Move Go code out of llm package

convert/convert.go

@@ -9,7 +9,7 @@ import (
 	"log/slog"
 	"strings"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fileutils"
 )
 
 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) fileutils.KV {
+	kv := fileutils.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() fileutils.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 := fileutils.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 fileutils.KV, ts []fileutils.Tensor) error {
+	return fileutils.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 fileutils.KV, ts []fileutils.Tensor) error {
+	return fileutils.WriteGGUF(ws, kv, ts)
 }
 
 type ModelConverter interface {
 	// KV maps parameters to LLM key-values
-	KV(*Tokenizer) llm.KV
+	KV(*Tokenizer) fileutils.KV
 	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
-	Tensors([]Tensor) []llm.Tensor
+	Tensors([]Tensor) []fileutils.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, fileutils.KV, []fileutils.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(fileutils.KV) fileutils.KV
 	// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
-	Tensors([]Tensor) []llm.Tensor
+	Tensors([]Tensor) []fileutils.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, fileutils.KV, []fileutils.Tensor) error
 }
 
-func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV llm.KV) error {
+func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV fileutils.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/fileutils"
 )
 
 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) fileutils.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) []fileutils.Tensor {
+	var out []fileutils.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, fileutils.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/fileutils"
 )
 
 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) fileutils.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) []fileutils.Tensor {
+	var out []fileutils.Tensor
 	for _, t := range ts {
 		if strings.HasSuffix(t.Name(), "_norm.weight") {
 			t.SetRepacker(p.addOne)
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, fileutils.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma2.go

@@ -1,7 +1,7 @@
 package convert
 
 import (
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fileutils"
 )
 
 type gemma2Model struct {
@@ -11,7 +11,7 @@ type gemma2Model struct {
 	FinalLogitSoftcap     float32 `json:"final_logit_softcapping"`
 }
 
-func (p *gemma2Model) KV(t *Tokenizer) llm.KV {
+func (p *gemma2Model) KV(t *Tokenizer) fileutils.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/fileutils"
 )
 
 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 fileutils.KV) fileutils.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) []fileutils.Tensor {
+	var out []fileutils.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, fileutils.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/fileutils"
 )
 
 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) fileutils.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) []fileutils.Tensor {
+	var out []fileutils.Tensor
 
 	if p.RopeScaling.factors != nil {
-		out = append(out, llm.Tensor{
+		out = append(out, fileutils.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, fileutils.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/fileutils"
 )
 
 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 fileutils.KV) fileutils.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) []fileutils.Tensor {
+	var out []fileutils.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, fileutils.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/fileutils"
 )
 
 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) fileutils.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) []fileutils.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 []fileutils.Tensor
 	for n, e := range experts {
 		// TODO(mxyng): sanity check experts
-		out = append(out, llm.Tensor{
+		out = append(out, fileutils.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/fileutils"
 )
 
 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) fileutils.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) []fileutils.Tensor {
 	var addRopeFactors sync.Once
 
-	out := make([]llm.Tensor, 0, len(ts)+2)
+	out := make([]fileutils.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, fileutils.Tensor{
 					Name:     "rope_factors_long.weight",
 					Kind:     0,
 					Shape:    []uint64{uint64(len(p.RopeScaling.LongFactor))},
 					WriterTo: p.RopeScaling.LongFactor,
-				}, llm.Tensor{
+				}, fileutils.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, fileutils.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/fileutils"
 )
 
 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, fileutils.KV, *fileutils.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 := fileutils.DecodeGGML(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 fileutils.KV, tensors *fileutils.Tensors) map[string]string {
 	actual := make(map[string]string)
 	for k, v := range kv {
 		if s, ok := v.(json.Marshaler); !ok {
@@ -330,7 +330,7 @@ func TestConvertAdapter(t *testing.T) {
 			}
 			defer r.Close()
 
-			m, _, err := llm.DecodeGGML(r, math.MaxInt)
+			m, _, err := fileutils.DecodeGGML(r, math.MaxInt)
 			if err != nil {
 				t.Fatal(err)
 			}

discover/README.md

@@ -0,0 +1,3 @@
+# `discover`
+
+This package is responsible for discovering information about the system and the capabilities to run LLM.  This includes GPU and CPU discovery so the optimal runner can be chosen for a given model.  The ollama scheduler relies on up-to-date available memory information, so this package provides the ability to refresh free memory as efficiently as possible.

