum, sr); err != nil {
@@ -332,7 +332,7 @@ func TestConvertAdapter(t *testing.T) {
 			}
 			defer r.Close()
 
-			m, _, err := llm.DecodeGGML(r, math.MaxInt)
+			m, _, err := ggml.Decode(r, math.MaxInt)
 			if err != nil {
 				t.Fatal(err)
 			}

llm/ggml.go → fs/ggml/ggml.go

@@ -1,15 +1,15 @@
-package llm
+package ggml
 
 import (
 	"encoding/binary"
 	"errors"
 	"fmt"
 	"io"
+	"log/slog"
 	"slices"
 	"strings"
-	"sync"
 
-	"github.com/ollama/ollama/util/bufioutil"
+	"github.com/ollama/ollama/fs/util/bufioutil"
 )
 
 type GGML struct {
@@ -19,145 +19,168 @@ type GGML struct {
 
 type model interface {
 	KV() KV
-	Tensors() *Tensors
+	Tensors() Tensors
 }
 
 type KV map[string]any
 
-func (kv KV) u64(key string) uint64 {
-	switch v := kv[key].(type) {
-	case uint64:
-		return v
-	case uint32:
-		return uint64(v)
-	case float64:
-		return uint64(v)
-	default:
-		return 0
-	}
-}
-
 func (kv KV) Architecture() string {
-	if s, ok := kv["general.architecture"].(string); ok {
-		return s
-	}
-
-	return "unknown"
+	return kv.String("general.architecture", "unknown")
 }
 
 func (kv KV) Kind() string {
-	if s, ok := kv["general.type"].(string); ok {
-		return s
-	}
-
-	return "unknown"
+	return kv.String("general.type", "unknown")
 }
 
 func (kv KV) ParameterCount() uint64 {
-	return kv.u64("general.parameter_count")
+	return keyValue[uint64](kv, "general.parameter_count")
 }
 
 func (kv KV) FileType() fileType {
-	if u64 := kv.u64("general.file_type"); u64 > 0 {
-		return fileType(uint32(u64))
+	if t := kv.Uint("general.file_type"); t > 0 {
+		return fileType(t)
 	}
 
 	return fileTypeUnknown
 }
 
 func (kv KV) BlockCount() uint64 {
-	return kv.u64(fmt.Sprintf("%s.block_count", kv.Architecture()))
+	return uint64(kv.Uint("block_count"))
+}
+
+func (kv KV) EmbeddingLength() uint64 {
+	return uint64(kv.Uint("embedding_length"))
 }
 
 func (kv KV) HeadCount() uint64 {
-	return kv.u64(fmt.Sprintf("%s.attention.head_count", kv.Architecture()))
+	return uint64(kv.Uint("attention.head_count"))
 }
 
 func (kv KV) HeadCountKV() uint64 {
-	if headCountKV := kv.u64(fmt.Sprintf("%s.attention.head_count_kv", kv.Architecture())); headCountKV > 0 {
-		return headCountKV
-	}
-
-	return 1
+	return uint64(kv.Uint("attention.head_count_kv", 1))
 }
 
 func (kv KV) EmbeddingHeadCount() uint64 {
 	if heads := kv.HeadCount(); heads > 0 {
-		return kv.EmbeddingLength() / kv.HeadCount()
+		return kv.EmbeddingLength() / heads
 	}
 
 	return 0
 }
 
 func (kv KV) EmbeddingHeadCountK() uint64 {
-	if k := kv.u64(fmt.Sprintf("%s.attention.key_length", kv.Architecture())); k > 0 {
-		return k
-	}
-
-	return kv.EmbeddingHeadCount()
+	return uint64(kv.Uint("attention.key_length", uint32(kv.EmbeddingHeadCount())))
 }
 
 func (kv KV) EmbeddingHeadCountV() uint64 {
-	if v := kv.u64(fmt.Sprintf("%s.attention.value_length", kv.Architecture())); v > 0 {
-		return v
-	}
-
-	return kv.EmbeddingHeadCount()
+	return uint64(kv.Uint("attention.value_length", uint32(kv.EmbeddingHeadCount())))
 }
 
 func (kv KV) GQA() uint64 {
 	return kv.HeadCount() / kv.HeadCountKV()
 }
 
-func (kv KV) EmbeddingLength() uint64 {
-	return kv.u64(fmt.Sprintf("%s.embedding_length", kv.Architecture()))
-}
-
 func (kv KV) ContextLength() uint64 {
-	return kv.u64(fmt.Sprintf("%s.context_length", kv.Architecture()))
+	return uint64(kv.Uint("context_length"))
 }
 
 func (kv KV) ChatTemplate() string {
-	s, _ := kv["tokenizer.chat_template"].(string)
+	return kv.String("tokenizer.chat_template")
+}
+
+func (kv KV) String(key string, defaultValue ...string) string {
+	return keyValue(kv, key, append(defaultValue, "")...)
+}
+
+func (kv KV) Uint(key string, defaultValue ...uint32) uint32 {
+	return keyValue(kv, key, append(defaultValue, 0)...)
+}
+
+func (kv KV) Float(key string, defaultValue ...float32) float32 {
+	return keyValue(kv, key, append(defaultValue, 0)...)
+}
+
+func (kv KV) Strings(key string, defaultValue ...[]string) []string {
+	r := keyValue(kv, key, &array{})
+	s := make([]string, r.size)
+	for i := range r.size {
+		s[i] = r.values[i].(string)
+	}
+
+	return s
+}
+
+func (kv KV) Uints(key string, defaultValue ...[]uint32) []uint32 {
+	r := keyValue(kv, key, &array{})
+	s := make([]uint32, r.size)
+	for i := range r.size {
+		s[i] = uint32(r.values[i].(int32))
+	}
+
 	return s
 }
 
+func keyValue[T string | uint32 | uint64 | float32 | *array](kv KV, key string, defaultValue ...T) T {
+	if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") {
+		key = kv.Architecture() + "." + key
+	}
+
+	if val, ok := kv[key]; ok {
+		return val.(T)
+	}
+
+	slog.Warn("key not found", "key", key, "default", defaultValue[0])
+	return defaultValue[0]
+}
+
 type Tensors struct {
-	Items  []*Tensor
+	items  []*Tensor
 	Offset uint64
+}
 
-	layers     map[string]Layer
-	layersOnce sync.Once
-}
-
-func (ts *Tensors) Layers() map[string]Layer {
-	ts.layersOnce.Do(func() {
-		ts.layers = make(map[string]Layer)
-		for _, t := range ts.Items {
-			parts := strings.Split(t.Name, ".")
-			if index := slices.IndexFunc(parts, func(s string) bool { return s == "blk" || s == "mm" }); index != -1 {
-				if len(parts) > index+2 {
-					// blk and mm should have a number after them, join it
-					parts = append(
-						[]string{strings.Join(parts[:index+2], ".")},
-						parts[index+2:]...)
-				}
-			}
+func (s Tensors) Items(prefix ...string) []*Tensor {
+	if len(prefix) == 0 {
+		return s.items
+	}
 
-			if _, ok := ts.layers[parts[0]]; !ok {
-				ts.layers[parts[0]] = make(Layer)
-			}
+	var items []*Tensor
+	for _, t := range s.items {
+		if strings.HasPrefix(t.Name, prefix[0]) {
+			items = append(items, t)
+		}
+	}
 
-			ts.layers[parts[0]][strings.Join(parts[1:], ".")] = t
+	return items
+}
+
+func (ts Tensors) Layers() map[string]Layer {
+	layers := make(map[string]Layer)
+	for _, t := range ts.items {
+		parts := strings.Split(t.Name, ".")
+		if i := slices.Index(parts, "blk"); i > 0 {
+			parts = append([]string{
+				strings.Join(parts[:i], "."),
+				strings.Join(parts[i:i+2], "."),
+			}, parts[i+2:]...)
+		} else if i == 0 {
+			parts = append([]string{
+				strings.Join(parts[i:i+2], "."),
+			}, parts[i+2:]...)
 		}
-	})
 
-	return ts.layers
+		if _, ok := layers[parts[0]]; !ok {
+			layers[parts[0]] = make(Layer)
+		}
+
+		layers[parts[0]][strings.Join(parts[1:], ".")] = t
+	}
+
+	return layers
 }
 
 type Layer map[string]*Tensor
 
-func (l Layer) size() (size uint64) {
+func (l Layer) Size() (size uint64) {
 	for _, t := range l {
 		size += t.Size()
 	}
@@ -255,8 +278,6 @@ func (t Tensor) typeSize() uint64 {
 		return 8
 	case 29: // IQ1_M
 		return blockSize/8 + blockSize/16 + blockSize/32
-	case 30: // BF16
-		return 2
 	default:
 		return 0
 	}
@@ -295,7 +316,7 @@ const (
 
 var ErrUnsupportedFormat = errors.New("unsupported model format")
 
-func DetectGGMLType(b []byte) string {
+func DetectContentType(b []byte) string {
 	switch binary.LittleEndian.Uint32(b[:4]) {
 	case FILE_MAGIC_GGML:
 		return "ggml"
@@ -312,12 +333,12 @@ func DetectGGMLType(b []byte) string {
 	}
 }
 
-// DecodeGGML decodes a GGML model from the given reader.
+// Decode decodes a GGML model from the given reader.
 //
 // It collects array values for arrays with a size less than or equal to
 // maxArraySize. If maxArraySize is 0, the default value of 1024 is used. If
 // the maxArraySize is negative, all arrays are collected.
-func DecodeGGML(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
+func Decode(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
 	if maxArraySize == 0 {
 		maxArraySize = 1024
 	}
@@ -331,10 +352,6 @@ func DecodeGGML(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
 
 	var c container
 	switch magic {
-	case FILE_MAGIC_GGML, FILE_MAGIC_GGMF, FILE_MAGIC_GGJT:
-		return nil, 0, ErrUnsupportedFormat
-	case FILE_MAGIC_GGLA:
-		c = &containerGGLA{}
 	case FILE_MAGIC_GGUF_LE:
 		c = &containerGGUF{ByteOrder: binary.LittleEndian, maxArraySize: maxArraySize}
 	case FILE_MAGIC_GGUF_BE:
@@ -530,21 +547,20 @@ func (llm GGML) GraphSize(context, batch uint64, kvCacheType string) (kv, partia
 }
 
 // SupportsKVCacheType checks if the requested cache type is supported
-func (ggml GGML) SupportsKVCacheType(cacheType string) bool {
-	validKVCacheTypes := []string{"f16", "q8_0", "q4_0"}
-	return slices.Contains(validKVCacheTypes, cacheType)
+func (llm GGML) SupportsKVCacheType(cacheType string) bool {
+	return slices.Contains([]string{"f16", "q8_0", "q4_0"}, cacheType)
 }
 
 // SupportsFlashAttention checks if the model supports flash attention
-func (ggml GGML) SupportsFlashAttention() bool {
-	_, isEmbedding := ggml.KV()[fmt.Sprintf("%s.pooling_type", ggml.KV().Architecture())]
+func (llm GGML) SupportsFlashAttention() bool {
+	_, isEmbedding := llm.KV()[fmt.Sprintf("%s.pooling_type", llm.KV().Architecture())]
 	if isEmbedding {
 		return false
 	}
 
 	// Check head counts match and are non-zero
-	headCountK := ggml.KV().EmbeddingHeadCountK()
-	headCountV := ggml.KV().EmbeddingHeadCountV()
+	headCountK := llm.KV().EmbeddingHeadCountK()
+	headCountV := llm.KV().EmbeddingHeadCountV()
 	return headCountK != 0 && headCountV != 0 && headCountK == headCountV
 }
 

llm/gguf.go → fs/ggml/gguf.go

@@ -1,4 +1,4 @@
-package llm
+package ggml
 
 import (
 	"bytes"
@@ -8,10 +8,9 @@ import (
 	"fmt"
 	"io"
 	"log/slog"
+	"maps"
 	"slices"
 	"strings"
-
-	"golang.org/x/exp/maps"
 )
 
 type containerGGUF struct {
@@ -110,9 +109,9 @@ func (llm *gguf) KV() KV {
 	return llm.kv
 }
 
-func (llm *gguf) Tensors() *Tensors {
-	return &Tensors{
-		Items:  llm.tensors,
+func (llm *gguf) Tensors() Tensors {
+	return Tensors{
+		items:  llm.tensors,
 		Offset: llm.tensorOffset,
 	}
 }
@@ -523,7 +522,7 @@ func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
 		return err
 	}
 
-	keys := maps.Keys(kv)
+	keys := slices.Collect(maps.Keys(kv))
 	slices.Sort(keys)
 
 	for _, key := range keys {

llm/filetype.go → fs/ggml/type.go

@@ -1,4 +1,4 @@
-package llm
+package ggml
 
 import "fmt"
 
@@ -32,10 +32,9 @@ const (
 	fileTypeIQ1_S
 	fileTypeIQ4_NL
 	fileTypeIQ3_S
-	fileTypeIQ3_M
 	fileTypeIQ2_S
-	fileTypeIQ2_M
 	fileTypeIQ4_XS
+	fileTypeIQ2_M
 	fileTypeIQ1_M
 	fileTypeBF16
 
@@ -94,8 +93,6 @@ func ParseFileType(s string) (fileType, error) {
 		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 "IQ4_XS":
@@ -163,8 +160,6 @@ func (t fileType) String() string {
 		return "IQ4_NL"
 	case fileTypeIQ3_S:
 		return "IQ3_S"
-	case fileTypeIQ3_M:
-		return "IQ3_M"
 	case fileTypeIQ2_S:
 		return "IQ2_S"
 	case fileTypeIQ4_XS:

llm/ggla.go

@@ -1,149 +0,0 @@
-package llm
-
-import (
-	"encoding/binary"
-	"errors"
-	"io"
-	"slices"
-)
-
-type containerGGLA struct {
-	version uint32
-}
-
-func (c *containerGGLA) Name() string {
-	return "ggla"
-}
-
-func (c *containerGGLA) Decode(rs io.ReadSeeker) (model, error) {
-	if err := binary.Read(rs, binary.LittleEndian, &c.version); err != nil {
-		return nil, err
-	}
-
-	switch c.version {
-	case 1:
-	default:
-		return nil, errors.New("invalid version")
-	}
-
-	model := newGGLA(c)
-	err := model.decode(rs)
-	return model, err
-}
-
-type ggla struct {
-	*containerGGLA
-
-	kv      KV
-	tensors []*Tensor
-
-	tensorOffset uint64
-}
-
-func newGGLA(container *containerGGLA) *ggla {
-	return &ggla{
-		containerGGLA: container,
-		kv:            make(KV),
-	}
-}
-
-func (llm *ggla) KV() KV {
-	return llm.kv
-}
-
-func (llm *ggla) Tensors() *Tensors {
-	return &Tensors{
-		Items:  llm.tensors,
-		Offset: llm.tensorOffset,
-	}
-}
-
-func (llm *ggla) decode(rs io.ReadSeeker) (retErr error) {
-	var r uint32
-	if err := binary.Read(rs, binary.LittleEndian, &r); err != nil {
-		return err
-	}
-	llm.kv["r"] = r
-
-	var alpha uint32
-	if err := binary.Read(rs, binary.LittleEndian, &alpha); err != nil {
-		return err
-	}
-	llm.kv["alpha"] = alpha
-
-	offset, err := rs.Seek(0, io.SeekCurrent)
-	if err != nil {
-		return err
-	}
-
-	llm.tensorOffset = uint64(offset)
-
-	for {
-		var dims uint32
-		if err := binary.Read(rs, binary.LittleEndian, &dims); err != nil {
-			if errors.Is(err, io.EOF) {
-				return nil
-			}
-			return err
-		}
-
-		defer func() {
-			if errors.Is(retErr, io.EOF) {
-				retErr = io.ErrUnexpectedEOF
-			}
-		}()
-
-		var namesize uint32
-		if err := binary.Read(rs, binary.LittleEndian, &namesize); err != nil {
-			return err
-		}
-
-		var t Tensor
-		if err := binary.Read(rs, binary.LittleEndian, &t.Kind); err != nil {
-			return err
-		}
-
-		t.Shape = make([]uint64, dims)
-		for i := 0; uint32(i) < dims; i++ {
-			var shape32 uint32
-			if err := binary.Read(rs, binary.LittleEndian, &shape32); err != nil {
-				return err
-			}
-
-			t.Shape[i] = uint64(shape32)
-		}
-
-		// ggla tensor shape is reversed
-		// ref: https://github.com/ggerganov/llama.cpp/blob/29ae62d2ae163e2b68aa0ad3bf2ab4636de0c957/convert-lora-to-ggml.py#L44
-		slices.Reverse(t.Shape)
-
-		name := make([]byte, namesize)
-		if err := binary.Read(rs, binary.LittleEndian, &name); err != nil {
-			return err
-		}
-
-		t.Name = string(name)
-
-		offset, err := rs.Seek(0, io.SeekCurrent)
-		if err != nil {
-			return err
-		}
-
-		if _, err := rs.Seek((offset+31)&-32-offset, io.SeekCurrent); err != nil {
-			return err
-		}
-
-		offset, err = rs.Seek(0, io.SeekCurrent)
-		if err != nil {
-			return err
-		}
-
-		t.Offset = uint64(offset)
-
-		if _, err := rs.Seek(int64(t.Size()), io.SeekCurrent); err != nil {
-			return err
-		}
-
-		llm.tensors = append(llm.tensors, &t)
-	}
-}

llm/ggml_test.go

@@ -1 +0,0 @@
-package llm

llm/memory.go

@@ -11,18 +11,19 @@ import (
 	"github.com/ollama/ollama/discover"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 // This algorithm looks for a complete fit to determine if we need to unload other models
-func PredictServerFit(allGpus discover.GpuInfoList, ggml *GGML, adapters, projectors []string, opts api.Options) (bool, uint64) {
+func PredictServerFit(allGpus discover.GpuInfoList, f *ggml.GGML, adapters, projectors []string, opts api.Options) (bool, uint64) {
 	// Split up the GPUs by type and try them
 	var estimatedVRAM uint64
 	for _, gpus := range allGpus.ByLibrary() {
 		var layerCount int
-		estimate := EstimateGPULayers(gpus, ggml, projectors, opts)
+		estimate := EstimateGPULayers(gpus, f, projectors, opts)
 		layerCount, estimatedVRAM = estimate.Layers, estimate.VRAMSize
 		if opts.NumGPU < 0 {
-			if layerCount > 0 && layerCount >= int(ggml.KV().BlockCount()+1) {
+			if layerCount > 0 && layerCount >= int(f.KV().BlockCount()+1) {
 				return true, estimatedVRAM
 			}
 		} else {
@@ -70,7 +71,7 @@ type MemoryEstimate struct {
 
 // Given a model and one or more GPU targets, predict how many layers and bytes we can load, and the total size
 // The GPUs provided must all be the same Library
-func EstimateGPULayers(gpus []discover.GpuInfo, ggml *GGML, projectors []string, opts api.Options) MemoryEstimate {
+func EstimateGPULayers(gpus []discover.GpuInfo, f *ggml.GGML, projectors []string, opts api.Options) MemoryEstimate {
 	// Graph size for a partial offload, applies to all GPUs
 	var graphPartialOffload uint64
 
@@ -115,33 +116,31 @@ func EstimateGPULayers(gpus []discover.GpuInfo, ggml *GGML, projectors []string,
 		opts.NumCtx = max(opts.NumCtx, 2048)
 	}
 
-	layers := ggml.Tensors().Layers()
+	layers := f.Tensors().Layers()
 	// add one layer worth of memory as a buffer
 	if blk0, ok := layers["blk.0"]; ok {
-		layerSize = blk0.size()
+		layerSize = blk0.Size()
 	} else {
 		slog.Warn("model missing blk.0 layer size")
 	}
 