fileutils/README.md

@@ -0,0 +1,3 @@
+# `modelfile`
+
+This package provides utilities for loading and inspecting model files

llm/filetype.go → fileutils/filetype.go

@@ -1,9 +1,11 @@
-package llm
+package fileutils
 
 import "fmt"
 
 type fileType uint32
 
+// TODO this should map over to the GGML CGO enum type
+
 const (
 	fileTypeF32 fileType = iota
 	fileTypeF16

llm/ggla.go → fileutils/ggla.go

@@ -1,4 +1,4 @@
-package llm
+package fileutils
 
 import (
 	"encoding/binary"

llm/ggml.go → fileutils/ggml.go

@@ -1,10 +1,11 @@
-package llm
+package fileutils
 
 import (
 	"encoding/binary"
 	"errors"
 	"fmt"
 	"io"
+	"os"
 	"slices"
 	"strings"
 	"sync"
@@ -488,3 +489,23 @@ func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload ui
 
 	return
 }
+
+// LoadModel will load a model from disk. The model must be in the GGML format.
+//
+// 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) {
+	if _, err := os.Stat(model); err != nil {
+		return nil, err
+	}
+
+	f, err := os.Open(model)
+	if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	ggml, _, err := DecodeGGML(f, maxArraySize)
+	return ggml, err
+}

fileutils/ggml_test.go

@@ -0,0 +1 @@
+package fileutils

llm/gguf.go → fileutils/gguf.go

@@ -1,4 +1,4 @@
-package llm
+package fileutils
 
 import (
 	"bytes"

llm/memory.go → fileutils/memory.go

@@ -1,4 +1,4 @@
-package llm
+package fileutils
 
 import (
 	"fmt"
@@ -329,7 +329,7 @@ func EstimateGPULayers(gpus []discover.GpuInfo, ggml *GGML, projectors []string,
 	return estimate
 }
 
-func (m MemoryEstimate) log() {
+func (m MemoryEstimate) Log() {
 	overhead := envconfig.GpuOverhead()
 
 	log := slog.With()

llm/memory_test.go → fileutils/memory_test.go

@@ -1,4 +1,4 @@
-package llm
+package fileutils
 
 import (
 	"bytes"

llm/ggml_test.go

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

runners/README.md

@@ -0,0 +1,3 @@
+# `runners`
+
+Ollama uses a subprocess model to run one or more child processes to load the LLM.  On some platforms (Linux non-containerized, MacOS) these executables are carried as payloads inside the main executable via the ../build package.  Extraction and discovery of these runners at runtime is implemented in this package.  This package also provides the abstraction to communicate with these subprocesses. 

runners/common.go

@@ -2,6 +2,7 @@ package runners
 
 import (
 	"compress/gzip"
+	"context"
 	"errors"
 	"fmt"
 	"io"
@@ -15,9 +16,11 @@ import (
 	"strings"
 	"sync"
 	"syscall"
+	"time"
 
 	"golang.org/x/sync/errgroup"
 
+	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/discover"
 	"github.com/ollama/ollama/envconfig"
 )
@@ -31,6 +34,36 @@ var (
 	runnersDir = ""
 )
 
+type CompletionRequest struct {
+	Prompt  string
+	Format  string
+	Images  []ImageData
+	Options *api.Options
+}
+
+type CompletionResponse struct {
+	Content            string
+	DoneReason         string
+	Done               bool
+	PromptEvalCount    int
+	PromptEvalDuration time.Duration
+	EvalCount          int
+	EvalDuration       time.Duration
+}
+
+type LLMServer interface {
+	Ping(ctx context.Context) error
+	WaitUntilRunning(ctx context.Context) error
+	Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error
+	Embedding(ctx context.Context, input string) ([]float32, error)
+	Tokenize(ctx context.Context, content string) ([]int, error)
+	Detokenize(ctx context.Context, tokens []int) (string, error)
+	Close() error
+	EstimatedVRAM() uint64 // Total VRAM across all GPUs
+	EstimatedTotal() uint64
+	EstimatedVRAMByGPU(gpuID string) uint64
+}
+
 // Return the location where runners are stored
 // If runners are payloads, this will either extract them
 // or refresh them if any have disappeared due to tmp cleaners

llm/server.go → runners/llama-server.go

@@ -1,4 +1,4 @@
-package llm
+package runners
 
 import (
 	"bufio"
@@ -28,24 +28,11 @@ import (
 	"github.com/ollama/ollama/build"
 	"github.com/ollama/ollama/discover"
 	"github.com/ollama/ollama/envconfig"
+	"github.com/ollama/ollama/fileutils"
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/llama"
-	"github.com/ollama/ollama/runners"
 )
 