-	fa := envconfig.FlashAttention() &&
-		discover.GetGPUInfo().FlashAttentionSupported() &&
-		ggml.SupportsFlashAttention()
-
 	var kvct string
-	if fa {
+	if envconfig.FlashAttention() &&
+		discover.GetGPUInfo().FlashAttentionSupported() &&
+		f.SupportsFlashAttention() {
 		requested := strings.ToLower(envconfig.KvCacheType())
-		if requested != "" && ggml.SupportsKVCacheType(requested) {
+		if requested != "" && f.SupportsKVCacheType(requested) {
 			kvct = requested
 		}
 	}
 
-	kv, graphPartialOffload, graphFullOffload := ggml.GraphSize(uint64(opts.NumCtx), uint64(min(opts.NumCtx, opts.NumBatch)), kvct)
+	kv, graphPartialOffload, graphFullOffload := f.GraphSize(uint64(opts.NumCtx), uint64(min(opts.NumCtx, opts.NumBatch)), kvct)
 
 	// KV is proportional to the number of layers
-	layerSize += kv / ggml.KV().BlockCount()
+	layerSize += kv / f.KV().BlockCount()
 
 	if graphPartialOffload == 0 {
-		graphPartialOffload = ggml.KV().GQA() * kv / 6
+		graphPartialOffload = f.KV().GQA() * kv / 6
 	}
 	if graphFullOffload == 0 {
 		graphFullOffload = graphPartialOffload
@@ -156,12 +155,12 @@ func EstimateGPULayers(gpus []discover.GpuInfo, ggml *GGML, projectors []string,
 	}
 
 	if layer, ok := layers["output_norm"]; ok {
-		memoryLayerOutput += layer.size()
+		memoryLayerOutput += layer.Size()
 	}
 	if layer, ok := layers["output"]; ok {
-		memoryLayerOutput += layer.size()
+		memoryLayerOutput += layer.Size()
 	} else if layer, ok := layers["token_embd"]; ok {
-		memoryLayerOutput += layer.size()
+		memoryLayerOutput += layer.Size()
 	}
 
 	// Output layer handled at the end if we have space
@@ -211,11 +210,11 @@ func EstimateGPULayers(gpus []discover.GpuInfo, ggml *GGML, projectors []string,
 	}
 
 	// For all the layers, find where they can fit on the GPU(s)
-	for i := range int(ggml.KV().BlockCount()) {
+	for i := range int(f.KV().BlockCount()) {
 		// Some models have inconsistent layer sizes
 		if blk, ok := layers[fmt.Sprintf("blk.%d", i)]; ok {
-			layerSize = blk.size()
-			layerSize += kv / ggml.KV().BlockCount()
+			layerSize = blk.Size()
+			layerSize += kv / f.KV().BlockCount()
 		}
 		memoryWeights += layerSize
 
@@ -238,10 +237,10 @@ func EstimateGPULayers(gpus []discover.GpuInfo, ggml *GGML, projectors []string,
 			}
 		}
 	}
-	if layerCount >= int(ggml.KV().BlockCount()) {
+	if layerCount >= int(f.KV().BlockCount()) {
 		fullyLoaded = true
 	} else {
-		for i := layerCount; i < int(ggml.KV().BlockCount()); i++ {
+		for i := layerCount; i < int(f.KV().BlockCount()); i++ {
 			overflow += layerSize
 		}
 	}
@@ -259,7 +258,7 @@ func EstimateGPULayers(gpus []discover.GpuInfo, ggml *GGML, projectors []string,
 			}
 		}
 
-		if layerCount < int(ggml.KV().BlockCount())+1 {
+		if layerCount < int(f.KV().BlockCount())+1 {
 			fullyLoaded = false
 			overflow += memoryLayerOutput
 		}
@@ -311,7 +310,7 @@ func EstimateGPULayers(gpus []discover.GpuInfo, ggml *GGML, projectors []string,
 
 		inferenceLibrary:    gpus[0].Library,
 		layersRequested:     opts.NumGPU,
-		layersModel:         int(ggml.KV().BlockCount()) + 1,
+		layersModel:         int(f.KV().BlockCount()) + 1,
 		availableList:       availableList,
 		kv:                  kv,
 		allocationsList:     allocationsList,
@@ -339,22 +338,9 @@ func EstimateGPULayers(gpus []discover.GpuInfo, ggml *GGML, projectors []string,
 	return estimate
 }
 
-func (m MemoryEstimate) log() {
-	overhead := envconfig.GpuOverhead()
-
-	log := slog.With()
-	if m.projectorWeights > 0 {
-		log = log.With(
-			slog.Group(
-				"projector",
-				"weights", format.HumanBytes2(m.projectorWeights),
-				"graph", format.HumanBytes2(m.projectorGraph),
-			),
-		)
-	}
-
-	log.Info(
-		"offload to "+m.inferenceLibrary,
+func (m MemoryEstimate) LogValue() slog.Value {
+	attrs := []slog.Attr{
+		slog.String("library", m.inferenceLibrary),
 		slog.Group(
 			"layers",
 			// requested number of layers to offload
@@ -370,7 +356,7 @@ func (m MemoryEstimate) log() {
 			"memory",
 			// memory available by GPU for offloading
 			"available", m.availableList,
-			"gpu_overhead", format.HumanBytes2(overhead),
+			"gpu_overhead", format.HumanBytes2(envconfig.GpuOverhead()),
 			slog.Group(
 				"required",
 				// memory required for full offloading
@@ -399,7 +385,17 @@ func (m MemoryEstimate) log() {
 				"partial", format.HumanBytes2(m.graphPartialOffload),
 			),
 		),
-	)
+	}
+
+	if m.projectorWeights > 0 {
+		attrs = append(attrs, slog.Group(
+			"projector",
+			"weights", format.HumanBytes2(m.projectorWeights),
+			"graph", format.HumanBytes2(m.projectorGraph),
+		))
+	}
+
+	return slog.GroupValue(attrs...)
 }
 
 func projectorMemoryRequirements(filename string) (weights, graphSize uint64) {
@@ -409,13 +405,13 @@ func projectorMemoryRequirements(filename string) (weights, graphSize uint64) {
 	}
 	defer file.Close()
 
-	ggml, _, err := DecodeGGML(file, 0)
+	ggml, _, err := ggml.Decode(file, 0)
 	if err != nil {
 		return 0, 0
 	}
 
 	for _, layer := range ggml.Tensors().Layers() {
-		weights += layer.size()
+		weights += layer.Size()
 	}
 
 	switch arch := ggml.KV().Architecture(); arch {

llm/memory_test.go

@@ -11,6 +11,7 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/discover"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 func TestEstimateGPULayers(t *testing.T) {
@@ -23,7 +24,7 @@ func TestEstimateGPULayers(t *testing.T) {
 	defer f.Close()
 	inputLayerCount := 5
 
-	tensors := []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))},
@@ -32,7 +33,7 @@ func TestEstimateGPULayers(t *testing.T) {
 		{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
 	}
 	assert.Len(t, tensors, inputLayerCount+1)
-	err = WriteGGUF(f, KV{
+	err = ggml.WriteGGUF(f, ggml.KV{
 		"general.architecture":          "llama",
 		"llama.context_length":          uint32(32),
 		"llama.embedding_length":        uint32(4096),

llm/server.go

@@ -29,6 +29,7 @@ import (
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/llama"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type LlamaServer interface {
@@ -71,7 +72,7 @@ type llmServer struct {
 // It collects array values for arrays with a size less than or equal to
 // maxArraySize. If maxArraySize is 0, the default value of 1024 is used. If
 // the maxArraySize is negative, all arrays are collected.
-func LoadModel(model string, maxArraySize int) (*GGML, error) {
+func LoadModel(model string, maxArraySize int) (*ggml.GGML, error) {
 	if _, err := os.Stat(model); err != nil {
 		return nil, err
 	}
@@ -82,21 +83,17 @@ func LoadModel(model string, maxArraySize int) (*GGML, error) {
 	}
 	defer f.Close()
 
-	ggml, _, err := DecodeGGML(f, maxArraySize)
+	ggml, _, err := ggml.Decode(f, maxArraySize)
 	return ggml, err
 }
 
 // NewLlamaServer will run a server for the given GPUs
 // The gpu list must be a single family.
-func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapters, projectors []string, opts api.Options, numParallel int) (LlamaServer, error) {
-	var systemTotalMemory uint64
-	var systemFreeMemory uint64
-	var systemSwapFreeMemory uint64
-
+func NewLlamaServer(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapters, projectors []string, opts api.Options, numParallel int) (LlamaServer, error) {
 	systemInfo := discover.GetSystemInfo()
-	systemTotalMemory = systemInfo.System.TotalMemory
-	systemFreeMemory = systemInfo.System.FreeMemory
-	systemSwapFreeMemory = systemInfo.System.FreeSwap
+	systemTotalMemory := systemInfo.System.TotalMemory
+	systemFreeMemory := systemInfo.System.FreeMemory
+	systemSwapFreeMemory := systemInfo.System.FreeSwap
 	slog.Info("system memory", "total", format.HumanBytes2(systemTotalMemory), "free", format.HumanBytes2(systemFreeMemory), "free_swap", format.HumanBytes2(systemSwapFreeMemory))
 
 	// If the user wants zero GPU layers, reset the gpu list to be CPU/system ram info
@@ -104,7 +101,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 		gpus = discover.GetCPUInfo()
 	}
 
-	estimate := EstimateGPULayers(gpus, ggml, projectors, opts)
+	estimate := EstimateGPULayers(gpus, f, projectors, opts)
 	if len(gpus) > 1 || gpus[0].Library != "cpu" {
 		switch {
 		case gpus[0].Library == "metal" && estimate.VRAMSize > systemTotalMemory:
@@ -130,7 +127,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 		}
 	}
 
-	estimate.log()
+	slog.Info("offload", "", estimate)
 
 	params := []string{
 		"--model", model,
@@ -174,7 +171,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 		fa = false
 	}
 
-	if fa && !ggml.SupportsFlashAttention() {
+	if fa && !f.SupportsFlashAttention() {
 		slog.Warn("flash attention enabled but not supported by model")
 		fa = false
 	}
@@ -187,7 +184,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 
 		// Flash Attention also supports kv cache quantization
 		// Enable if the requested and kv cache type is supported by the model
-		if kvct != "" && ggml.SupportsKVCacheType(kvct) {
+		if kvct != "" && f.SupportsKVCacheType(kvct) {
 			params = append(params, "--kv-cache-type", kvct)
 		} else {
 			slog.Warn("kv cache type not supported by model", "type", kvct)
@@ -200,7 +197,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 	for _, g := range gpus {
 		if g.Library == "metal" &&
 			uint64(opts.NumGPU) > 0 &&
-			uint64(opts.NumGPU) < ggml.KV().BlockCount()+1 {
+			uint64(opts.NumGPU) < f.KV().BlockCount()+1 {
 			opts.UseMMap = new(bool)
 			*opts.UseMMap = false
 		}
@@ -335,7 +332,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 			estimate:    estimate,
 			numParallel: numParallel,
 			sem:         semaphore.NewWeighted(int64(numParallel)),
-			totalLayers: ggml.KV().BlockCount() + 1,
+			totalLayers: f.KV().BlockCount() + 1,
 			gpus:        gpus,
 			done:        make(chan error, 1),
 		}

ml/backend.go

@@ -0,0 +1,191 @@
+package ml
+
+import (
+	"bytes"
+	"encoding/binary"
+	"fmt"
+	"os"
+	"strings"
+)
+
+type Config interface {
+	Architecture() string
+	String(string, ...string) string
+	Uint(string, ...uint32) uint32
+	Float(string, ...float32) float32
+
+	Strings(string, ...[]string) []string
+	Uints(string, ...[]uint32) []uint32
+}
+
+type Backend interface {
+	Config() Config
+	Get(name string) Tensor
+	NewContext() Context
+}
+
+var backends = make(map[string]func(*os.File) (Backend, error))
+
+func RegisterBackend(name string, f func(*os.File) (Backend, error)) {
+	if _, ok := backends[name]; ok {
+		panic("backend: backend already registered")
+	}
+
+	backends[name] = f
+}
+
+func NewBackend(f *os.File) (Backend, error) {
+	if backend, ok := backends["ggml"]; ok {
+		return backend(f)
+	}
+
+	return nil, fmt.Errorf("unsupported backend")
+}
+
+type Context interface {
+	Zeros(dtype DType, shape ...int) Tensor
+	FromFloatSlice(s []float32, shape ...int) (Tensor, error)
+	FromIntSlice(s []int32, shape ...int) (Tensor, error)
+
+	Forward(Tensor)
+	Compute(Tensor) Tensor
+	Close() error
+}
+
+type Tensor interface {
+	Dim(n int) int64
+	Stride(n int) int64
+
+	Shape() []int64
+	DType() DType
+
+	Bytes() []byte
+	Floats() []float32
+
+	Add(ctx Context, t2 Tensor) Tensor
+	Mul(ctx Context, t2 Tensor) Tensor
+	Mulmat(ctx Context, t2 Tensor) Tensor
+
+	Softmax(ctx Context) Tensor
+	LayerNorm(ctx Context, weight, bias Tensor, eps float32) Tensor
+	RMSNorm(ctx Context, weight Tensor, eps float32) Tensor
+	Scale(ctx Context, s float64) Tensor
+
+	Conv2D(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
+	RoPE(ctx Context, positionIDs, ropeFactors Tensor, dim uint32, base, scale float32) Tensor
+
+	Tanh(ctx Context) Tensor
+	GELU(ctx Context) Tensor
+	SILU(ctx Context) Tensor
+
+	Reshape(ctx Context, shape ...int64) Tensor
+	View(ctx Context, offset int, shape ...int) Tensor
+	Permute(ctx Context, shape ...int) Tensor
+	Contiguous(ctx Context) Tensor
+
+	Pad(ctx Context, shape ...int64) Tensor
+	Unpad(ctx Context, shape ...int64) Tensor
+
+	Stack(ctx Context, dim int, s ...Tensor) Tensor
+	Concat(ctx Context, t2 Tensor, dim int) Tensor
+	Rows(ctx Context, t2 Tensor) Tensor
+	Copy(ctx Context, t2 Tensor) Tensor
+}
+
+type number interface {
+	~int | ~int8 | ~int16 | ~int32 | ~int64 |
+		~uint | ~uint8 | ~uint16 | ~uint32 | ~uint64 |
+		~float32 | ~float64 |
+		~complex64 | ~complex128
+}
+
+func mul[T number](s ...T) T {
+	p := T(1)
+	for _, v := range s {
+		p *= v
+	}
+
+	return p
+}
+
+type DumpOptions struct {
+	// Items is the number of elements to print at the beginning and end of each dimension.
+	Items int64
+
+	// Precision is the number of decimal places to print. Applies to float32 and float64.
+	Precision int
+}
+
+func Dump(t Tensor, opts ...DumpOptions) string {
+	if len(opts) < 1 {
+		opts = append(opts, DumpOptions{
+			Items:     3,
+			Precision: 4,
+		})
+	}
+
+	switch t.DType() {
+	case DTypeF32:
+		return dump[[]float32](t, opts[0])
+	case DTypeI32:
+		return dump[[]int32](t, opts[0])
+	default:
+		return "<unsupported>"
+	}
+}
+
+func dump[S ~[]E, E number](t Tensor, opts DumpOptions) string {
+	bts := t.Bytes()
+	if bts == nil {
+		return "<nil>"
+	}
+
+	s := make(S, mul(t.Shape()...))
+	if err := binary.Read(bytes.NewBuffer(t.Bytes()), binary.LittleEndian, &s); err != nil {
+		panic(err)
+	}
+
+	shape := t.Shape()
+
+	var sb strings.Builder
+	var f func([]int64, int64)
+	f = func(dims []int64, stride int64) {
+		prefix := strings.Repeat(" ", len(shape)-len(dims)+1)
+		fmt.Fprint(&sb, "[")
+		defer func() { fmt.Fprint(&sb, "]") }()
+		for i := int64(0); i < dims[0]; i++ {
+			if i >= opts.Items && i < dims[0]-opts.Items {
+				fmt.Fprint(&sb, "..., ")
+				// skip to next printable element
+				skip := dims[0] - 2*opts.Items
+				if len(dims) > 1 {
+					stride += mul(append(dims[1:], skip)...)
+					fmt.Fprint(&sb, strings.Repeat("\n", len(dims)-1), prefix)
+				}
+				i += skip - 1
+			} else if len(dims) > 1 {
+				f(dims[1:], stride)
+				stride += mul(dims[1:]...)
+				if i < dims[0]-1 {
+					fmt.Fprint(&sb, ",", strings.Repeat("\n", len(dims)-1), prefix)
+				}
+			} else {
+				fmt.Fprint(&sb, s[stride+i])
+				if i < dims[0]-1 {
+					fmt.Fprint(&sb, ", ")
+				}
+			}
+		}
+	}
+	f(shape, 0)
+
+	return sb.String()
+}
+
+type DType int
+
+const (
+	DTypeF32 DType = iota
+	DTypeI32
+	DTypeOther
+)

ml/backend/backend.go

@@ -0,0 +1,5 @@
+package backend
+
+import (
+	_ "github.com/ollama/ollama/ml/backend/ggml"
+)