-type LlamaServer interface {
-	Ping(ctx context.Context) error
-	WaitUntilRunning(ctx context.Context) error
-	Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error
-	Embedding(ctx context.Context, input string) ([]float32, error)
-	Tokenize(ctx context.Context, content string) ([]int, error)
-	Detokenize(ctx context.Context, tokens []int) (string, error)
-	Close() error
-	EstimatedVRAM() uint64 // Total VRAM across all GPUs
-	EstimatedTotal() uint64
-	EstimatedVRAMByGPU(gpuID string) uint64
-}
-
 // llmServer is an instance of the llama.cpp server
 type llmServer struct {
 	port        int
@@ -58,7 +45,7 @@ type llmServer struct {
 	modelLock   sync.Mutex   // Temporary until we switch fully to Go server
 	model       *llama.Model // If non-nil, the runner is a new Go server
 
-	estimate    MemoryEstimate
+	estimate    fileutils.MemoryEstimate
 	totalLayers uint64
 	// gpuCount     int
 	gpus         discover.GpuInfoList // Recorded just before the model loaded, free space will be incorrect
@@ -68,32 +55,12 @@ type llmServer struct {
 	sem *semaphore.Weighted
 }
 
-// LoadModel will load a model from disk. The model must be in the GGML format.
-//
-// 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) {
-	if _, err := os.Stat(model); err != nil {
-		return nil, err
-	}
-
-	f, err := os.Open(model)
-	if err != nil {
-		return nil, err
-	}
-	defer f.Close()
-
-	ggml, _, err := DecodeGGML(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) {
+func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *fileutils.GGML, adapters, projectors []string, opts api.Options, numParallel int) (LLMServer, error) {
 	var err error
 	var cpuRunner string
-	var estimate MemoryEstimate
+	var estimate fileutils.MemoryEstimate
 	var systemTotalMemory uint64
 	var systemFreeMemory uint64
 	var systemSwapFreeMemory uint64
@@ -109,10 +76,10 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 		gpus = discover.GetCPUInfo()
 	}
 	if len(gpus) == 1 && gpus[0].Library == "cpu" {
-		cpuRunner = runners.ServerForCpu()
-		estimate = EstimateGPULayers(gpus, ggml, projectors, opts)
+		cpuRunner = ServerForCpu()
+		estimate = fileutils.EstimateGPULayers(gpus, ggml, projectors, opts)
 	} else {
-		estimate = EstimateGPULayers(gpus, ggml, projectors, opts)
+		estimate = fileutils.EstimateGPULayers(gpus, ggml, projectors, opts)
 
 		switch {
 		case gpus[0].Library == "metal" && estimate.VRAMSize > systemTotalMemory:
@@ -121,7 +88,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 			opts.NumGPU = 0
 		case gpus[0].Library != "metal" && estimate.Layers == 0:
 			// Don't bother loading into the GPU if no layers can fit
-			cpuRunner = runners.ServerForCpu()
+			cpuRunner = ServerForCpu()
 			gpus = discover.GetCPUInfo()
 		case opts.NumGPU < 0 && estimate.Layers > 0 && gpus[0].Library != "cpu":
 			opts.NumGPU = estimate.Layers
@@ -139,7 +106,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 		}
 	}
 
-	estimate.log()
+	estimate.Log()
 
 	// Loop through potential servers
 	finalErr := errors.New("no suitable llama servers found")
@@ -148,12 +115,12 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 		return nil, errors.New("ollama supports only one lora adapter, but multiple were provided")
 	}
 
-	rDir, err := runners.Refresh(build.EmbedFS)
+	rDir, err := Refresh(build.EmbedFS)
 	if err != nil {
 		return nil, err
 	}
 
-	availableServers := runners.GetAvailableServers(rDir)
+	availableServers := GetAvailableServers(rDir)
 	if len(availableServers) == 0 {
 		return nil, finalErr
 	}
@@ -161,7 +128,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 	if cpuRunner != "" {
 		servers = []string{cpuRunner}
 	} else {
-		servers = runners.ServersForGpu(gpus[0]) // All GPUs in the list are matching Library and Variant
+		servers = ServersForGpu(gpus[0]) // All GPUs in the list are matching Library and Variant
 	}
 	demandLib := envconfig.LLMLibrary()
 	if demandLib != "" {
@@ -325,7 +292,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 		_, err := os.Stat(server)
 		if errors.Is(err, os.ErrNotExist) {
 			slog.Warn("llama server disappeared, reinitializing payloads", "path", server, "error", err)
-			_, err = runners.Refresh(build.EmbedFS)
+			_, err = Refresh(build.EmbedFS)
 			if err != nil {
 				slog.Warn("failed to reinitialize payloads", "error", err)
 				return nil, err
@@ -673,23 +640,6 @@ type completion struct {
 	}
 }
 