ml/backend/ggml/ggml.go

@@ -0,0 +1,580 @@
+package ggml
+
+// #cgo CPPFLAGS: -I${SRCDIR}/ggml/include
+// #include <stdlib.h>
+// #include <stdint.h>
+// #include "ggml.h"
+// #include "ggml-cpu.h"
+// #include "ggml-backend.h"
+import "C"
+
+import (
+	"bytes"
+	"encoding/binary"
+	"fmt"
+	"io"
+	"log/slog"
+	"os"
+	"sync"
+	"unsafe"
+
+	"github.com/ollama/ollama/format"
+	fs "github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/ml"
+	"golang.org/x/sync/errgroup"
+
+	"github.com/ollama/ollama/ml/backend/ggml/ggml/src"
+)
+
+type device struct {
+	d *C.struct_ggml_backend_device
+}
+
+func (d device) LogValue() slog.Value {
+	var free, total uint64
+	C.ggml_backend_dev_memory(d.d, (*C.size_t)(&free), (*C.size_t)(&total))
+
+	kind := "unknown"
+	switch C.ggml_backend_dev_type(d.d) {
+	case C.GGML_BACKEND_DEVICE_TYPE_CPU:
+		kind = "cpu"
+	case C.GGML_BACKEND_DEVICE_TYPE_GPU:
+		kind = "gpu"
+	case C.GGML_BACKEND_DEVICE_TYPE_ACCEL:
+		kind = "accel"
+	}
+
+	return slog.GroupValue(
+		slog.String("name", C.GoString(C.ggml_backend_dev_name(d.d))),
+		slog.String("description", C.GoString(C.ggml_backend_dev_description(d.d))),
+		slog.String("kind", kind),
+		slog.String("free", format.HumanBytes2(free)),
+		slog.String("total", format.HumanBytes2(total)),
+	)
+}
+
+var devices = sync.OnceValue(func() []device {
+	ggml.OnceLoad()
+
+	s := make([]device, C.ggml_backend_dev_count())
+	for i := range s {
+		s[i] = device{C.ggml_backend_dev_get(C.size_t(i))}
+	}
+
+	return s
+})
+
+type Backend struct {
+	meta       *fs.GGML
+	cpus, gpus []Context
+	tensors    map[string]*Context
+}
+
+func New(r *os.File) (ml.Backend, error) {
+	meta, n, err := fs.Decode(r, -1)
+	if err != nil {
+		return nil, err
+	}
+
+	slog.Info(
+		"",
+		"architecture", meta.KV().Architecture(),
+		"file_type", meta.KV().FileType(),
+		"name", meta.KV().String("general.name"),
+		"description", meta.KV().String("general.description"),
+		"num_tensors", len(meta.Tensors().Items()),
+		"num_key_values", len(meta.KV()),
+	)
+
+	var cpus, gpus []Context
+	for _, d := range devices() {
+		switch C.ggml_backend_dev_type(d.d) {
+		case C.GGML_BACKEND_DEVICE_TYPE_CPU,
+			C.GGML_BACKEND_DEVICE_TYPE_ACCEL:
+			slog.Info("cpu", "device", d)
+			cpus = append(cpus, Context{
+				ctx: C.ggml_init(C.struct_ggml_init_params{
+					mem_size: C.size_t(int(C.ggml_tensor_overhead()) * (len(meta.Tensors().Items()) + 1 + int(meta.KV().BlockCount())*2)),
+					no_alloc: true,
+				}),
+				backend: C.ggml_backend_dev_init(d.d, nil),
+			})
+		case C.GGML_BACKEND_DEVICE_TYPE_GPU:
+			slog.Info("gpu", "device", d)
+			gpus = append(gpus, Context{
+				ctx: C.ggml_init(C.struct_ggml_init_params{
+					mem_size: C.size_t(int(C.ggml_tensor_overhead()) * (len(meta.Tensors().Items()) + 1 + int(meta.KV().BlockCount())*2)),
+					no_alloc: true,
+				}),
+				backend: C.ggml_backend_dev_init(d.d, nil),
+			})
+		}
+	}
+
+	ctxFunc := func(s []Context) (*Context, error) {
+		for _, e := range s {
+			return &e, nil
+		}
+
+		return nil, fmt.Errorf("no devices available")
+	}
+
+	tensors := make(map[*fs.Tensor]*Context, len(meta.Tensors().Items()))
+	for _, t := range meta.Tensors().Items() {
+		c, err := ctxFunc(append(gpus, cpus...))
+		if err != nil {
+			return nil, err
+		}
+
+		func() {
+			tt := C.ggml_new_tensor(c.ctx, t.Kind, C.int(len(t.Shape)), (*C.int64_t)(unsafe.Pointer(&t.Shape[0])))
+
+			cname := C.CString(t.Name)
+			defer C.free(unsafe.Pointer(cname))
+			C.ggml_set_name(tt, cname)
+
+			tensors[t] = c
+		}()
+	}
+
+	for _, b := range append(gpus, cpus...) {
+		C.ggml_backend_alloc_ctx_tensors(b.ctx, b.backend)
+	}
+
+	sr := io.NewSectionReader(r, int64(meta.Tensors().Offset), n-int64(meta.Tensors().Offset))
+
+	var g errgroup.Group
+	for t, c := range tensors {
+		g.Go(func() error {
+			bts := make([]byte, t.Size())
+			n, err := io.ReadFull(io.NewSectionReader(sr, int64(t.Offset), int64(t.Size())), bts)
+			if err != nil {
+				return err
+			}
+
+			if n != int(t.Size()) {
+				return fmt.Errorf("expected %d bytes, got %d", t.Size(), n)
+			}
+
+			cname := C.CString(t.Name)
+			defer C.free(unsafe.Pointer(cname))
+
+			C.ggml_backend_tensor_set(C.ggml_get_tensor(c.ctx, cname), unsafe.Pointer(&bts[0]), 0, C.size_t(n))
+			return nil
+		})
+	}
+
+	if err := g.Wait(); err != nil {
+		return nil, err
+	}
+
+	return &Backend{
+		meta: meta,
+		cpus: cpus,
+		gpus: gpus,
+	}, nil
+}
+
+func init() {
+	ml.RegisterBackend("ggml", New)
+}
+
+func (b *Backend) Config() ml.Config {
+	return b.meta.KV()
+}
+
+func (b *Backend) Get(name string) ml.Tensor {
+	cname := C.CString(name)
+	defer C.free(unsafe.Pointer(cname))
+
+	for _, c := range append(b.gpus, b.cpus...) {
+		if t := C.ggml_get_tensor(c.ctx, cname); t != nil {
+			return &Tensor{t: t}
+		}
+	}
+
+	return nil
+}
+
+func (b *Backend) NewContext() ml.Context {
+	nodes := max(8192, len(b.meta.Tensors().Items())*5)
+	bts := make([]byte, C.size_t(nodes)*C.ggml_tensor_overhead()+C.ggml_graph_overhead_custom(C.size_t(nodes), false))
+	c := C.ggml_init(C.struct_ggml_init_params{
+		mem_buffer: unsafe.Pointer(&bts[0]),
+		mem_size:   C.size_t(len(bts)),
+		no_alloc:   true,
+	})
+
+	backends := make([]*C.struct_ggml_backend, len(b.gpus)+len(b.cpus))
+	bufts := make([]*C.struct_ggml_backend_buffer_type, len(b.gpus)+len(b.cpus))
+	for i, c := range append(b.gpus, b.cpus...) {
+		backends[i] = c.backend
+		bufts[i] = C.ggml_backend_get_default_buffer_type(c.backend)
+	}
+
+	return &Context{
+		ctx:     c,
+		backend: backends[0],
+		nodes:   nodes,
+		sched: C.ggml_backend_sched_new(
+			(*C.ggml_backend_t)(unsafe.Pointer(&backends[0])),
+			(*C.ggml_backend_buffer_type_t)(unsafe.Pointer(&bufts[0])),
+			C.int(len(backends)),
+			C.size_t(nodes),
+			true,
+		),
+	}
+}
+
+type Context struct {
+	ctx     *C.struct_ggml_context
+	backend *C.struct_ggml_backend
+
+	sched *C.struct_ggml_backend_sched
+	graph *C.struct_ggml_cgraph
+	nodes int
+}
+
+func (c *Context) Forward(t ml.Tensor) {
+	if c.graph == nil {
+		c.graph = C.ggml_new_graph_custom(c.ctx, C.size_t(c.nodes), false)
+	}
+
+	C.ggml_build_forward_expand(c.graph, t.(*Tensor).t)
+}
+
+func (c *Context) Compute(t ml.Tensor) ml.Tensor {
+	c.Forward(t)
+	C.ggml_backend_sched_graph_compute_async(c.sched, c.graph)
+
+	backend := C.ggml_backend_sched_get_tensor_backend(c.sched, t.(*Tensor).t)
+
+	t.(*Tensor).data = make([]byte, C.ggml_nbytes(t.(*Tensor).t))
+	C.ggml_backend_tensor_get_async(backend, t.(*Tensor).t, unsafe.Pointer(&t.(*Tensor).data[0]), 0, C.ggml_nbytes(t.(*Tensor).t))
+	return t
+}
+
+func (c Context) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
+	if len(shape) < 1 || len(shape) > 4 {
+		panic("unsupported number of dimensions")
+	}
+
+	for _, dim := range shape {
+		if dim < 1 {
+			panic("invalid shape")
+		}
+	}
+
+	var t *C.struct_ggml_tensor
+	switch dtype {
+	case ml.DTypeF32:
+		t = C.ggml_new_tensor(c.ctx, C.GGML_TYPE_F32, C.int(len(shape)), (*C.int64_t)(unsafe.Pointer(&shape[0])))
+	case ml.DTypeI32:
+		t = C.ggml_new_tensor(c.ctx, C.GGML_TYPE_I32, C.int(len(shape)), (*C.int64_t)(unsafe.Pointer(&shape[0])))
+	default:
+		panic("unsupported dtype")
+	}
+
+	b := C.ggml_backend_alloc_buffer(c.backend, C.ggml_nbytes(t))
+	C.ggml_backend_tensor_alloc(b, t, C.ggml_backend_buffer_get_base(b))
+	C.ggml_set_zero(t)
+	return &Tensor{t: t}
+}
+
+func fromSlice[S ~[]E, E float32 | int32](ctx Context, s S, shape []int, dtype uint32) (ml.Tensor, error) {
+	n := len(s)
+	for _, v := range shape {
+		n /= v
+	}
+
+	if n != 1 {
+		return nil, fmt.Errorf("invalid shape %v for %d elements", shape, len(s))
+	}
+
+	t := C.ggml_new_tensor(ctx.ctx, dtype, C.int(len(shape)), (*C.int64_t)(unsafe.Pointer(&shape[0])))
+	b := C.ggml_backend_alloc_buffer(ctx.backend, C.ggml_nbytes(t))
+	C.ggml_backend_tensor_alloc(b, t, C.ggml_backend_buffer_get_base(b))
+	C.ggml_backend_tensor_set(t, unsafe.Pointer(&s[0]), 0, C.ggml_nbytes(t))
+	return &Tensor{t: t}, nil
+}
+
+func (c Context) FromFloatSlice(s []float32, shape ...int) (ml.Tensor, error) {
+	return fromSlice(c, s, shape, C.GGML_TYPE_F32)
+}
+
+func (c Context) FromIntSlice(s []int32, shape ...int) (ml.Tensor, error) {
+	return fromSlice(c, s, shape, C.GGML_TYPE_I32)
+}
+
+func (c *Context) Close() error {
+	C.ggml_backend_sched_free(c.sched)
+	C.ggml_free(c.ctx)
+	return nil
+}
+
+type Tensor struct {
+	t    *C.struct_ggml_tensor
+	data []byte
+}
+
+func (t *Tensor) LogValue() slog.Value {
+	return slog.GroupValue(
+		slog.String("name", C.GoString(C.ggml_get_name(t.t))),
+		slog.String("type", C.GoString(C.ggml_type_name(t.t._type))),
+		slog.Any("shape", t.Shape()),
+	)
+}
+
+func (t *Tensor) Dim(n int) int64 {
+	return int64(t.t.ne[n])
+}
+
+func (t *Tensor) Stride(n int) int64 {
+	return int64(t.t.nb[n])
+}
+
+func (t *Tensor) Shape() []int64 {
+	shape := make([]int64, C.ggml_n_dims(t.t))
+	for i := range shape {
+		shape[i] = t.Dim(i)
+	}
+
+	return shape
+}
+
+func (t *Tensor) Bytes() []byte {
+	if bts := C.ggml_get_data(t.t); bts != nil {
+		return C.GoBytes(bts, C.int(C.ggml_nbytes(t.t)))
+	}
+
+	return nil
+}
+
+func (t *Tensor) Floats() (f32s []float32) {
+	if t.data != nil {
+		f32s = make([]float32, C.ggml_nelements(t.t))
+		_ = binary.Read(bytes.NewReader(t.data), binary.LittleEndian, f32s)
+	}
+
+	return
+}
+
+func (t *Tensor) DType() ml.DType {
+	switch t.t._type {
+	case C.GGML_TYPE_F32:
+		return ml.DTypeF32
+	case C.GGML_TYPE_I32:
+		return ml.DTypeI32
+	default:
+		return ml.DTypeOther
+	}
+}
+
+func (t *Tensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_add(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
+	}
+}
+
+func (t *Tensor) Stack(ctx ml.Context, dim int, s ...ml.Tensor) ml.Tensor {
+	if len(s) > 0 {
+		return t.Concat(ctx, s[0].Stack(ctx, dim, s[1:]...), dim)
+	}
+
+	return t
+}
+
+func (t *Tensor) Concat(ctx ml.Context, t2 ml.Tensor, dim int) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_concat(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.int(dim)),
+	}
+}
+
+func (t *Tensor) Contiguous(ctx ml.Context) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_cont(ctx.(*Context).ctx, t.t),
+	}
+}
+
+func (t *Tensor) Mul(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_mul(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
+	}
+}
+
+func (t *Tensor) Mulmat(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_mul_mat(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
+	}
+}
+
+func (t *Tensor) LayerNorm(ctx ml.Context, w, b ml.Tensor, eps float32) ml.Tensor {
+	tt := (&Tensor{t: C.ggml_norm(ctx.(*Context).ctx, t.t, C.float(eps))}).Mul(ctx, w)
+	if b != nil {
+		tt = tt.Add(ctx, b)
+	}
+
+	return tt
+}
+
+func (t *Tensor) RMSNorm(ctx ml.Context, w ml.Tensor, eps float32) ml.Tensor {
+	return (&Tensor{t: C.ggml_norm(ctx.(*Context).ctx, t.t, C.float(eps))}).Mul(ctx, w)
+}
+
+func (t *Tensor) Pad(ctx ml.Context, shape ...int64) ml.Tensor {
+	if len(shape) != 4 {
+		panic("expected 4 dimensions")
+	}
+
+	return &Tensor{
+		t: C.ggml_pad(ctx.(*Context).ctx, t.t, C.int(shape[0]), C.int(shape[1]), C.int(shape[2]), C.int(shape[3])),
+	}
+}
+
+func (t *Tensor) Permute(ctx ml.Context, shape ...int) ml.Tensor {
+	if len(shape) != 4 {
+		panic("expected 4 dimensions")
+	}
+
+	return &Tensor{
+		t: C.ggml_permute(ctx.(*Context).ctx, t.t, C.int(shape[0]), C.int(shape[1]), C.int(shape[2]), C.int(shape[3])),
+	}
+}
+
+func (t *Tensor) Rows(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_get_rows(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
+	}
+}
+
+func (t *Tensor) Copy(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_cpy(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
+	}
+}
+
+func (t *Tensor) Reshape(ctx ml.Context, shape ...int64) ml.Tensor {
+	switch len(shape) {
+	case 1:
+		return &Tensor{
+			t: C.ggml_reshape_1d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0])),
+		}
+	case 2:
+		return &Tensor{
+			t: C.ggml_reshape_2d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1])),
+		}
+	case 3:
+		return &Tensor{
+			t: C.ggml_reshape_3d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1]), C.int64_t(shape[2])),
+		}
+	case 4:
+		return &Tensor{
+			t: C.ggml_reshape_4d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.int64_t(shape[1]), C.int64_t(shape[2]), C.int64_t(shape[3])),
+		}
+	default:
+		panic("unsupported number of dimensions")
+	}
+}
+
+func (t *Tensor) Scale(ctx ml.Context, s float64) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_scale(ctx.(*Context).ctx, t.t, (C.float)(s)),
+	}
+}
+
+func (t *Tensor) Softmax(ctx ml.Context) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_soft_max(ctx.(*Context).ctx, t.t),
+	}
+}
+
+func (t *Tensor) Tanh(ctx ml.Context) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_tanh_inplace(ctx.(*Context).ctx, t.t),
+	}
+}
+
+func (t *Tensor) Unpad(ctx ml.Context, shape ...int64) ml.Tensor {
+	if len(shape) != 4 {
+		panic("expected 4 dimensions")
+	}
+
+	return &Tensor{
+		t: C.ggml_unpad(ctx.(*Context).ctx, t.t, C.int(shape[0]), C.int(shape[1]), C.int(shape[2]), C.int(shape[3])),
+	}
+}
+
+func (t *Tensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
+	switch len(shape) {
+	case 1:
+		return &Tensor{
+			t: C.ggml_view_1d(ctx.(*Context).ctx, t.t, C.int64_t(shape[0]), C.size_t(offset)),
+		}
+	case 3:
+		return &Tensor{
+			t: C.ggml_view_2d(ctx.(*Context).ctx, t.t,
+				C.int64_t(shape[0]), C.int64_t(shape[2]),
+				C.size_t(shape[1]),
+				C.size_t(offset)),
+		}
+	case 5:
+		return &Tensor{
+			t: C.ggml_view_3d(ctx.(*Context).ctx, t.t,
+				C.int64_t(shape[0]), C.int64_t(shape[2]), C.int64_t(shape[4]),
+				C.size_t(shape[1]), C.size_t(shape[3]),
+				C.size_t(offset)),
+		}
+	case 7:
+		return &Tensor{
+			t: C.ggml_view_4d(ctx.(*Context).ctx, t.t,
+				C.int64_t(shape[0]), C.int64_t(shape[2]), C.int64_t(shape[4]), C.int64_t(shape[6]),
+				C.size_t(shape[1]), C.size_t(shape[3]), C.size_t(shape[5]),
+				C.size_t(offset)),
+		}
+	default:
+		panic("unsupported number of dimensions")
+	}
+}
+
+const (
+	ropeTypeNorm C.int = iota
+)
+
+func (t *Tensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, ropeDim uint32, ropeBase, ropeScale float32) ml.Tensor {
+	if ropeFactors == nil {
+		ropeFactors = &Tensor{}
+	}
+
+	return &Tensor{
+		t: C.ggml_rope_ext(
+			ctx.(*Context).ctx, t.t, positionIDs.(*Tensor).t, ropeFactors.(*Tensor).t,
+			C.int(ropeDim),
+			131072,       // YaRN n_ctx_train
+			ropeTypeNorm, // ROPE_TYPE_NORM
+			C.float(ropeBase),
+			C.float(ropeScale),
+			0.,  // YaRN ext_factor
+			1.,  // YaRN attn_factor
+			32., // YaRN beta_fast
+			1.,  // YaRN beta_slow
+		),
+	}
+}
+
+func (t *Tensor) GELU(ctx ml.Context) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_gelu_inplace(ctx.(*Context).ctx, t.t),
+	}
+}
+
+func (t *Tensor) SILU(ctx ml.Context) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_silu_inplace(ctx.(*Context).ctx, t.t),
+	}
+}
+
+func (t *Tensor) Conv2D(ctx ml.Context, t2 ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
+	return &Tensor{
+		t: C.ggml_conv_2d(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.int(s0), C.int(s1), C.int(p0), C.int(p1), C.int(d0), C.int(d1)),
+	}
+}

ml/nn/convolution.go

@@ -0,0 +1,11 @@
+package nn
+
+import "github.com/ollama/ollama/ml"
+
+type Conv2D struct {
+	Weight ml.Tensor `gguf:"weight"`
+}
+
+func (m *Conv2D) Forward(ctx ml.Context, t ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
+	return m.Weight.Conv2D(ctx, t, s0, s1, p0, p1, d0, d1)
+}

ml/nn/embedding.go

@@ -0,0 +1,11 @@
+package nn
+
+import "github.com/ollama/ollama/ml"
+
+type Embedding struct {
+	Weight ml.Tensor `gguf:"weight"`
+}
+
+func (m *Embedding) Forward(ctx ml.Context, hiddenState ml.Tensor) ml.Tensor {
+	return m.Weight.Rows(ctx, hiddenState)
+}

ml/nn/linear.go

@@ -0,0 +1,17 @@
+package nn
+
+import "github.com/ollama/ollama/ml"
+
+type Linear struct {
+	Weight ml.Tensor `gguf:"weight"`
+	Bias   ml.Tensor `gguf:"bias"`
+}
+
+func (m *Linear) Forward(ctx ml.Context, t ml.Tensor) ml.Tensor {
+	t = m.Weight.Mulmat(ctx, t)
+	if m.Bias != nil {
+		t = t.Add(ctx, m.Bias)
+	}
+
+	return t
+}

ml/nn/normalization.go

@@ -0,0 +1,22 @@
+package nn
+
+import (
+	"github.com/ollama/ollama/ml"
+)
+
+type LayerNorm struct {
+	Weight ml.Tensor `gguf:"weight"`
+	Bias   ml.Tensor `gguf:"bias"`
+}
+
+func (m *LayerNorm) Forward(ctx ml.Context, t ml.Tensor, eps float32) ml.Tensor {
+	return t.LayerNorm(ctx, m.Weight, m.Bias, eps)
+}
+
+type RMSNorm struct {
+	Weight ml.Tensor `gguf:"weight"`
+}
+
+func (m *RMSNorm) Forward(ctx ml.Context, t ml.Tensor, eps float32) ml.Tensor {
+	return t.RMSNorm(ctx, m.Weight, eps)
+}

model/llama/model.go

@@ -0,0 +1,155 @@
+package llama
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+)
+
+type Options struct {
+	RopeFactors                      ml.Tensor `gguf:"rope_freqs.weight"`
+	hiddenSize, numHeads, numKVHeads int64
+	eps, ropeBase, ropeScale         float32
+	ropeDim                          uint32
+}
+
+type Model struct {
+	model.Base
+	model.BytePairEncoding
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Layers         []Layer       `gguf:"blk"`
+	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
+	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
+
+	*Options
+}
+
+func New(c ml.Config) (model.Model, error) {
+	return &Model{
+		BytePairEncoding: model.BytePairEncoding{
+			Pretokenizer: c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
+			Vocabulary: &model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Types:  c.Uints("tokenizer.ggml.token_type"),
+				Merges: c.Strings("tokenizer.ggml.merges"),
+				BOS:    c.Uint("tokenizer.ggml.bos_token_id"),
+				EOS:    c.Uint("tokenizer.ggml.eos_token_id"),
+			},
+		},
+		Layers: make([]Layer, c.Uint("block_count")),
+		Options: &Options{
+			hiddenSize: int64(c.Uint("embedding_length")),
+			numHeads:   int64(c.Uint("attention.head_count")),
+			numKVHeads: int64(c.Uint("attention.head_count_kv")),
+			eps:        c.Float("attention.layer_norm_rms_epsilon"),
+			ropeBase:   c.Float("rope.freq_base"),
+			ropeScale:  c.Float("rope.freq_scale", 1),
+			ropeDim:    c.Uint("rope.dimension_count"),
+		},
+	}, nil
+}
+
+type SelfAttention struct {
+	Query  *nn.Linear `gguf:"attn_q"`
+	Key    *nn.Linear `gguf:"attn_k"`
+	Value  *nn.Linear `gguf:"attn_v"`
+	Output *nn.Linear `gguf:"attn_output"`
+}
+
+func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache model.Cache, opts *Options) ml.Tensor {
+	batchSize := hiddenState.Dim(1)
+	headDim := opts.hiddenSize / opts.numHeads
+
+	q := sa.Query.Forward(ctx, hiddenState)
+	q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
+	q = q.RoPE(ctx, positionIDs, opts.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+
+	k := sa.Key.Forward(ctx, hiddenState)
+	k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
+	k = k.RoPE(ctx, positionIDs, opts.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+
+	v := sa.Value.Forward(ctx, hiddenState)
+	v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
+
+	k, v = cache.Put(ctx, k, v, cache.Options)
+
+	q = q.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	k = k.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	v = v.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+
+	kq := k.Mulmat(ctx, q)
+	kq = kq.Scale(ctx, 1.0/math.Sqrt(float64(headDim)))
+	kq = kq.Softmax(ctx)
+
+	kqv := v.Mulmat(ctx, kq)
+	kqv = kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	kqv = kqv.Reshape(ctx, opts.hiddenSize, batchSize)
+
+	return sa.Output.Forward(ctx, kqv)
+}
+
+type MLP struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+	Gate *nn.Linear `gguf:"ffn_gate"`
+}
+
+func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	return mlp.Down.Forward(ctx, hiddenState)
+}
+
+type Layer struct {
+	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
+	SelfAttention *SelfAttention
+	MLPNorm       *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP           *MLP
+}
+
+func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache model.Cache, opts *Options) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.SelfAttention.Forward(ctx, hiddenState, positionIDs, cache, opts)
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
+	return hiddenState.Add(ctx, residual)
+}
+
+func (m *Model) Forward(ctx ml.Context, opts model.Options) (ml.Tensor, error) {
+	inputs, err := ctx.FromIntSlice(opts.Inputs(), len(opts.Inputs()))
+	if err != nil {
+		return nil, err
+	}
+
+	positions, err := ctx.FromIntSlice(opts.Positions(), len(opts.Positions()))
+	if err != nil {
+		return nil, err
+	}
+
+	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
+
+	for i, layer := range m.Layers {
+		hiddenState = layer.Forward(ctx, hiddenState, positions, opts.Cache.Sub(i), m.Options)
+	}
+
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	hiddenState = m.Output.Forward(ctx, hiddenState)
+
+	outputs, err := ctx.FromIntSlice([]int32{int32(len(opts.Positions())) - 1}, 1)
+	if err != nil {
+		return nil, err
+	}
+
+	return hiddenState.Rows(ctx, outputs), nil
+}
+
+func init() {
+	model.Register("llama", New)
+}

model/mllama/model.go

@@ -0,0 +1,90 @@
+package mllama
+
+import (
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+)
+
+type Model struct {
+	model.Base
+
+	*VisionModel `gguf:"v,vision"`
+	*TextModel
+
+	Projector *nn.Linear `gguf:"mm.0"`
+
+	ImageProcessor
+	TextProcessor
+}
+
+func New(c ml.Config) (model.Model, error) {
+	return &Model{
+		ImageProcessor: newImageProcessor(c),
+		VisionModel:    newVisionModel(c),
+		TextProcessor:  newTextProcessor(c),
+		TextModel:      newTextModel(c),
+	}, nil
+}
+
+func (m *Model) Forward(ctx ml.Context, opts model.Options) (ml.Tensor, error) {
+	var crossAttentionStates ml.Tensor
+	if opts.Images != nil {
+		f32s, aspectRatioID, err := m.ImageProcessor.ProcessImage(opts.Images[0])
+		if err != nil {
+			return nil, err
+		}
+
+		pixelValues, err := ctx.FromFloatSlice(f32s,
+			m.ImageProcessor.imageSize,
+			m.ImageProcessor.imageSize,
+			m.ImageProcessor.numChannels,
+			m.ImageProcessor.maxNumTiles,
+		)
+		if err != nil {
+			return nil, err
+		}
+
+		aspectRatio, err := ctx.FromIntSlice([]int32{int32(aspectRatioID)}, 1)
+		if err != nil {
+			return nil, err
+		}
+
+		positions := make([]int32, 1601)
+		for i := range positions {
+			positions[i] = int32(i)
+		}
+
+		positionIDs, err := ctx.FromIntSlice(positions, len(positions))
+		if err != nil {
+			return nil, err
+		}
+
+		crossAttentionStates = m.VisionModel.Forward(ctx, pixelValues, positionIDs, aspectRatio)
+		crossAttentionStates = m.Projector.Forward(ctx, crossAttentionStates)
+	}
+
+	inputs, err := ctx.FromIntSlice(opts.Inputs(), len(opts.Inputs()))
+	if err != nil {
+		return nil, err
+	}
+
+	positions, err := ctx.FromIntSlice(opts.Positions(), len(opts.Positions()))
+	if err != nil {
+		return nil, err
+	}
+
+	// TODO: attention mask, cross attention mask
+	hiddenState := m.TextModel.Forward(ctx, inputs, positions, nil, crossAttentionStates, nil, opts.Cache)
+
+	outputs, err := ctx.FromIntSlice([]int32{int32(len(opts.Positions())) - 1}, 1)
+	if err != nil {
+		return nil, err
+	}
+
+	return hiddenState.Rows(ctx, outputs), nil
+}
+
+func init() {
+	model.Register("mllama", New)
+}

model/mllama/model_text.go

@@ -0,0 +1,225 @@
+package mllama
+
+import (
+	"math"
+	"slices"
+
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+)
+
+type TextSelfAttention struct {
+	Query  *nn.Linear `gguf:"attn_q"`
+	Key    *nn.Linear `gguf:"attn_k"`
+	Value  *nn.Linear `gguf:"attn_v"`
+	Output *nn.Linear `gguf:"attn_output"`
+}
+
+func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, mask ml.Tensor, cache model.Cache, opts *TextModelOptions) ml.Tensor {
+	batchSize := hiddenState.Dim(1)
+	headDim := opts.hiddenSize / opts.numHeads
+
+	query := sa.Query.Forward(ctx, hiddenState)
+	query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)
+	query = query.RoPE(ctx, positions, opts.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+
+	key := sa.Key.Forward(ctx, hiddenState)
+	key = key.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
+	key = key.RoPE(ctx, positions, opts.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+
+	value := sa.Value.Forward(ctx, hiddenState)
+	value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
+
+	key, value = cache.Put(ctx, key, value, cache.Options)
+
+	query = query.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	key = key.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+
+	scores := key.Mulmat(ctx, query)
+	scores = scores.Scale(ctx, 1.0/math.Sqrt(float64(headDim)))
+
+	if mask != nil {
+		scores = scores.Add(ctx, mask)
+	}
+
+	scores = scores.Softmax(ctx)
+
+	attention := value.Mulmat(ctx, scores)
+	attention = attention.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	attention = attention.Reshape(ctx, opts.hiddenSize, batchSize)
+
+	return sa.Output.Forward(ctx, attention)
+}
+
+type TextMLP struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+	Gate *nn.Linear `gguf:"ffn_gate"`
+}
+
+func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextModelOptions) ml.Tensor {
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	return mlp.Down.Forward(ctx, hiddenState)
+}
+
+type TextSelfAttentionDecoderLayer struct {
+	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
+	SelfAttention *TextSelfAttention
+
+	MLPNorm *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP     *TextMLP
+}
+
+func (d *TextSelfAttentionDecoderLayer) Forward(ctx ml.Context, hiddenState, positions, mask, _, _ ml.Tensor, cache model.Cache, opts *TextModelOptions) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = d.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = d.SelfAttention.Forward(ctx, hiddenState, positions, mask, cache, opts)
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = d.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = d.MLP.Forward(ctx, hiddenState, opts)
+	return hiddenState.Add(ctx, residual)
+}
+
+type TextCrossAttention struct {
+	QueryNorm *nn.RMSNorm `gguf:"cross_attn_q_norm"`
+	Query     *nn.Linear  `gguf:"cross_attn_q_proj"`
+	KeyNorm   *nn.RMSNorm `gguf:"cross_attn_k_norm"`
+	Key       *nn.Linear  `gguf:"cross_attn_k_proj"`
+	Value     *nn.Linear  `gguf:"cross_attn_v_proj"`
+	Output    *nn.Linear  `gguf:"cross_attn_o_proj"`
+}
+
+func (ca *TextCrossAttention) Forward(ctx ml.Context, hiddenState, crossAttentionStates ml.Tensor, cache model.Cache, opts *TextModelOptions) ml.Tensor {
+	batchSize := hiddenState.Dim(1)
+	headDim := opts.hiddenSize / opts.numHeads
+	numVisionTokens, numTiles := crossAttentionStates.Dim(1), crossAttentionStates.Dim(2)
+
+	query := ca.Query.Forward(ctx, hiddenState)
+	query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)
+	query = ca.QueryNorm.Forward(ctx, query, opts.eps)
+
+	key := ca.Key.Forward(ctx, crossAttentionStates)
+	key = key.Reshape(ctx, headDim, opts.numKVHeads, numVisionTokens*numTiles)
+	key = ca.KeyNorm.Forward(ctx, key, opts.eps)
+
+	value := ca.Value.Forward(ctx, crossAttentionStates)
+	value = value.Reshape(ctx, headDim, opts.numKVHeads, numVisionTokens*numTiles)
+
+	// TODO cache key, value
+
+	query = query.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	key = key.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+
+	scores := key.Mulmat(ctx, query)
+	scores = scores.Scale(ctx, 1.0/math.Sqrt(float64(headDim)))
+	scores = scores.Softmax(ctx)
+
+	attention := value.Mulmat(ctx, scores)
+	attention = attention.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	attention = attention.Reshape(ctx, opts.hiddenSize, batchSize)
+
+	return ca.Output.Forward(ctx, attention)
+}
+
+type TextCrossAttentionDecoderLayer struct {
+	AttentionNorm  *nn.RMSNorm `gguf:"attn_norm"`
+	CrossAttention *TextCrossAttention
+	AttentionGate  ml.Tensor `gguf:"cross_attn_attn_gate"`
+
+	MLPNorm *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP     *TextMLP
+	MLPGate ml.Tensor `gguf:"cross_attn_mlp_gate"`
+}
+
+func (d TextCrossAttentionDecoderLayer) Forward(ctx ml.Context, hiddenState, _, _, crossAttentionStates, crossAttentionMask ml.Tensor, cache model.Cache, opts *TextModelOptions) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = d.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = d.CrossAttention.Forward(ctx, hiddenState, crossAttentionStates, cache, opts)
+	hiddenState = hiddenState.Mul(ctx, d.AttentionGate.Tanh(ctx))
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = d.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = d.MLP.Forward(ctx, hiddenState, opts)
+	hiddenState = hiddenState.Mul(ctx, d.MLPGate.Tanh(ctx))
+	return hiddenState.Add(ctx, residual)
+}
+
+type TextDecoderLayer interface {
+	Forward(ctx ml.Context, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache model.Cache, opts *TextModelOptions) ml.Tensor
+}
+
+type TextDecoder struct {
+	Layers []TextDecoderLayer
+}
+
+func (d *TextDecoder) Forward(ctx ml.Context, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache model.Cache, opts *TextModelOptions) ml.Tensor {
+	for i, layer := range d.Layers {
+		if !slices.Contains(opts.crossAttentionLayers, uint32(i)) || crossAttentionStates != nil {
+			hiddenState = layer.Forward(ctx, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask, cache.Sub(i), opts)
+		}
+	}
+
+	return hiddenState
+}
+
+type TextModelOptions struct {
+	RopeFactors ml.Tensor `gguf:"rope_freqs.weight"`
+
+	hiddenSize, numHeads, numKVHeads int64
+	eps, ropeBase, ropeScale         float32
+	ropeDim                          uint32
+
+	crossAttentionLayers []uint32
+}
+
+type TextModel struct {
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Transformer    *TextDecoder  `gguf:"blk"`
+	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
+	Output         *nn.Linear    `gguf:"output"`
+
+	*TextModelOptions
+}
+
+func (m *TextModel) Forward(ctx ml.Context, inputIDs, positionIDs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache model.Cache) ml.Tensor {
+	hiddenState := m.TokenEmbedding.Forward(ctx, inputIDs)
+	hiddenState = m.Transformer.Forward(ctx, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask, cache, m.TextModelOptions)
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	return m.Output.Forward(ctx, hiddenState)
+}
+
+func newTextModel(c ml.Config) *TextModel {
+	var decoderLayers []TextDecoderLayer
+	for i := range c.Uint("block_count") {
+		var textDecoderLayer TextDecoderLayer
+		if slices.Contains(c.Uints("attention.cross_attention_layers"), i) {
+			textDecoderLayer = &TextCrossAttentionDecoderLayer{}
+		} else {
+			textDecoderLayer = &TextSelfAttentionDecoderLayer{}
+		}
+
+		decoderLayers = append(decoderLayers, textDecoderLayer)
+	}
+
+	return &TextModel{
+		Transformer: &TextDecoder{Layers: decoderLayers},
+		TextModelOptions: &TextModelOptions{
+			hiddenSize:           int64(c.Uint("embedding_length")),
+			numHeads:             int64(c.Uint("attention.head_count")),
+			numKVHeads:           int64(c.Uint("attention.head_count_kv")),
+			eps:                  c.Float("attention.layer_norm_rms_epsilon"),
+			ropeBase:             c.Float("rope.freq_base"),
+			ropeScale:            c.Float("rope.freq_scale", 1),
+			ropeDim:              c.Uint("rope.dimension_count"),
+			crossAttentionLayers: c.Uints("attention.cross_attention_layers"),
+		},
+	}
+}