-type CompletionRequest struct {
-	Prompt  string
-	Format  string
-	Images  []ImageData
-	Options *api.Options
-}
-
-type CompletionResponse struct {
-	Content            string
-	DoneReason         string
-	Done               bool
-	PromptEvalCount    int
-	PromptEvalDuration time.Duration
-	EvalCount          int
-	EvalDuration       time.Duration
-}
-
 func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error {
 	if err := s.sem.Acquire(ctx, 1); err != nil {
 		slog.Error("Failed to acquire semaphore", "error", err)

llm/status.go → runners/llama-status.go

@@ -1,4 +1,4 @@
-package llm
+package runners
 
 import (
 	"bytes"

llm/llm_darwin.go → runners/llama_darwin.go

@@ -1,4 +1,4 @@
-package llm
+package runners
 
 import (
 	"syscall"

llm/llm_linux.go → runners/llama_linux.go

@@ -1,4 +1,4 @@
-package llm
+package runners
 
 import (
 	"syscall"

llm/llm_windows.go → runners/llama_windows.go

@@ -1,4 +1,4 @@
-package llm
+package runners
 
 import (
 	"syscall"

server/images.go

@@ -25,9 +25,9 @@ import (
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/auth"
 	"github.com/ollama/ollama/envconfig"
+	"github.com/ollama/ollama/fileutils"
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/llama"
-	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/template"
 	"github.com/ollama/ollama/types/errtypes"
@@ -91,7 +91,7 @@ func (m *Model) CheckCapabilities(caps ...Capability) error {
 			defer f.Close()
 
 			// TODO(mxyng): decode the GGML into model to avoid doing this multiple times
-			ggml, _, err := llm.DecodeGGML(f, 0)
+			ggml, _, err := fileutils.DecodeGGML(f, 0)
 			if err != nil {
 				slog.Error("couldn't decode ggml", "error", err)
 				continue
@@ -431,7 +431,7 @@ func CreateModel(ctx context.Context, name model.Name, modelFileDir, quantizatio
 					baseLayer.MediaType == "application/vnd.ollama.image.model" &&
 					baseLayer.GGML != nil &&
 					baseLayer.GGML.Name() == "gguf" {
-					want, err := llm.ParseFileType(quantization)
+					want, err := fileutils.ParseFileType(quantization)
 					if err != nil {
 						return err
 					}
@@ -467,7 +467,7 @@ func CreateModel(ctx context.Context, name model.Name, modelFileDir, quantizatio
 							return err
 						}
 
-						ggml, _, err := llm.DecodeGGML(temp, 0)
+						ggml, _, err := fileutils.DecodeGGML(temp, 0)
 						if err != nil {
 							return err
 						}

server/model.go

@@ -18,7 +18,7 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/convert"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fileutils"
 	"github.com/ollama/ollama/template"
 	"github.com/ollama/ollama/types/model"
 )
@@ -27,7 +27,7 @@ var intermediateBlobs map[string]string = make(map[string]string)
 
 type layerGGML struct {
 	Layer
-	*llm.GGML
+	*fileutils.GGML
 }
 
 func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {
@@ -67,7 +67,7 @@ func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressRe
 			}
 			defer blob.Close()
 
-			ggml, _, err := llm.DecodeGGML(blob, 0)
+			ggml, _, err := fileutils.DecodeGGML(blob, 0)
 			if err != nil {
 				return nil, err
 			}
@@ -112,7 +112,7 @@ func parseFromZipFile(_ context.Context, command string, baseLayers []*layerGGML
 
 	switch command {
 	case "adapter":
-		var baseModel *llm.GGML
+		var baseModel *fileutils.GGML
 		for _, l := range baseLayers {
 			if l.GGML != nil {
 				baseModel = l.GGML
@@ -150,7 +150,7 @@ func parseFromZipFile(_ context.Context, command string, baseLayers []*layerGGML
 	}
 	defer bin.Close()
 