model/mllama/model_vision.go

@@ -0,0 +1,234 @@
+package mllama
+
+import (
+	"math"
+	"slices"
+
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+)
+
+var batchSize int64 = 1
+
+type VisionSelfAttention struct {
+	Query  *nn.Linear `gguf:"attn_q"`
+	Key    *nn.Linear `gguf:"attn_k"`
+	Value  *nn.Linear `gguf:"attn_v"`
+	Output *nn.Linear `gguf:"attn_out"`
+
+	Gate ml.Tensor `gguf:"attn_gate"`
+}
+
+func (sa *VisionSelfAttention) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *VisionModelOptions) ml.Tensor {
+	headDim := opts.hiddenSize / opts.numHeads
+
+	query := sa.Query.Forward(ctx, hiddenState)
+	query = query.Reshape(ctx, headDim, opts.numHeads, query.Dim(1), batchSize)
+	query = query.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+
+	key := sa.Key.Forward(ctx, hiddenState)
+	key = key.Reshape(ctx, headDim, opts.numHeads, key.Dim(1), batchSize)
+	key = key.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+
+	value := sa.Value.Forward(ctx, hiddenState)
+	value = value.Reshape(ctx, headDim, opts.numHeads, value.Dim(1), batchSize)
+	value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+
+	scores := key.Mulmat(ctx, query)
+	scores = scores.Scale(ctx, 1.0/math.Sqrt(float64(headDim)))
+	scores = scores.Softmax(ctx)
+
+	attention := value.Mulmat(ctx, scores)
+	attention = attention.Reshape(ctx, headDim, attention.Dim(1), opts.numHeads, batchSize)
+	attention = attention.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	attention = attention.Reshape(ctx, opts.hiddenSize, attention.Dim(2), batchSize)
+
+	hiddenState = sa.Output.Forward(ctx, attention)
+	if sa.Gate != nil {
+		hiddenState = hiddenState.Mul(ctx, sa.Gate)
+	}
+
+	return hiddenState
+}
+
+type VisionMLP struct {
+	Down *nn.Linear `gguf:"ffn_down"`
+	Up   *nn.Linear `gguf:"ffn_up"`
+
+	Gate ml.Tensor `gguf:"ffn_gate"`
+}
+
+func (mlp *VisionMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *VisionModelOptions) ml.Tensor {
+	hiddenState = mlp.Down.Forward(ctx, hiddenState).GELU(ctx)
+	hiddenState = mlp.Up.Forward(ctx, hiddenState)
+	if mlp.Gate != nil {
+		hiddenState = hiddenState.Mul(ctx, mlp.Gate)
+	}
+
+	return hiddenState
+}
+
+type VisionEncoderLayer struct {
+	AttentionNorm *nn.LayerNorm `gguf:"ln1"`
+	SelfAttention *VisionSelfAttention
+
+	MLPNorm *nn.LayerNorm `gguf:"ln2"`
+	MLP     *VisionMLP
+}
+
+func (e *VisionEncoderLayer) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *VisionModelOptions) ml.Tensor {
+	residual := hiddenState
+
+	// self attention
+	hiddenState = e.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = e.SelfAttention.Forward(ctx, hiddenState, opts)
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	// feed forward
+	hiddenState = e.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = e.MLP.Forward(ctx, hiddenState, opts)
+	return hiddenState.Add(ctx, residual)
+}
+
+type VisionEncoder struct {
+	Layers []VisionEncoderLayer
+}
+
+func (e *VisionEncoder) Forward(ctx ml.Context, hiddenState ml.Tensor, intermediateLayersIndices []uint32, opts *VisionModelOptions) (ml.Tensor, []ml.Tensor) {
+	var intermediateHiddenStates []ml.Tensor
+	for i, layer := range e.Layers {
+		if slices.Contains(intermediateLayersIndices, uint32(i)) {
+			intermediateHiddenStates = append(intermediateHiddenStates, hiddenState.Reshape(ctx, append([]int64{1}, hiddenState.Shape()...)...))
+		}
+
+		hiddenState = layer.Forward(ctx, hiddenState, opts)
+	}
+
+	return hiddenState, intermediateHiddenStates
+}
+
+type PrecomputedAspectRatioEmbedding struct {
+	Embedding *nn.Embedding
+	Gate      ml.Tensor `gguf:"gate"`
+}
+
+func (e *PrecomputedAspectRatioEmbedding) Forward(ctx ml.Context, hiddenState ml.Tensor, aspectRatioIDs ml.Tensor, opts *VisionModelOptions) ml.Tensor {
+	embeddings := e.Embedding.Forward(ctx, aspectRatioIDs)
+	embeddings = embeddings.Reshape(ctx, opts.hiddenSize, 1, opts.numTiles)
+	if e.Gate != nil {
+		embeddings = embeddings.Mul(ctx, e.Gate)
+	}
+
+	return hiddenState.Add(ctx, embeddings)
+}
+
+type PrecomputedPositionEmbedding struct {
+	PositionEmbedding     *nn.Embedding `gguf:"position_embd"`
+	PositionEmbeddingGate ml.Tensor     `gguf:"position_embd.gate"`
+
+	TilePositionEmbedding     *nn.Embedding `gguf:"tile_position_embd"`
+	TilePositionEmbeddingGate ml.Tensor     `gguf:"tile_position_embd.gate"`
+}
+
+func (e *PrecomputedPositionEmbedding) Forward(ctx ml.Context, hiddenState, positionIDs, aspectRatioIDs ml.Tensor, numPositions int64, opts *VisionModelOptions) ml.Tensor {
+	positionEmbedding := e.PositionEmbedding.Forward(ctx, positionIDs)
+	if e.PositionEmbeddingGate != nil {
+		positionEmbedding = positionEmbedding.Mul(ctx, e.PositionEmbeddingGate)
+	}
+
+	hiddenState = hiddenState.Add(ctx, positionEmbedding)
+
+	tilePositionEmbedding := e.TilePositionEmbedding.Forward(ctx, aspectRatioIDs)
+	tilePositionEmbedding = tilePositionEmbedding.Reshape(ctx, opts.hiddenSize, numPositions, opts.numTiles)
+	if e.TilePositionEmbeddingGate != nil {
+		tilePositionEmbedding = tilePositionEmbedding.Mul(ctx, e.TilePositionEmbeddingGate)
+	}
+
+	return hiddenState.Add(ctx, tilePositionEmbedding)
+}
+
+type VisionModelOptions struct {
+	hiddenSize, numHeads, numTiles int64
+	imageSize, patchSize           int
+	eps                            float32
+
+	intermediateLayersIndices []uint32
+}
+
+type VisionModel struct {
+	PatchEmbeddings *nn.Conv2D `gguf:"patch_embd"`
+
+	PreTilePositionEmbedding  *PrecomputedAspectRatioEmbedding `gguf:"pre_tile_position_embd"`
+	PostTilePositionEmbedding *PrecomputedAspectRatioEmbedding `gguf:"post_tile_position_embd"`
+	PositionEmbedding         *PrecomputedPositionEmbedding
+
+	PreLayerNorm   *nn.LayerNorm `gguf:"pre_ln"`
+	PostLayerNorm  *nn.LayerNorm `gguf:"post_ln"`
+	ClassEmbedding ml.Tensor     `gguf:"class_embd"`
+
+	Transformer       *VisionEncoder `gguf:"blk"`
+	GlobalTransformer *VisionEncoder `gguf:"global.blk"`
+
+	*VisionModelOptions
+}
+
+func (m *VisionModel) Forward(ctx ml.Context, pixelValues, positionIDs, aspectRatioIDs ml.Tensor) ml.Tensor {
+	numPatches := int64((m.imageSize / m.patchSize) * (m.imageSize / m.patchSize))
+	numPositions := numPatches
+	if m.ClassEmbedding != nil {
+		numPositions++
+	}
+
+	hiddenState := m.PatchEmbeddings.Forward(ctx, pixelValues, m.patchSize, m.patchSize, 0, 0, 1, 1)
+	hiddenState = hiddenState.Reshape(ctx, numPatches, m.hiddenSize, m.numTiles)
+	hiddenState = hiddenState.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
+
+	hiddenState = m.PreTilePositionEmbedding.Forward(ctx, hiddenState, aspectRatioIDs, m.VisionModelOptions)
+	hiddenState = m.ClassEmbedding.Stack(ctx, 2, slices.Repeat([]ml.Tensor{m.ClassEmbedding}, int(m.numTiles)-1)...).Concat(ctx, hiddenState, 1)
+
+	hiddenState = m.PositionEmbedding.Forward(ctx, hiddenState, positionIDs, aspectRatioIDs, numPositions, m.VisionModelOptions)
+	hiddenState = m.PreLayerNorm.Forward(ctx, hiddenState, m.eps)
+
+	numPaddingPatches := 8 - (hiddenState.Dim(1)%8)%8
+	hiddenState = hiddenState.Pad(ctx, 0, numPaddingPatches, 0, 0)
+
+	hiddenState = hiddenState.Reshape(ctx, hiddenState.Dim(0), hiddenState.Dim(1)*hiddenState.Dim(2), batchSize)
+	hiddenState, intermediateHiddenStates := m.Transformer.Forward(ctx, hiddenState, m.intermediateLayersIndices, m.VisionModelOptions)
+
+	hiddenState = m.PostLayerNorm.Forward(ctx, hiddenState, m.eps)
+
+	hiddenState = hiddenState.Reshape(ctx, m.hiddenSize, numPositions+numPaddingPatches, m.numTiles, batchSize)
+	hiddenState = m.PostTilePositionEmbedding.Forward(ctx, hiddenState, aspectRatioIDs, m.VisionModelOptions)
+
+	hiddenState = hiddenState.Reshape(ctx, m.hiddenSize, m.numTiles*(numPositions+numPaddingPatches), batchSize)
+	hiddenState, _ = m.GlobalTransformer.Forward(ctx, hiddenState, nil, m.VisionModelOptions)
+
+	hiddenStates := intermediateHiddenStates[0].Stack(ctx, 0, intermediateHiddenStates[1:]...)
+	hiddenStates = hiddenStates.Reshape(ctx, int64(len(intermediateHiddenStates))*m.hiddenSize, numPositions+numPaddingPatches, m.numTiles, batchSize)
+	hiddenStates = hiddenStates.Unpad(ctx, 0, numPaddingPatches, 0, 0)
+
+	hiddenState = hiddenState.Reshape(ctx, m.hiddenSize, numPositions+numPaddingPatches, m.numTiles, batchSize)
+	hiddenState = hiddenState.Unpad(ctx, 0, numPaddingPatches, 0, 0)
+	return hiddenState.Concat(ctx, hiddenStates, 0)
+}
+
+func newVisionModel(c ml.Config) *VisionModel {
+	return &VisionModel{
+		Transformer:       &VisionEncoder{Layers: make([]VisionEncoderLayer, c.Uint("vision.block_count"))},
+		GlobalTransformer: &VisionEncoder{Layers: make([]VisionEncoderLayer, c.Uint("vision.global.block_count"))},
+
+		VisionModelOptions: &VisionModelOptions{
+			hiddenSize: int64(c.Uint("vision.embedding_length")),
+			numHeads:   int64(c.Uint("vision.attention.head_count")),
+			numTiles:   int64(c.Uint("vision.max_num_tiles")),
+
+			imageSize: int(c.Uint("vision.image_size")),
+			patchSize: int(c.Uint("vision.patch_size")),
+
+			eps: c.Float("vision.attention.layer_norm_epsilon"),
+
+			intermediateLayersIndices: c.Uints("vision.intermediate_layers_indices"),
+		},
+	}
+}

model/mllama/process_image.go

@@ -0,0 +1,240 @@
+package mllama
+
+import (
+	"image"
+	"image/color"
+	"math"
+	"slices"
+
+	"golang.org/x/image/draw"
+
+	"github.com/ollama/ollama/ml"
+)
+
+type ImageProcessor struct {
+	imageSize, numChannels, maxNumTiles int
+}
+
+func newImageProcessor(c ml.Config) ImageProcessor {
+	return ImageProcessor{
+		imageSize:   int(c.Uint("vision.image_size")),
+		numChannels: int(c.Uint("vision.num_channels")),
+		maxNumTiles: int(c.Uint("vision.max_num_tiles")),
+	}
+}
+
+func (p *ImageProcessor) supportedAspectRatios(maxTiles int) []image.Point {
+	ratios := []image.Point{}
+
+	for w := range maxTiles {
+		for h := range maxTiles {
+			if (w+1)*(h+1) <= maxTiles {
+				ratios = append(ratios, image.Point{w + 1, h + 1})
+			}
+		}
+	}
+
+	return ratios
+}
+
+func (p *ImageProcessor) clip(a, a_min, a_max int) int {
+	if a < a_min {
+		return a_min
+	} else if a > a_max {
+		return a_max
+	}
+
+	return a
+}
+
+func (p *ImageProcessor) fitToCanvas(imageSize, canvasSize image.Point, tileSize int) image.Point {
+	targetWidth := p.clip(imageSize.X, tileSize, canvasSize.X)
+	targetHeight := p.clip(imageSize.Y, tileSize, canvasSize.Y)
+
+	scaleWidth := float64(targetWidth) / float64(imageSize.X)
+	scaleHeight := float64(targetHeight) / float64(imageSize.Y)
+
+	var w, h int
+
+	if scaleWidth < scaleHeight {
+		w = targetWidth
+		h = min(int(math.Floor(float64(imageSize.Y)*scaleWidth)), targetHeight)
+	} else {
+		w = min(int(math.Floor(float64(imageSize.X)*scaleHeight)), targetWidth)
+		h = targetHeight
+	}
+
+	return image.Point{w, h}
+}
+
+func (p *ImageProcessor) optimalTiledCanvas(imageSize image.Point, maxImageTiles, tileSize int) image.Point {
+	possibleTileArrangements := p.supportedAspectRatios(maxImageTiles)
+	possibleCanvasSizes := []image.Point{}
+	for _, pta := range possibleTileArrangements {
+		possibleCanvasSizes = append(possibleCanvasSizes, image.Point{pta.X * tileSize, pta.Y * tileSize})
+	}
+
+	scales := []float64{}
+
+	for _, pcs := range possibleCanvasSizes {
+		scaleHeight := float64(pcs.Y) / float64(imageSize.Y)
+		scaleWidth := float64(pcs.X) / float64(imageSize.X)
+
+		if scaleWidth > scaleHeight {
+			scales = append(scales, scaleHeight)
+		} else {
+			scales = append(scales, scaleWidth)
+		}
+	}
+
+	var minUpscale float64
+	var maxDownscale float64
+	var upscale bool
+
+	for _, s := range scales {
+		if s > 1.0 {
+			upscale = true
+			if minUpscale == 0 {
+				minUpscale = s
+			} else {
+				minUpscale = math.Min(minUpscale, s)
+			}
+		} else {
+			maxDownscale = math.Max(maxDownscale, s)
+		}
+	}
+
+	selectedScale := maxDownscale
+	if upscale {
+		selectedScale = minUpscale
+	}
+
+	var selectedCanvas image.Point
+	for n, pcs := range possibleCanvasSizes {
+		if scales[n] == selectedScale {
+			// choose the smallest possible canvas
+			if selectedCanvas.X == 0 && selectedCanvas.Y == 0 {
+				selectedCanvas = pcs
+			} else if pcs.X*pcs.Y < selectedCanvas.X*selectedCanvas.Y {
+				selectedCanvas = pcs
+			}
+		}
+	}
+	return selectedCanvas
+}
+
+func (p *ImageProcessor) splitToTiles(img image.Image, numTilesSize image.Point) []image.Image {
+	b := img.Bounds()
+	width := b.Max.X - b.Min.X
+	height := b.Max.Y - b.Min.Y
+	tileHeight := height / numTilesSize.Y
+	tileWidth := width / numTilesSize.X
+
+	images := []image.Image{}
+
+	for h := range numTilesSize.Y {
+		for w := range numTilesSize.X {
+			rect := image.Rect(tileWidth*w, tileHeight*h, tileWidth*(w+1), tileHeight*(h+1))
+			images = append(images, img.(interface {
+				SubImage(image.Rectangle) image.Image
+			}).SubImage(rect))
+		}
+	}
+
+	return images
+}
+
+// remove the "alpha" channel by drawing over a prefilled image
+//
+// remove the "alpha" channel by drawing over a prefilled image
+//
+//nolint:unused
+func (p *ImageProcessor) compositeImage(img image.Image) image.Image {
+	dst := image.NewRGBA(img.Bounds())
+
+	white := color.RGBA{255, 255, 255, 255}
+	draw.Draw(dst, dst.Bounds(), &image.Uniform{white}, image.Point{}, draw.Src)
+	draw.Draw(dst, dst.Bounds(), img, img.Bounds().Min, draw.Over)
+
+	return dst
+}
+
+func (p *ImageProcessor) resize(img image.Image, outputSize image.Point, maxImageTiles int) (image.Image, image.Point) {
+	b := img.Bounds()
+	tileSize := outputSize.Y
+
+	canvasSize := p.optimalTiledCanvas(b.Max, maxImageTiles, tileSize)
+	aspectRatio := image.Point{canvasSize.X / tileSize, canvasSize.Y / tileSize}
+	newSize := p.fitToCanvas(b.Max, canvasSize, tileSize)
+
+	dst := image.NewRGBA(image.Rect(0, 0, newSize.X, newSize.Y))
+
+	// scaling choices:
+	//   NearestNeighbor	fast, blocky output
+	//   ApproxBiLinear	fast, medium quality
+	//   BiLinear		slow, high quality
+	//   CatmullRom		very slow, very high quality
+	draw.BiLinear.Scale(dst, dst.Rect, img, b, draw.Over, nil)
+
+	return dst, aspectRatio
+}
+
+func (p *ImageProcessor) pad(img image.Image, outputSize, aspectRatio image.Point) image.Image {
+	paddedSize := image.Point{
+		X: outputSize.X * aspectRatio.X,
+		Y: outputSize.Y * aspectRatio.Y,
+	}
+
+	dst := image.NewRGBA(image.Rect(0, 0, paddedSize.X, paddedSize.Y))
+	draw.Draw(dst, img.Bounds(), img, image.Point{0, 0}, draw.Over)
+
+	return dst
+}
+
+func (p *ImageProcessor) pack(img image.Image, aspectRatio image.Point, mean, std [3]float32) []float32 {
+	subImages := p.splitToTiles(img, aspectRatio)
+
+	var pixelVals []float32
+
+	for _, subImg := range subImages {
+		bounds := subImg.Bounds()
+		var rVals, gVals, bVals []float32
+		for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
+			for x := bounds.Min.X; x < bounds.Max.X; x++ {
+				c := subImg.At(x, y)
+				r, g, b, _ := c.RGBA()
+				rVal := float32(r>>8) / 255.0
+				gVal := float32(g>>8) / 255.0
+				bVal := float32(b>>8) / 255.0
+
+				rVal = (rVal - mean[0]) / std[0]
+				gVal = (gVal - mean[1]) / std[1]
+				bVal = (bVal - mean[2]) / std[2]
+
+				rVals = append(rVals, rVal)
+				gVals = append(gVals, gVal)
+				bVals = append(bVals, bVal)
+			}
+		}
+		pixelVals = append(pixelVals, rVals...)
+		pixelVals = append(pixelVals, gVals...)
+		pixelVals = append(pixelVals, bVals...)
+	}
+
+	return pixelVals
+}
+
+func (p ImageProcessor) ProcessImage(img image.Image) ([]float32, int, error) {
+	outputSize := image.Point{p.imageSize, p.imageSize}
+
+	// clip values
+	mean := [3]float32{0.48145466, 0.4578275, 0.40821073}
+	std := [3]float32{0.26862954, 0.26130258, 0.27577711}
+
+	newImage, aspectRatio := p.resize(img, outputSize, p.maxNumTiles)
+	newImage = p.pad(newImage, outputSize, aspectRatio)
+
+	data := p.pack(newImage, aspectRatio, mean, std)
+	aspectRatioIndex := slices.Index(p.supportedAspectRatios(p.maxNumTiles), aspectRatio) + 1
+	return data, aspectRatioIndex, nil
+}

model/mllama/process_text.go

@@ -0,0 +1,25 @@
+package mllama
+
+import (
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model"
+)
+
+type TextProcessor struct {
+	model.BytePairEncoding
+}
+
+func newTextProcessor(c ml.Config) TextProcessor {
+	return TextProcessor{
+		BytePairEncoding: model.BytePairEncoding{
+			Pretokenizer: c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
+			Vocabulary: &model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Types:  c.Uints("tokenizer.ggml.token_type"),
+				Merges: c.Strings("tokenizer.ggml.merges"),
+				BOS:    c.Uint("tokenizer.ggml.bos_token_id"),
+				EOS:    c.Uint("tokenizer.ggml.eos_token_id"),
+			},
+		},
+	}
+}

model/mllama/process_text_test.go

@@ -0,0 +1,87 @@
+package mllama
+
+import (
+	"encoding/json"
+	"errors"
+	"os"
+	"path/filepath"
+	"strconv"
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+	"github.com/google/go-cmp/cmp/cmpopts"
+
+	"github.com/ollama/ollama/model"
+)
+
+func TestProcessText(t *testing.T) {
+	ours, err := model.New(filepath.Join("testdata", "model.bin"))
+	if errors.Is(err, os.ErrNotExist) {
+		t.Skip("no model.bin")
+	} else if err != nil {
+		t.Fatal(err)
+	}
+
+	t.Run("decode", func(t *testing.T) {
+		f, err := os.Open(filepath.Join("testdata", "theirs.json"))
+		if errors.Is(err, os.ErrNotExist) {
+			t.Skip("no theirs.json")
+		} else if err != nil {
+			t.Fatal(err)
+		}
+		defer f.Close()
+
+		var theirs [][]byte
+		if err := json.NewDecoder(f).Decode(&theirs); err != nil {
+			t.Fatal(err)
+		}
+
+		for id := range theirs {
+			ids := []int32{int32(id)}
+			s, err := ours.(model.TextProcessor).Decode(ids)
+			if err != nil {
+				t.Fatal(err)
+			}
+
+			if diff := cmp.Diff(string(theirs[id]), s); diff != "" {
+				t.Errorf("%d no match (-theirs +ours):\n%s", id, diff)
+			}
+		}
+	})
+
+	t.Run("encode", func(t *testing.T) {
+		f, err := os.Open(filepath.Join("..", "testdata", "inputs.json"))
+		if errors.Is(err, os.ErrNotExist) {
+			t.Skip("no inputs.json")
+		} else if err != nil {
+			t.Fatal(err)
+		}
+		defer f.Close()
+
+		var inputs []struct {
+			Values []byte  `json:"base64"`
+			IDs    []int32 `json:"ids"`
+		}
+
+		if err := json.NewDecoder(f).Decode(&inputs); err != nil {
+			t.Fatal(err)
+		}
+
+		for i, input := range inputs {
+			if i == 45 {
+				t.Skip("skip 45")
+			}
+
+			t.Run(strconv.Itoa(i), func(t *testing.T) {
+				ids, err := ours.(model.TextProcessor).Encode(string(input.Values))
+				if err != nil {
+					t.Fatal(err)
+				}
+
+				if diff := cmp.Diff(input.IDs, ids, cmpopts.EquateEmpty()); diff != "" {
+					t.Errorf("%s: no match (-theirs +ours):\n%s", input.Values, diff)
+				}
+			})
+		}
+	})
+}

model/mllama/testdata/model.bin

@@ -0,0 +1 @@
+/Users/michaelyang/git/ollama/library/nltpt/Llama-3.2-11B-Vision-Instruct/merged.gguf

model/model.go

@@ -0,0 +1,279 @@
+package model
+
+import (
+	"fmt"
+	"image"
+	_ "image/jpeg"
+	_ "image/png"
+	"log/slog"
+	"os"
+	"reflect"
+	"strconv"
+	"strings"
+
+	_ "golang.org/x/image/bmp"
+	_ "golang.org/x/image/tiff"
+	_ "golang.org/x/image/webp"
+
+	"github.com/ollama/ollama/cache"
+	"github.com/ollama/ollama/ml"
+	_ "github.com/ollama/ollama/ml/backend"
+)
+
+type Cache struct {
+	cache.Cache
+	cache.Options
+}
+
+func (c Cache) Sub(i int) Cache {
+	if c.Cache != nil {
+		return Cache{
+			Cache:   c.Cache.Sub(i),
+			Options: c.Options,
+		}
+	}
+
+	return c
+}
+
+func (c Cache) Put(ctx ml.Context, key, value ml.Tensor, opts cache.Options) (ml.Tensor, ml.Tensor) {
+	if c.Cache != nil {
+		return c.Cache.Put(ctx, key, value, opts)
+	}
+
+	return key, value
+}
+
+type Options struct {
+	inputs []int32
+
+	Offset int
+
+	Images []image.Image
+
+	Cache
+}
+
+func (opts Options) Inputs() []int32 {
+	return opts.inputs[opts.Offset:]
+}
+
+func (opts Options) Positions() []int32 {
+	positions := make([]int32, len(opts.inputs)-opts.Offset)
+	for i := range positions {
+		positions[i] = int32(opts.Offset + i)
+	}
+
+	return positions
+}
+
+type OptionsFunc func(Model, *Options)
+
+func WithInputIDs(ids []int32) OptionsFunc {
+	return func(m Model, opts *Options) {
+		opts.inputs = ids
+	}
+}
+
+func WithOffset(offset int) OptionsFunc {
+	return func(m Model, opts *Options) {
+		opts.Offset = offset
+		opts.Cache.Position = offset
+	}
+}
+
+func WithImage(img image.Image) OptionsFunc {
+	return func(m Model, opts *Options) {
+		opts.Images = append(opts.Images, img)
+	}
+}
+
+func WithCache(c cache.Cache) OptionsFunc {
+	return func(m Model, opts *Options) {
+		opts.Cache = Cache{
+			Cache: c,
+			Options: cache.Options{
+				Position: opts.Offset,
+			},
+		}
+	}
+}
+
+type Base struct {
+	b ml.Backend
+}
+
+func (m *Base) Backend() ml.Backend {
+	return m.b
+}
+
+type Model interface {
+	Forward(ml.Context, Options) (ml.Tensor, error)
+
+	Backend() ml.Backend
+}
+
+var models = make(map[string]func(ml.Config) (Model, error))
+
+func Register(name string, f func(ml.Config) (Model, error)) {
+	if _, ok := models[name]; ok {
+		panic("model: model already registered")
+	}
+
+	models[name] = f
+}
+
+func New(s string) (Model, error) {
+	r, err := os.Open(s)
+	if err != nil {
+		return nil, err
+	}
+	defer r.Close()
+
+	b, err := ml.NewBackend(r)
+	if err != nil {
+		return nil, err
+	}
+
+	arch := b.Config().Architecture()
+	f, ok := models[arch]
+	if !ok {
+		return nil, fmt.Errorf("unsupported model architecture %q", arch)
+	}
+
+	m, err := f(b.Config())
+	if err != nil {
+		return nil, err
+	}
+
+	v := reflect.ValueOf(m)
+	v.Elem().Set(populateFields(b, v))
+	return m, nil
+}
+
+func populateFields(b ml.Backend, v reflect.Value, tags ...Tag) reflect.Value {
+	t := v.Type()
+	if t.Kind() == reflect.Pointer {
+		t, v = t.Elem(), v.Elem()
+	}
+
+	if t.Kind() == reflect.Struct {
+		allNil := true
+		for i := range t.NumField() {
+			tt := t.Field(i).Type
+			vv := v.Field(i)
+			if !vv.CanSet() {
+				continue
+			}
+
+			// make a copy
+			tagsCopy := tags
+			if tag := t.Field(i).Tag.Get("gguf"); tag != "" {
+				tagsCopy = append(tagsCopy, ParseTags(tag))
+			}
+
+			if tt == reflect.TypeOf((*Base)(nil)).Elem() {
+				vv.Set(reflect.ValueOf(Base{b: b}))
+			} else if tt == reflect.TypeOf((*ml.Tensor)(nil)).Elem() {
+				var fn func([]Tag) [][]string
+				fn = func(tags []Tag) (values [][]string) {
+					if len(tags) < 1 {
+						return nil
+					}
+
+					values = [][]string{{tags[0].Name}}
+					for _, alt := range tags[0].Alternate {
+						values = append(values, []string{alt})
+					}
+
+					for i, value := range values {
+						for _, rest := range fn(tags[1:]) {
+							value = append(value, rest...)
+						}
+
+						values[i] = value
+					}
+
+					return values
+				}
+
+				names := fn(tagsCopy)
+				for _, name := range names {
+					if tensor := b.Get(strings.Join(name, ".")); tensor != nil {
+						slog.Debug("found tensor", "", tensor)
+						vv.Set(reflect.ValueOf(tensor))
+						break
+					}
+				}
+			} else if tt.Kind() == reflect.Pointer {
+				vvv := vv.Elem()
+				if vv.IsNil() {
+					vvv = reflect.New(tt.Elem())
+				}
+
+				if f := populateFields(b, vvv, tagsCopy...); f.CanAddr() {
+					vv.Set(f.Addr())
+				}
+			} else if tt.Kind() == reflect.Slice || tt.Kind() == reflect.Array {
+				for i := range vv.Len() {
+					vv.Index(i).Set(populateFields(b, vv.Index(i), append(tagsCopy, Tag{Name: strconv.Itoa(i)})...))
+				}
+			}
+
+			if !canNil(tt) || !vv.IsNil() {
+				allNil = false
+			}
+		}
+
+		if allNil {
+			return reflect.Zero(t)
+		}
+	}
+
+	return v
+}
+
+type Tag struct {
+	Name      string
+	Alternate []string
+}
+
+func ParseTags(s string) (tag Tag) {
+	parts := strings.Split(s, ",")
+	if len(parts) > 0 {
+		tag.Name = parts[0]
+
+		for _, part := range parts[1:] {
+			if value, ok := strings.CutPrefix(part, "alt:"); ok {
+				tag.Alternate = append(tag.Alternate, value)
+			}
+		}
+	}
+
+	return
+}
+
+func canNil(t reflect.Type) bool {
+	return t.Kind() == reflect.Chan ||
+		t.Kind() == reflect.Func ||
+		t.Kind() == reflect.Interface ||
+		t.Kind() == reflect.Map ||
+		t.Kind() == reflect.Pointer ||
+		t.Kind() == reflect.Slice
+}
+
+func Forward(m Model, optsFuncs ...OptionsFunc) (ml.Tensor, error) {
+	var opts Options
+	for _, optsFunc := range optsFuncs {
+		optsFunc(m, &opts)
+	}
+
+	ctx := m.Backend().NewContext()
+	t, err := m.Forward(ctx, opts)
+	if err != nil {
+		return nil, err
+	}
+	defer ctx.Close()
+
+	return ctx.Compute(t), nil
+}

model/model_test.go

@@ -0,0 +1,136 @@
+package model
+
+import (
+	"reflect"
+	"slices"
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/backend/ggml"
+	"github.com/ollama/ollama/ml/nn"
+)
+
+func TestParseTags(t *testing.T) {
+	cases := []struct {
+		value string
+		want  Tag
+	}{
+		{
+			value: "output",
+			want: Tag{
+				Name: "output",
+			},
+		},
+		{
+			value: "output,alt:token_embd",
+			want: Tag{
+				Name: "output",
+				Alternate: []string{
+					"token_embd",
+				},
+			},
+		},
+	}
+
+	for _, tt := range cases {
+		t.Run(tt.value, func(t *testing.T) {
+			got := ParseTags(tt.value)
+			if diff := cmp.Diff(tt.want, got); diff != "" {
+				t.Errorf("ParseTags() returned unexpected values (-want +got):\n%s", diff)
+			}
+		})
+	}
+}
+
+type fakeBackend struct {
+	*ggml.Backend
+	names []string
+}
+
+type fakeTensor struct {
+	*ggml.Tensor
+	Name string
+}
+
+func (m *fakeBackend) Get(name string) ml.Tensor {
+	if slices.Contains(m.names, name) {
+		return &fakeTensor{Name: name}
+	}
+
+	return nil
+}
+
+func TestPopulateFields(t *testing.T) {
+	type fakeLayer struct {
+		Query  *nn.Linear `gguf:"attn_q"`
+		Key    *nn.Linear `gguf:"attn_k"`
+		Value  *nn.Linear `gguf:"attn_v"`
+		Output *nn.Linear `gguf:"attn_o"`
+	}
+
+	type fakeModel struct {
+		Input      *nn.Embedding `gguf:"input"`
+		OutputNorm *nn.RMSNorm   `gguf:"output_norm"`
+		Output     *nn.Linear    `gguf:"output"`
+		Layers     [2]fakeLayer  `gguf:"blk"`
+	}
+
+	var m fakeModel
+	v := reflect.ValueOf(&m)
+	v.Elem().Set(populateFields(&fakeBackend{
+		names: []string{
+			"input.weight",
+			"blk.0.attn_q.weight",
+			"blk.0.attn_k.weight",
+			"blk.0.attn_v.weight",
+			"blk.1.attn_q.weight",
+			"blk.1.attn_k.weight",
+			"blk.1.attn_v.weight",
+			"output_norm.weight",
+			"output.weight",
+		},
+	}, v))
+
+	if diff := cmp.Diff(fakeModel{
+		Input:      &nn.Embedding{Weight: &fakeTensor{Name: "input.weight"}},
+		OutputNorm: &nn.RMSNorm{Weight: &fakeTensor{Name: "output_norm.weight"}},
+		Output:     &nn.Linear{Weight: &fakeTensor{Name: "output.weight"}},
+		Layers: [2]fakeLayer{
+			{
+				Query: &nn.Linear{Weight: &fakeTensor{Name: "blk.0.attn_q.weight"}},
+				Key:   &nn.Linear{Weight: &fakeTensor{Name: "blk.0.attn_k.weight"}},
+				Value: &nn.Linear{Weight: &fakeTensor{Name: "blk.0.attn_v.weight"}},
+			},
+			{
+				Query: &nn.Linear{Weight: &fakeTensor{Name: "blk.1.attn_q.weight"}},
+				Key:   &nn.Linear{Weight: &fakeTensor{Name: "blk.1.attn_k.weight"}},
+				Value: &nn.Linear{Weight: &fakeTensor{Name: "blk.1.attn_v.weight"}},
+			},
+		},
+	}, m); diff != "" {
+		t.Errorf("populateFields() set incorrect values (-want +got):\n%s", diff)
+	}
+}
+
+func TestPopulateFieldsAlternateName(t *testing.T) {
+	type fakeModel struct {
+		Input  *nn.Embedding `gguf:"input"`
+		Output *nn.Linear    `gguf:"output,alt:input"`
+	}
+
+	m := fakeModel{}
+	v := reflect.ValueOf(&m)
+	v.Elem().Set(populateFields(&fakeBackend{
+		names: []string{
+			"input.weight",
+		},
+	}, v))
+
+	if diff := cmp.Diff(fakeModel{
+		Input:  &nn.Embedding{Weight: &fakeTensor{Name: "input.weight"}},
+		Output: &nn.Linear{Weight: &fakeTensor{Name: "input.weight"}},
+	}, m); diff != "" {
+		t.Errorf("populateFields() set incorrect values (-want +got):\n%s", diff)
+	}
+}

model/process_text.go

@@ -0,0 +1,312 @@
+package model
+
+import (
+	"cmp"
+	"log/slog"
+	"strings"
+	"sync"
+
+	"github.com/dlclark/regexp2"
+	heap "github.com/emirpasic/gods/v2/trees/binaryheap"
+)
+
+type Special int32
+
+const (
+	SpecialBOS Special = iota
+	SpecialEOS
+)
+
+type TextProcessor interface {
+	Encode(string) ([]int32, error)
+	Decode([]int32) (string, error)
+	Is(uint32, Special) bool
+}
+
+type Vocabulary struct {
+	Values []string
+	Types  []uint32
+	Scores []uint32
+	Merges []string
+
+	BOS, EOS uint32
+
+	specialOnce sync.Once
+	special     []string
+
+	valuesOnce sync.Once
+	values     map[string]int32
+
+	mergeOnce sync.Once
+	merge     map[string]int32
+}
+
+func (v *Vocabulary) Is(id uint32, special Special) bool {
+	switch special {
+	case SpecialBOS:
+		return id == v.BOS
+	case SpecialEOS:
+		return id == v.EOS
+	default:
+		return false
+	}
+}
+
+func (v *Vocabulary) Encode(s string) int32 {
+	v.valuesOnce.Do(func() {
+		v.values = make(map[string]int32, len(v.Values))
+		for i, value := range v.Values {
+			v.values[value] = int32(i)
+		}
+	})
+
+	if id, ok := v.values[s]; ok {
+		return id
+	}
+
+	return -1
+}
+
+func (v *Vocabulary) Decode(id int32) string {
+	return v.Values[id]
+}
+
+func (v *Vocabulary) SpecialVocabulary() []string {
+	v.specialOnce.Do(func() {
+		for i := range v.Values {
+			if v.Types[i] == 3 {
+				v.special = append(v.special, v.Values[i])
+			}
+		}
+	})
+
+	return v.special
+}
+
+func (v *Vocabulary) Merge(left, right string) int {
+	v.mergeOnce.Do(func() {
+		v.merge = make(map[string]int32, len(v.Merges))
+		for i, merge := range v.Merges {
+			v.merge[merge] = int32(i)
+		}
+	})
+
+	if id, ok := v.merge[left+" "+right]; ok {
+		return int(id)
+	}
+
+	return -1
+}
+
+type BytePairEncoding struct {
+	Pretokenizer string
+
+	*Vocabulary
+}
+
+func (bpe BytePairEncoding) split(s string) ([]string, error) {
+	re, err := regexp2.Compile(bpe.Pretokenizer, regexp2.Unicode|regexp2.RE2)
+	if err != nil {
+		return nil, err
+	}
+
+	var matches []string
+	for m, _ := re.FindStringMatch(s); m != nil; m, _ = re.FindNextMatch(m) {
+		matches = append(matches, m.String())
+	}
+
+	return matches, nil
+}
+
+// fragment is a string fragment and their corresponding token IDs
+type fragment struct {
+	value string
+	ids   []int32
+}
+
+// pair is a pair of runes and its rank
+type pair struct {
+	a, b  int
+	rank  int
+	value string
+}
+
+type merge struct {
+	p, n  int
+	runes []rune
+}
+
+func (bpe BytePairEncoding) Encode(s string) ([]int32, error) {
+	fragments := []fragment{{value: s}}
+	for _, special := range bpe.Vocabulary.SpecialVocabulary() {
+		// TODO: process special tokens concurrently
+		id := bpe.Vocabulary.Encode(special)
+		for i := 0; i < len(fragments); i++ {
+			frag := fragments[i]
+			if len(frag.ids) > 0 {
+				continue
+			}
+
+			var middle []fragment
+			switch i := strings.Index(frag.value, special); {
+			case i < 0:
+				middle = append(middle, frag)
+			case i > 0:
+				middle = append(middle, fragment{value: frag.value[:i]})
+				fallthrough
+			default:
+				middle = append(middle, fragment{value: special, ids: []int32{id}})
+				if rest := frag.value[i+len(special):]; rest != "" {
+					middle = append(middle, fragment{value: rest})
+				}
+			}
+
+			fragments = append(fragments[:i], append(middle, fragments[i+1:]...)...)
+		}
+	}
+
+	var ids []int32
+	for _, frag := range fragments {
+		if len(frag.ids) > 0 {
+			ids = append(ids, frag.ids...)
+			slog.Debug("encoded", "text", frag.value, "ids", frag.ids, "special", true)
+			continue
+		}
+
+		// split fragment using pretokenizer
+		splits, err := bpe.split(frag.value)
+		if err != nil {
+			return nil, err
+		}
+
+		for _, split := range splits {
+			// TODO: process splits concurrently
+			var sb strings.Builder
+			for _, b := range []byte(split) {
+				r := rune(b)
+				switch {
+				case r == 0x00ad:
+					r = 0x0143
+				case r <= 0x0020:
+					r = r + 0x0100
+				case r >= 0x007e && r <= 0x00a0:
+					r = r + 0x00a2
+				}
+
+				sb.WriteRune(r)
+			}
+
+			// short circuit if the fragment is in the vocabulary
+			if id := bpe.Vocabulary.Encode(sb.String()); id >= 0 {
+				ids = append(ids, id)
+				slog.Debug("encoded", "text", sb.String(), "ids", []int32{id})
+				continue
+			}
+
+			runes := []rune(sb.String())
+			merges := make([]merge, len(runes))
+			for r := range runes {
+				merges[r] = merge{
+					p:     r - 1,
+					n:     r + 1,
+					runes: []rune{runes[r]},
+				}
+			}
+
+			pairwise := func(a, b int) *pair {
+				if a < 0 || b >= len(runes) {
+					return nil
+				}
+
+				left, right := string(merges[a].runes), string(merges[b].runes)
+				rank := bpe.Vocabulary.Merge(left, right)
+				if rank < 0 {
+					return nil
+				}
+
+				return &pair{
+					a:     a,
+					b:     b,
+					rank:  rank,
+					value: left + right,
+				}
+			}
+
+			pairs := heap.NewWith(func(i, j *pair) int {
+				return cmp.Compare(i.rank, j.rank)
+			})
+
+			for i := range len(runes) - 1 {
+				if pair := pairwise(i, i+1); pair != nil {
+					pairs.Push(pair)
+				}
+			}
+
+			for !pairs.Empty() {
+				pair, _ := pairs.Pop()
+
+				left, right := merges[pair.a], merges[pair.b]
+				if len(left.runes) == 0 || len(right.runes) == 0 ||
+					string(left.runes)+string(right.runes) != pair.value {
+					continue
+				}
+
+				merges[pair.a].runes = append(left.runes, right.runes...)
+				merges[pair.b].runes = nil
+
+				merges[pair.a].n = right.n
+				if right.n < len(merges) {
+					merges[right.n].p = pair.a
+				}
+
+				if pair := pairwise(merges[pair.a].p, pair.a); pair != nil {
+					pairs.Push(pair)
+				}
+
+				if pair := pairwise(pair.a, merges[pair.a].n); pair != nil {
+					pairs.Push(pair)
+				}
+			}
+
+			for _, merge := range merges {
+				if len(merge.runes) > 0 {
+					// TODO: handle the edge case where the rune isn't in the vocabulary
+					if id := bpe.Vocabulary.Encode(string(merge.runes)); id >= 0 {
+						ids = append(ids, id)
+						slog.Debug("encoded", "text", string(merge.runes), "ids", []int32{id})
+					}
+				}
+			}
+		}
+	}
+
+	return ids, nil
+}
+
+func (bpe BytePairEncoding) Decode(ids []int32) (string, error) {
+	var sb strings.Builder
+	for _, id := range ids {
+		for _, r := range bpe.Vocabulary.Decode(id) {
+			switch {
+			case r == 0x0100:
+				// this produces 0x00 aka NULL
+				continue
+			case r == 0x0143:
+				r = 0x00ad
+			case r > 0x0100 && r <= 0x0120:
+				r = r - 0x0100
+			case r > 0x0120 && r <= 0x0142:
+				r = r - 0x00a2
+			}
+
+			// NOTE: not using WriteRune here because it writes the UTF-8
+			// encoding of the rune which is _not_ what we want
+			if err := sb.WriteByte(byte(r)); err != nil {
+				return "", err
+			}
+		}
+	}
+
+	slog.Debug("decoded", "ids", ids, "text", sb.String())
+	return sb.String(), nil
+}

model/testdata/inputs.json

@@ -0,0 +1,586 @@
+[
+    {
+        "base64": "aWVkIDQgwr0gbW9udGhz",
+        "ids": [
+            1142,
+            220,
+            19,
+            220,
+            27154,
+            4038
+        ]
+    },
+    {
+        "base64": "RsO8aHJlcg==",
+        "ids": [
+            37,
+            51853,
+            261
+        ]
+    },
+    {
+        "base64": "",
+        "ids": []
+    },
+    {
+        "base64": "IA==",
+        "ids": [
+            220
+        ]
+    },
+    {
+        "base64": "ICA=",
+        "ids": [
+            256
+        ]
+    },
+    {
+        "base64": "ICAg",
+        "ids": [
+            262
+        ]
+    },
+    {
+        "base64": "CQ==",
+        "ids": [
+            197
+        ]
+    },
+    {
+        "base64": "Cg==",
+        "ids": [
+            198
+        ]
+    },
+    {
+        "base64": "Cgo=",
+        "ids": [
+            271
+        ]
+    },
+    {
+        "base64": "CgoK",
+        "ids": [
+            1432
+        ]
+    },
+    {
+        "base64": "CQo=",
+        "ids": [
+            1602
+        ]
+    },
+    {
+        "base64": "SGVsbG8gd29ybGQ=",
+        "ids": [
+            9906,
+            1917
+        ]
+    },
+    {
+        "base64": "IEhlbGxvIHdvcmxk",
+        "ids": [
+            22691,
+            1917
+        ]
+    },
+    {
+        "base64": "SGVsbG8gV29ybGQ=",
+        "ids": [
+            9906,
+            4435
+        ]
+    },
+    {
+        "base64": "IEhlbGxvIFdvcmxk",
+        "ids": [
+            22691,
+            4435
+        ]
+    },
+    {
+        "base64": "IEhlbGxvIFdvcmxkIQ==",
+        "ids": [
+            22691,
+            4435,
+            0
+        ]
+    },
+    {
+        "base64": "SGVsbG8sIHdvcmxkIQ==",
+        "ids": [
+            9906,
+            11,
+            1917,
+            0
+        ]
+    },
+    {
+        "base64": "IEhlbGxvLCB3b3JsZCE=",
+        "ids": [
+            22691,
+            11,
+            1917,
+            0
+        ]
+    },
+    {
+        "base64": "IHRoaXMgaXMg8J+mmS5jcHA=",
+        "ids": [
+            420,
+            374,
+            11410,
+            99,
+            247,
+            13,
+            11055
+        ]
+    },
+    {
+        "base64": "dzA0OCA3dHVpamsgZHNkZmh1",
+        "ids": [
+            86,
+            23904,
+            220,
+            22,
+            83,
+            2005,
+            42908,
+            11729,
+            3013,
+            17156
+        ]
+    },
+    {
+        "base64": "0L3QtdGJ0L4g0L3QsCDQkdGK0LvQs9Cw0YDRgdC60Lg=",
+        "ids": [
+            79862,
+            102118,
+            13373,
+            64571,
+            34694,
+            3114,
+            112203,
+            80112
+        ]
+    },
+    {
+        "base64": "4Z6A4Z624Z6T4Z+L4Z6P4Z+C4Z6W4Z634Z6f4Z+B4Z6f4Z6i4Z624Z6F4Z6B4Z6b4Z6F4Z+B4Z6J",
+        "ids": [
+            21549,
+            222,
+            98629,
+            241,
+            45358,
+            233,
+            21549,
+            237,
+            45358,
+            224,
+            21549,
+            244,
+            21549,
+            115,
+            21549,
+            253,
+            45358,
+            223,
+            21549,
+            253,
+            21549,
+            95,
+            98629,
+            227,
+            21549,
+            223,
+            21549,
+            249,
+            21549,
+            227,
+            45358,
+            223,
+            21549,
+            231
+        ]
+    },
+    {
+        "base64": "8J+agCAobm9ybWFsKSDwn5i24oCN8J+Mq++4jyAobXVsdGlwbGUgZW1vamlzIGNvbmNhdGVuYXRlZCkg4pyFIChvbmx5IGVtb2ppIHRoYXQgaGFzIGl0cyBvd24gdG9rZW4p",
+        "ids": [
+            9468,
+            248,
+            222,
+            320,
+            8416,
+            8,
+            27623,
+            114,
+            102470,
+            9468,
+            234,
+            104,
+            31643,
+            320,
+            36773,
+            100166,
+            98634,
+            8,
+            26602,
+            227,
+            320,
+            3323,
+            43465,
+            430,
+            706,
+            1202,
+            1866,
+            4037,
+            8
+        ]
+    },
+    {
+        "base64": "SGVsbG8=",
+        "ids": [
+            9906
+        ]
+    },
+    {
+        "base64": "IEhlbGxv",
+        "ids": [
+            22691
+        ]
+    },
+    {
+        "base64": "ICBIZWxsbw==",
+        "ids": [
+            220,
+            22691
+        ]
+    },
+    {
+        "base64": "ICAgSGVsbG8=",
+        "ids": [
+            256,
+            22691
+        ]
+    },
+    {
+        "base64": "ICAgIEhlbGxv",
+        "ids": [
+            262,
+            22691
+        ]
+    },
+    {
+        "base64": "ICAgIEhlbGxvCiAgICBIZWxsbw==",
+        "ids": [
+            262,
+            22691,
+            198,
+            262,
+            22691
+        ]
+    },
+    {
+        "base64": "ICg=",
+        "ids": [
+            320
+        ]
+    },
+    {
+        "base64": "CiA9",
+        "ids": [
+            198,
+            284
+        ]
+    },
+    {
+        "base64": "JyBlcmE=",
+        "ids": [
+            6,
+            11639
+        ]
+    },
+    {
+        "base64": "SGVsbG8sIHknYWxsISBIb3cgYXJlIHlvdSDwn5iBID/miJHmg7PlnKhhcHBsZeW3peS9nDEzMTQxNTHlpKnvvZ4=",
+        "ids": [
+            9906,
+            11,
+            379,
+            65948,
+            0,
+            2650,
+            527,
+            499,
+            27623,
+            223,
+            949,
+            37046,
+            101067,
+            19000,
+            23182,
+            102301,
+            9263,
+            18136,
+            16,
+            36827,
+            21909
+        ]
+    },
+    {
+        "base64": "ISEhISEh",
+        "ids": [
+            17523,
+            3001
+        ]
+    },
+    {
+        "base64": "Mw==",
+        "ids": [
+            18
+        ]
+    },
+    {
+        "base64": "MzM=",
+        "ids": [
+            1644
+        ]
+    },
+    {
+        "base64": "MzMz",
+        "ids": [
+            8765
+        ]
+    },
+    {
+        "base64": "MzMzMw==",
+        "ids": [
+            8765,
+            18
+        ]
+    },
+    {
+        "base64": "MzMzMzM=",
+        "ids": [
+            8765,
+            1644
+        ]
+    },
+    {
+        "base64": "MzMzMzMz",
+        "ids": [
+            8765,
+            8765
+        ]
+    },
+    {
+        "base64": "MzMzMzMzMw==",
+        "ids": [
+            8765,
+            8765,
+            18
+        ]
+    },
+    {
+        "base64": "MzMzMzMzMzM=",
+        "ids": [
+            8765,
+            8765,
+            1644
+        ]
+    },
+    {
+        "base64": "MzMzMzMzMzMz",
+        "ids": [
+            8765,
+            8765,
+            8765
+        ]
+    },
+    {
+        "base64": "Q+G7rWEgVmnhu4d0",
+        "ids": [
+            34,
+            91163,
+            101798
+        ]
+    },
+    {
+        "base64": "IGRpc2NhcmRz",
+        "ids": [
+            2624,
+            2402
+        ]
+    },
+    {
+        "base64": "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",
+        "ids": [
+            198,
+            4815,
+            15073,
+            66597,
+            8004,
+            1602,
+            2355,
+            79772,
+            11187,
+            9468,
+            248,
+            222,
+            320,
+            8416,
+            8,
+            27623,
+            114,
+            102470,
+            9468,
+            234,
+            104,
+            31643,
+            320,
+            36773,
+            100166,
+            98634,
+            8,
+            26602,
+            227,
+            11410,
+            99,
+            247,
+            9468,
+            99,
+            247,
+            220,
+            18,
+            220,
+            1644,
+            220,
+            8765,
+            220,
+            8765,
+            18,
+            220,
+            8765,
+            1644,
+            220,
+            8765,
+            8765,
+            220,
+            8765,
+            8765,
+            18,
+            220,
+            8765,
+            8765,
+            1644,
+            220,
+            18,
+            13,
+            18,
+            220,
+            18,
+            497,
+            18,
+            220,
+            18,
+            1131,
+            18,
+            220,
+            21549,
+            222,
+            98629,
+            241,
+            45358,
+            233,
+            21549,
+            237,
+            45358,
+            224,
+            21549,
+            244,
+            21549,
+            115,
+            21549,
+            253,
+            45358,
+            223,
+            21549,
+            253,
+            21549,
+            95,
+            98629,
+            227,
+            76460,
+            223,
+            949,
+            37046,
+            101067,
+            19000,
+            23182,
+            102301,
+            9263,
+            18136,
+            16,
+            36827,
+            21909,
+            56560,
+            54337,
+            19175,
+            102118,
+            13373,
+            64571,
+            34694,
+            3114,
+            112203,
+            80112,
+            3436,
+            106451,
+            14196,
+            14196,
+            74694,
+            3089,
+            3089,
+            29249,
+            17523,
+            3001,
+            27708,
+            7801,
+            358,
+            3077,
+            1027,
+            364,
+            83,
+            820,
+            568,
+            596,
+            1070,
+            11,
+            364,
+            793,
+            499,
+            2771,
+            30,
+            364,
+            44,
+            539,
+            2771,
+            358,
+            3358,
+            1304,
+            433,
+            11,
+            364,
+            35,
+            499,
+            1093,
+            1063,
+            15600,
+            30,
+            1226,
+            6,
+            43712,
+            264,
+            64966,
+            43
+        ]
+    }
+]

parser/parser_test.go

@@ -19,7 +19,7 @@ import (
 	"golang.org/x/text/encoding/unicode"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 func TestParseFileFile(t *testing.T) {
@@ -769,7 +769,7 @@ 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 []llm.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")
@@ -778,7 +778,7 @@ func createBinFile(t *testing.T, kv map[string]any, ti []llm.Tensor) (string, st
 	}
 	defer f.Close()
 
-	if err := llm.WriteGGUF(f, kv, ti); err != nil {
+	if err := ggml.WriteGGUF(f, kv, ti); err != nil {
 		t.Fatal(err)
 	}
 	// Calculate sha256 of file

sample/greedy.go

@@ -0,0 +1,13 @@
+package sample
+
+import "gonum.org/v1/gonum/floats"
+
+type greedy struct{}
+
+func Greedy() Sampler {
+	return greedy{}
+}
+
+func (s greedy) Sample(t []float64) ([]float64, error) {
+	return []float64{float64(floats.MaxIdx(t))}, nil
+}

sample/sample.go

@@ -0,0 +1,74 @@
+package sample
+
+import (
+	"slices"
+
+	"gonum.org/v1/gonum/floats"
+	"gonum.org/v1/gonum/stat/sampleuv"
+)
+
+type Sampler interface {
+	Sample([]float64) ([]float64, error)
+}
+
+type Temperature float64
+
+func (s Temperature) Sample(t []float64) ([]float64, error) {
+	floats.Div(t, slices.Repeat([]float64{float64(s)}, len(t)))
+	return t, nil
+}
+
+type softmax struct{}
+
+func Softmax() Sampler {
+	return softmax{}
+}
+
+func (softmax) Sample(t []float64) ([]float64, error) {
+	return t, nil
+}
+
+type TopK int
+
+func (s TopK) Sample(t []float64) ([]float64, error) {
+	return t, nil
+}
+
+type TopP float32
+
+func (s TopP) Sample(t []float64) ([]float64, error) {
+	return t, nil
+}
+
+type MinP float32
+
+func (s MinP) Sample(t []float64) ([]float64, error) {
+	return t, nil
+}
+
+type weighed struct{}
+
+func Weighed() Sampler {
+	return weighed{}
+}
+
+func (s weighed) Sample(t []float64) ([]float64, error) {
+	w := sampleuv.NewWeighted(t, nil)
+	if v, ok := w.Take(); ok {
+		return []float64{float64(v)}, nil
+	}
+
+	return t, nil
+}
+
+func Sample(floats []float64, samplers ...Sampler) ([]float64, error) {
+	var err error
+	for _, sampler := range samplers {
+		floats, err = sampler.Sample(floats)
+		if err != nil {
+			return nil, err
+		}
+	}
+
+	return floats, nil
+}

server/create.go

@@ -21,8 +21,8 @@ import (
 	"github.com/ollama/ollama/convert"
 	"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/llm"
 	"github.com/ollama/ollama/template"
 	"github.com/ollama/ollama/types/errtypes"
 	"github.com/ollama/ollama/types/model"
@@ -205,7 +205,7 @@ func detectModelTypeFromFiles(files map[string]string) string {
 				return ""
 			}
 
-			ct := llm.DetectGGMLType(buf)
+			ct := ggml.DetectContentType(buf)
 			if ct == "gguf" {
 				return "gguf"
 			}
@@ -271,11 +271,11 @@ func convertFromSafetensors(files map[string]string, baseLayers []*layerGGML, is
 		return nil, err
 	}
 
-	ggml, _, err := llm.DecodeGGML(bin, 0)
+	f, _, err := ggml.Decode(bin, 0)
 	if err != nil {
 		return nil, err
 	}
-	layers := []*layerGGML{{layer, ggml}}
+	layers := []*layerGGML{{layer, f}}
 
 	if !isAdapter {
 		return detectChatTemplate(layers)
@@ -283,13 +283,13 @@ func convertFromSafetensors(files map[string]string, baseLayers []*layerGGML, is
 	return layers, nil
 }
 
-func kvFromLayers(baseLayers []*layerGGML) (llm.KV, error) {
+func kvFromLayers(baseLayers []*layerGGML) (ggml.KV, error) {
 	for _, l := range baseLayers {
 		if l.GGML != nil {
 			return l.KV(), nil
 		}
 	}
-	return llm.KV{}, fmt.Errorf("no base model was found")
+	return ggml.KV{}, fmt.Errorf("no base model was found")
 }
 
 func createModel(r api.CreateRequest, name model.Name, baseLayers []*layerGGML, fn func(resp api.ProgressResponse)) (err error) {
@@ -306,7 +306,7 @@ func createModel(r api.CreateRequest, name model.Name, baseLayers []*layerGGML,
 		if layer.GGML != nil {
 			quantType := strings.ToUpper(cmp.Or(r.Quantize, r.Quantization))
 			if quantType != "" && layer.GGML.Name() == "gguf" && layer.MediaType == "application/vnd.ollama.image.model" {
-				want, err := llm.ParseFileType(quantType)
+				want, err := ggml.ParseFileType(quantType)
 				if err != nil {
 					return err
 				}
@@ -403,7 +403,7 @@ func quantizeLayer(layer *layerGGML, quantizeType string, fn func(resp api.Progr
 	ft := layer.GGML.KV().FileType()
 	fn(api.ProgressResponse{Status: fmt.Sprintf("quantizing %s model to %s", ft, quantizeType)})
 
-	want, err := llm.ParseFileType(quantizeType)
+	want, err := ggml.ParseFileType(quantizeType)
 	if err != nil {
 		return nil, err
 	}
@@ -433,13 +433,13 @@ func quantizeLayer(layer *layerGGML, quantizeType string, fn func(resp api.Progr
 		return nil, err
 	}
 
-	ggml, _, err := llm.DecodeGGML(temp, 0)
+	f, _, err := ggml.Decode(temp, 0)
 	if err != nil {
 		slog.Error(fmt.Sprintf("error decoding ggml: %s\n", err))
 		return nil, err
 	}
 
-	return &layerGGML{newLayer, ggml}, nil
+	return &layerGGML{newLayer, f}, nil
 }
 
 func ggufLayers(digest string, fn func(resp api.ProgressResponse)) ([]*layerGGML, error) {
@@ -475,7 +475,7 @@ func ggufLayers(digest string, fn func(resp api.ProgressResponse)) ([]*layerGGML
 
 	var offset int64
 	for offset < stat.Size() {
-		ggml, n, err := llm.DecodeGGML(blob, 0)
+		f, n, err := ggml.Decode(blob, 0)
 		if errors.Is(err, io.EOF) {
 			break
 		} else if err != nil {
@@ -483,9 +483,9 @@ func ggufLayers(digest string, fn func(resp api.ProgressResponse)) ([]*layerGGML
 		}
 
 		mediatype := "application/vnd.ollama.image.model"
-		if ggml.KV().Kind() == "adapter" {
+		if f.KV().Kind() == "adapter" {
 			mediatype = "application/vnd.ollama.image.adapter"
-		} else if _, ok := ggml.KV()[fmt.Sprintf("%s.vision.block_count", ggml.KV().Architecture())]; ok || ggml.KV().Kind() == "projector" {
+		} else if _, ok := f.KV()[fmt.Sprintf("%s.vision.block_count", f.KV().Architecture())]; ok || f.KV().Kind() == "projector" {
 			mediatype = "application/vnd.ollama.image.projector"
 		}
 
@@ -506,7 +506,7 @@ func ggufLayers(digest string, fn func(resp api.ProgressResponse)) ([]*layerGGML
 			}
 		}
 
-		layers = append(layers, &layerGGML{layer, ggml})
+		layers = append(layers, &layerGGML{layer, f})
 		offset = n
 	}
 

server/images.go

@@ -23,7 +23,7 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/template"
 	"github.com/ollama/ollama/types/model"
@@ -78,21 +78,21 @@ func (m *Model) CheckCapabilities(caps ...Capability) error {
 	for _, cap := range caps {
 		switch cap {
 		case CapabilityCompletion:
-			f, err := os.Open(m.ModelPath)
+			r, err := os.Open(m.ModelPath)
 			if err != nil {
 				slog.Error("couldn't open model file", "error", err)
 				continue
 			}
-			defer f.Close()
+			defer r.Close()
 
 			// TODO(mxyng): decode the GGML into model to avoid doing this multiple times
-			ggml, _, err := llm.DecodeGGML(f, 0)
+			f, _, err := ggml.Decode(r, 0)
 			if err != nil {
 				slog.Error("couldn't decode ggml", "error", err)
 				continue
 			}
 
-			if _, ok := ggml.KV()[fmt.Sprintf("%s.pooling_type", ggml.KV().Architecture())]; ok {
+			if _, ok := f.KV()[fmt.Sprintf("%s.pooling_type", f.KV().Architecture())]; ok {
 				errs = append(errs, errCapabilityCompletion)
 			}
 		case CapabilityTools:

server/model.go

@@ -15,7 +15,7 @@ import (
 	"text/template/parse"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/template"
 	"github.com/ollama/ollama/types/model"
 )
@@ -24,7 +24,7 @@ var intermediateBlobs map[string]string = make(map[string]string)
 
 type layerGGML struct {
 	Layer
-	*llm.GGML
+	*ggml.GGML
 }
 
 func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {
@@ -64,12 +64,12 @@ func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressRe
 			}
 			defer blob.Close()
 
-			ggml, _, err := llm.DecodeGGML(blob, 0)
+			f, _, err := ggml.Decode(blob, 0)
 			if err != nil {
 				return nil, err
 			}
 
-			layers = append(layers, &layerGGML{layer, ggml})
+			layers = append(layers, &layerGGML{layer, f})
 		default:
 			layers = append(layers, &layerGGML{layer, nil})
 		}
@@ -118,7 +118,7 @@ func detectContentType(r io.Reader) (string, error) {
 		return "", err
 	}
 
-	if contentType := llm.DetectGGMLType(b.Bytes()); contentType != "" {
+	if contentType := ggml.DetectContentType(b.Bytes()); contentType != "" {
 		return contentType, nil
 	}
 

server/routes.go

@@ -30,6 +30,7 @@ import (
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/discover"
 	"github.com/ollama/ollama/envconfig"
+	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/model/mllama"
 	"github.com/ollama/ollama/openai"
@@ -860,7 +861,7 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 	return resp, nil
 }
 
-func getKVData(digest string, verbose bool) (llm.KV, error) {
+func getKVData(digest string, verbose bool) (ggml.KV, error) {
 	maxArraySize := 0
 	if verbose {
 		maxArraySize = -1

server/routes_create_test.go

@@ -19,12 +19,12 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 var stream bool = false
 
-func createBinFile(t *testing.T, kv map[string]any, ti []llm.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()))
 
@@ -36,7 +36,7 @@ func createBinFile(t *testing.T, kv map[string]any, ti []llm.Tensor) (string, st
 	}
 	defer f.Close()
 
-	if err := llm.WriteGGUF(f, kv, ti); err != nil {
+	if err := ggml.WriteGGUF(f, kv, ti); err != nil {
 		t.Fatal(err)
 	}
 	// Calculate sha256 of file
@@ -672,7 +672,7 @@ func TestCreateDetectTemplate(t *testing.T) {
 	var s Server
 
 	t.Run("matched", func(t *testing.T) {
-		_, digest := createBinFile(t, llm.KV{
+		_, digest := createBinFile(t, ggml.KV{
 			"tokenizer.chat_template": "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}",
 		}, nil)
 		w := createRequest(t, s.CreateHandler, api.CreateRequest{

server/routes_generate_test.go

@@ -16,6 +16,7 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/discover"
+	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/llm"
 )
 
@@ -45,8 +46,8 @@ func (mockRunner) Tokenize(_ context.Context, s string) (tokens []int, err error
 	return
 }
 
-func newMockServer(mock *mockRunner) func(discover.GpuInfoList, string, *llm.GGML, []string, []string, api.Options, int) (llm.LlamaServer, error) {
-	return func(gpus discover.GpuInfoList, model string, ggml *llm.GGML, projectors, system []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
+func newMockServer(mock *mockRunner) func(discover.GpuInfoList, string, *ggml.GGML, []string, []string, api.Options, int) (llm.LlamaServer, error) {
+	return func(_ discover.GpuInfoList, _ string, _ *ggml.GGML, _, _ []string, _ api.Options, _ int) (llm.LlamaServer, error) {
 		return mock, nil
 	}
 }
@@ -76,7 +77,7 @@ func TestGenerateChat(t *testing.T) {
 			getGpuFn:      discover.GetGPUInfo,
 			getCpuFn:      discover.GetCPUInfo,
 			reschedDelay:  250 * time.Millisecond,
-			loadFn: func(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList, numParallel int) {
+			loadFn: func(req *LlmRequest, _ *ggml.GGML, _ discover.GpuInfoList, _ int) {
 				// add small delay to simulate loading
 				time.Sleep(time.Millisecond)
 				req.successCh <- &runnerRef{
@@ -88,7 +89,7 @@ func TestGenerateChat(t *testing.T) {
 
 	go s.sched.Run(context.TODO())
 
-	_, digest := createBinFile(t, llm.KV{
+	_, digest := createBinFile(t, ggml.KV{
 		"general.architecture":          "llama",
 		"llama.block_count":             uint32(1),
 		"llama.context_length":          uint32(8192),
@@ -98,7 +99,7 @@ func TestGenerateChat(t *testing.T) {
 		"tokenizer.ggml.tokens":         []string{""},
 		"tokenizer.ggml.scores":         []float32{0},
 		"tokenizer.ggml.token_type":     []int32{0},
-	}, []llm.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))},
@@ -154,10 +155,10 @@ func TestGenerateChat(t *testing.T) {
 	})
 
 	t.Run("missing capabilities chat", func(t *testing.T) {
-		_, digest := createBinFile(t, llm.KV{
+		_, digest := createBinFile(t, ggml.KV{
 			"general.architecture": "bert",
 			"bert.pooling_type":    uint32(0),
-		}, []llm.Tensor{})
+		}, []ggml.Tensor{})
 		w := createRequest(t, s.CreateHandler, api.CreateRequest{
 			Model:  "bert",
 			Files:  map[string]string{"bert.gguf": digest},
@@ -612,7 +613,7 @@ func TestGenerate(t *testing.T) {
 			getGpuFn:      discover.GetGPUInfo,
 			getCpuFn:      discover.GetCPUInfo,
 			reschedDelay:  250 * time.Millisecond,
-			loadFn: func(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList, numParallel int) {
+			loadFn: func(req *LlmRequest, _ *ggml.GGML, _ discover.GpuInfoList, _ int) {
 				// add small delay to simulate loading
 				time.Sleep(time.Millisecond)
 				req.successCh <- &runnerRef{
@@ -624,7 +625,7 @@ func TestGenerate(t *testing.T) {
 
 	go s.sched.Run(context.TODO())
 
-	_, digest := createBinFile(t, llm.KV{
+	_, digest := createBinFile(t, ggml.KV{
 		"general.architecture":          "llama",
 		"llama.block_count":             uint32(1),
 		"llama.context_length":          uint32(8192),
@@ -634,7 +635,7 @@ func TestGenerate(t *testing.T) {
 		"tokenizer.ggml.tokens":         []string{""},
 		"tokenizer.ggml.scores":         []float32{0},
 		"tokenizer.ggml.token_type":     []int32{0},
-	}, []llm.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))},
@@ -686,10 +687,10 @@ func TestGenerate(t *testing.T) {
 	})
 
 	t.Run("missing capabilities generate", func(t *testing.T) {
-		_, digest := createBinFile(t, llm.KV{
+		_, digest := createBinFile(t, ggml.KV{
 			"general.architecture": "bert",
 			"bert.pooling_type":    uint32(0),
-		}, []llm.Tensor{})
+		}, []ggml.Tensor{})
 
 		w := createRequest(t, s.CreateHandler, api.CreateRequest{
 			Model:  "bert",

server/routes_test.go

@@ -21,7 +21,7 @@ import (
 	"unicode"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/openai"
 	"github.com/ollama/ollama/types/model"
 	"github.com/ollama/ollama/version"
@@ -654,8 +654,8 @@ func TestShow(t *testing.T) {
 
 	var s Server
 
-	_, digest1 := createBinFile(t, llm.KV{"general.architecture": "test"}, nil)
-	_, digest2 := createBinFile(t, llm.KV{"general.type": "projector", "general.architecture": "clip"}, nil)
+	_, digest1 := createBinFile(t, ggml.KV{"general.architecture": "test"}, nil)
+	_, digest2 := createBinFile(t, ggml.KV{"general.type": "projector", "general.architecture": "clip"}, nil)
 
 	createRequest(t, s.CreateHandler, api.CreateRequest{
 		Name:  "show-model",

server/sched.go

@@ -18,6 +18,7 @@ import (
 	"github.com/ollama/ollama/discover"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
+	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/llm"
 )
 
@@ -41,8 +42,8 @@ type Scheduler struct {
 	loaded   map[string]*runnerRef
 	loadedMu sync.Mutex
 
-	loadFn       func(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList, numParallel int)
-	newServerFn  func(gpus discover.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error)
+	loadFn       func(req *LlmRequest, f *ggml.GGML, gpus discover.GpuInfoList, numParallel int)
+	newServerFn  func(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error)
 	getGpuFn     func() discover.GpuInfoList
 	getCpuFn     func() discover.GpuInfoList
 	reschedDelay time.Duration
@@ -409,7 +410,7 @@ func (pending *LlmRequest) useLoadedRunner(runner *runnerRef, finished chan *Llm
 	}()
 }
 
-func (s *Scheduler) load(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList, numParallel int) {
+func (s *Scheduler) load(req *LlmRequest, f *ggml.GGML, gpus discover.GpuInfoList, numParallel int) {
 	if numParallel < 1 {
 		numParallel = 1
 	}
@@ -417,12 +418,12 @@ func (s *Scheduler) load(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoL
 	if req.sessionDuration != nil {
 		sessionDuration = req.sessionDuration.Duration
 	}
-	llama, err := s.newServerFn(gpus, req.model.ModelPath, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts, numParallel)
+	llama, err := s.newServerFn(gpus, req.model.ModelPath, f, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts, numParallel)
 	if err != nil {
 		// some older models are not compatible with newer versions of llama.cpp
 		// show a generalized compatibility error until there is a better way to
 		// check for model compatibility
-		if errors.Is(err, llm.ErrUnsupportedFormat) || strings.Contains(err.Error(), "failed to load model") {
+		if errors.Is(err, ggml.ErrUnsupportedFormat) || strings.Contains(err.Error(), "failed to load model") {
 			err = fmt.Errorf("%v: this model may be incompatible with your version of Ollama. If you previously pulled this model, try updating it by running `ollama pull %s`", err, req.model.ShortName)
 		}
 		slog.Info("NewLlamaServer failed", "model", req.model.ModelPath, "error", err)
@@ -685,7 +686,7 @@ func (a ByDuration) Less(i, j int) bool {
 // If the model can not be fit fully within the available GPU(s) nil is returned
 // If numParallel is <= 0, this will attempt try to optimize parallelism based on available VRAM, and adjust
 // opts.NumCtx accordingly
-func pickBestFullFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList, numParallel *int) discover.GpuInfoList {
+func pickBestFullFitByLibrary(req *LlmRequest, f *ggml.GGML, gpus discover.GpuInfoList, numParallel *int) discover.GpuInfoList {
 	var estimatedVRAM uint64
 
 	var numParallelToTry []int
@@ -710,7 +711,7 @@ func pickBestFullFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus discover.Gpu
 			req.opts.NumCtx = req.origNumCtx * p
 			if !envconfig.SchedSpread() {
 				for _, g := range sgl {
-					if ok, estimatedVRAM = llm.PredictServerFit([]discover.GpuInfo{g}, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts); ok {
+					if ok, estimatedVRAM = llm.PredictServerFit([]discover.GpuInfo{g}, f, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts); ok {
 						slog.Info("new model will fit in available VRAM in single GPU, loading", "model", req.model.ModelPath, "gpu", g.ID, "parallel", p, "available", g.FreeMemory, "required", format.HumanBytes2(estimatedVRAM))
 						*numParallel = p
 						return []discover.GpuInfo{g}
@@ -726,7 +727,7 @@ func pickBestFullFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus discover.Gpu
 		// Now try all the GPUs
 		for _, p := range numParallelToTry {
 			req.opts.NumCtx = req.origNumCtx * p
-			if ok, estimatedVRAM = llm.PredictServerFit(sgl, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts); ok {
+			if ok, estimatedVRAM = llm.PredictServerFit(sgl, f, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts); ok {
 				slog.Info("new model will fit in available VRAM, loading", "model", req.model.ModelPath, "library", sgl[0].Library, "parallel", p, "required", format.HumanBytes2(estimatedVRAM))
 				*numParallel = p
 				return sgl
@@ -737,7 +738,7 @@ func pickBestFullFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus discover.Gpu
 }
 
 // If multiple Libraries are detected, pick the Library which loads the most layers for the model
-func pickBestPartialFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList, numParallel *int) discover.GpuInfoList {
+func pickBestPartialFitByLibrary(req *LlmRequest, f *ggml.GGML, gpus discover.GpuInfoList, numParallel *int) discover.GpuInfoList {
 	if *numParallel <= 0 {
 		*numParallel = 1
 		req.opts.NumCtx = req.origNumCtx
@@ -749,7 +750,7 @@ func pickBestPartialFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus discover.
 	var bestEstimate uint64
 	var bestFit int
 	for i, gl := range byLibrary {
-		_, estimatedVRAM := llm.PredictServerFit(gl, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts)
+		_, estimatedVRAM := llm.PredictServerFit(gl, f, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts)
 		if estimatedVRAM > bestEstimate {
 			bestEstimate = estimatedVRAM
 			bestFit = i
@@ -822,9 +823,9 @@ func (s *Scheduler) expireRunner(model *Model) {
 
 // If other runners are loaded, make sure the pending request will fit in system memory
 // If not, pick a runner to unload, else return nil and the request can be loaded
-func (s *Scheduler) maybeFindCPURunnerToUnload(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList) *runnerRef {
+func (s *Scheduler) maybeFindCPURunnerToUnload(req *LlmRequest, f *ggml.GGML, gpus discover.GpuInfoList) *runnerRef {
 	slog.Debug("evaluating if CPU model load will fit in available system memory")
-	estimate := llm.EstimateGPULayers(gpus, ggml, req.model.ProjectorPaths, req.opts)
+	estimate := llm.EstimateGPULayers(gpus, f, req.model.ProjectorPaths, req.opts)
 	if estimate.TotalSize <= gpus[0].FreeMemory {
 		slog.Debug("cpu inference mode, model fits in available system memory", "model", format.HumanBytes2(estimate.TotalSize), "available", format.HumanBytes2(gpus[0].FreeMemory))
 		return nil

server/sched_test.go

@@ -15,6 +15,7 @@ import (
 	"github.com/ollama/ollama/app/lifecycle"
 	"github.com/ollama/ollama/discover"
 	"github.com/ollama/ollama/format"
+	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/llm"
 )
 
@@ -37,7 +38,7 @@ func TestLoad(t *testing.T) {
 	ctx, done := context.WithTimeout(context.Background(), 20*time.Millisecond)
 	defer done()
 	s := InitScheduler(ctx)
-	var ggml *llm.GGML // value not used in tests
+	var f *ggml.GGML // value not used in tests
 	req := &LlmRequest{
 		ctx:             ctx,
 		model:           &Model{ModelPath: "foo"},
@@ -47,11 +48,11 @@ func TestLoad(t *testing.T) {
 		sessionDuration: &api.Duration{Duration: 2 * time.Second},
 	}
 	// Fail to load model first
-	s.newServerFn = func(gpus discover.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
+	s.newServerFn = func(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
 		return nil, errors.New("something failed to load model blah")
 	}
 	gpus := discover.GpuInfoList{}
-	s.load(req, ggml, gpus, 0)
+	s.load(req, f, gpus, 0)
 	require.Empty(t, req.successCh)
 	require.Len(t, req.errCh, 1)
 	s.loadedMu.Lock()
@@ -61,10 +62,10 @@ func TestLoad(t *testing.T) {
 	require.Contains(t, err.Error(), "this model may be incompatible")
 
 	server := &mockLlm{estimatedVRAM: 10, estimatedVRAMByGPU: map[string]uint64{}}
-	s.newServerFn = func(gpus discover.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
+	s.newServerFn = func(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
 		return server, nil
 	}
-	s.load(req, ggml, gpus, 0)
+	s.load(req, f, gpus, 0)
 	select {
 	case err := <-req.errCh:
 		require.NoError(t, err)
@@ -78,7 +79,7 @@ func TestLoad(t *testing.T) {
 
 	req.model.ModelPath = "dummy_model_path"
 	server.waitResp = errors.New("wait failure")
-	s.load(req, ggml, gpus, 0)
+	s.load(req, f, gpus, 0)
 	select {
 	case err := <-req.errCh:
 		require.Contains(t, err.Error(), "wait failure")
@@ -99,10 +100,10 @@ type reqBundle struct {
 	ctxDone func()
 	srv     *mockLlm
 	req     *LlmRequest
-	ggml    *llm.GGML
+	f       *ggml.GGML
 }
 
-func (scenario *reqBundle) newServer(gpus discover.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
+func (scenario *reqBundle) newServer(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
 	return scenario.srv, nil
 }
 
@@ -115,7 +116,7 @@ func newScenarioRequest(t *testing.T, ctx context.Context, modelName string, est
 	require.NoError(t, err)
 	defer f.Close()
 
-	require.NoError(t, llm.WriteGGUF(f, llm.KV{
+	require.NoError(t, ggml.WriteGGUF(f, ggml.KV{
 		"general.architecture":          "llama",
 		"llama.context_length":          uint32(32),
 		"llama.embedding_length":        uint32(4096),
@@ -125,7 +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},
-	}, []llm.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))},
 	}))
@@ -133,7 +134,7 @@ func newScenarioRequest(t *testing.T, ctx context.Context, modelName string, est
 
 	fname := f.Name()
 	model := &Model{Name: modelName, ModelPath: fname}
-	b.ggml, err = llm.LoadModel(model.ModelPath, 0)
+	b.f, err = llm.LoadModel(model.ModelPath, 0)
 	require.NoError(t, err)
 
 	if duration == nil {
@@ -174,7 +175,7 @@ func TestRequestsSameModelSameRequest(t *testing.T) {
 	a := newScenarioRequest(t, ctx, "ollama-model-1", 10, &api.Duration{Duration: 5 * time.Millisecond})
 	b := newScenarioRequest(t, ctx, "ollama-model-1", 11, &api.Duration{Duration: 0})
 	b.req.model = a.req.model
-	b.ggml = a.ggml
+	b.f = a.f
 
 	s.newServerFn = a.newServer
 	slog.Info("a")
@@ -218,7 +219,7 @@ func TestRequestsSimpleReloadSameModel(t *testing.T) {
 	b := newScenarioRequest(t, ctx, "ollama-model-1", 20, &api.Duration{Duration: 5 * time.Millisecond})
 	tmpModel := *a.req.model
 	b.req.model = &tmpModel
-	b.ggml = a.ggml
+	b.f = a.f
 
 	s.newServerFn = a.newServer
 	slog.Info("a")
@@ -419,13 +420,13 @@ func TestExpireRunner(t *testing.T) {
 		sessionDuration: &api.Duration{Duration: 2 * time.Minute},
 	}
 
-	var ggml *llm.GGML
+	var f *ggml.GGML
 	gpus := discover.GpuInfoList{}
 	server := &mockLlm{estimatedVRAM: 10, estimatedVRAMByGPU: map[string]uint64{}}
-	s.newServerFn = func(gpus discover.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
+	s.newServerFn = func(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
 		return server, nil
 	}
-	s.load(req, ggml, gpus, 0)
+	s.load(req, f, gpus, 0)
 
 	select {
 	case err := <-req.errCh:
@@ -729,9 +730,9 @@ func TestHomogeneousGPUs(t *testing.T) {
 	}
 	s.getCpuFn = getCpuFn
 	a := newScenarioRequest(t, ctx, "ollama-model-1", 10, &api.Duration{Duration: 5 * time.Millisecond})
-	s.newServerFn = func(gpus discover.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
+	s.newServerFn = func(gpus discover.GpuInfoList, model string, f *ggml.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
 		require.Len(t, gpus, 1)
-		return a.newServer(gpus, model, ggml, adapters, projectors, opts, numParallel)
+		return a.newServer(gpus, model, f, adapters, projectors, opts, numParallel)
 	}
 	slog.Info("a")
 	s.pendingReqCh <- a.req

template/template_test.go

@@ -14,7 +14,7 @@ import (
 	"github.com/google/go-cmp/cmp"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 func TestNamed(t *testing.T) {
@@ -33,7 +33,7 @@ func TestNamed(t *testing.T) {
 
 		for k, v := range ss {
 			t.Run(k, func(t *testing.T) {
-				kv := llm.KV{"tokenizer.chat_template": v}
+				kv := ggml.KV{"tokenizer.chat_template": v}
 				s := kv.ChatTemplate()
 				r, err := Named(s)
 				if err != nil {