-	ggml, _, err := llm.DecodeGGML(bin, 0)
+	ggml, _, err := fileutils.DecodeGGML(bin, 0)
 	if err != nil {
 		return nil, err
 	}
@@ -184,7 +184,7 @@ func parseFromFile(ctx context.Context, command string, baseLayers []*layerGGML,
 
 	var offset int64
 	for offset < stat.Size() {
-		ggml, n, err := llm.DecodeGGML(file, 0)
+		ggml, n, err := fileutils.DecodeGGML(file, 0)
 		if errors.Is(err, io.EOF) {
 			break
 		} else if err != nil {
@@ -263,7 +263,7 @@ func detectContentType(r io.Reader) (string, error) {
 		return "", err
 	}
 
-	if contentType := llm.DetectGGMLType(b.Bytes()); contentType != "" {
+	if contentType := fileutils.DetectGGMLType(b.Bytes()); contentType != "" {
 		return contentType, nil
 	}
 

server/model_test.go

@@ -13,7 +13,7 @@ import (
 	"github.com/google/go-cmp/cmp"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fileutils"
 	"github.com/ollama/ollama/template"
 )
 
@@ -147,7 +147,7 @@ func TestParseFromFileFromLayer(t *testing.T) {
 		t.Fatalf("failed to open file: %v", err)
 	}
 	defer file.Close()
-	if err := llm.WriteGGUF(file, llm.KV{"general.architecture": "gemma"}, []llm.Tensor{}); err != nil {
+	if err := fileutils.WriteGGUF(file, fileutils.KV{"general.architecture": "gemma"}, []fileutils.Tensor{}); err != nil {
 		t.Fatalf("failed to write gguf: %v", err)
 	}
 
@@ -200,7 +200,7 @@ func TestParseLayerFromCopy(t *testing.T) {
 	defer file2.Close()
 
 	for range 5 {
-		if err := llm.WriteGGUF(file2, llm.KV{"general.architecture": "gemma"}, []llm.Tensor{}); err != nil {
+		if err := fileutils.WriteGGUF(file2, fileutils.KV{"general.architecture": "gemma"}, []fileutils.Tensor{}); err != nil {
 			t.Fatalf("failed to write gguf: %v", err)
 		}
 	}

server/prompt.go

@@ -10,7 +10,7 @@ import (
 	"strings"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/runners"
 	"github.com/ollama/ollama/server/imageproc"
 	"github.com/ollama/ollama/template"
 )
@@ -22,7 +22,7 @@ var errTooManyImages = errors.New("vision model only supports a single image per
 // chatPrompt accepts a list of messages and returns the prompt and images that should be used for the next chat turn.
 // chatPrompt truncates any messages that exceed the context window of the model, making sure to always include 1) the
 // latest message and 2) system messages
-func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.Options, msgs []api.Message, tools []api.Tool) (prompt string, images []llm.ImageData, _ error) {
+func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.Options, msgs []api.Message, tools []api.Tool) (prompt string, images []runners.ImageData, _ error) {
 	var system []api.Message
 
 	isMllama := checkMllamaModelFamily(m)
@@ -90,7 +90,7 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
 					return "", nil, err
 				}
 
-				imgData := llm.ImageData{
+				imgData := runners.ImageData{
 					Data:          buf.Bytes(),
 					AspectRatioID: aspectRatioID,
 				}
@@ -105,7 +105,7 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
 			prefix := ""
 			prompt := msg.Content
 			for _, i := range msg.Images {
-				imgData := llm.ImageData{
+				imgData := runners.ImageData{
 					ID:   len(images),
 					Data: i,
 				}

server/routes.go

@@ -29,7 +29,7 @@ import (
 	"github.com/ollama/ollama/build"
 	"github.com/ollama/ollama/discover"
 	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fileutils"
 	"github.com/ollama/ollama/openai"
 	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/runners"
@@ -78,7 +78,7 @@ func modelOptions(model *Model, requestOpts map[string]interface{}) (api.Options
 
 // scheduleRunner schedules a runner after validating inputs such as capabilities and model options.
 // It returns the allocated runner, model instance, and consolidated options if successful and error otherwise.
-func (s *Server) scheduleRunner(ctx context.Context, name string, caps []Capability, requestOpts map[string]any, keepAlive *api.Duration) (llm.LlamaServer, *Model, *api.Options, error) {
+func (s *Server) scheduleRunner(ctx context.Context, name string, caps []Capability, requestOpts map[string]any, keepAlive *api.Duration) (runners.LLMServer, *Model, *api.Options, error) {
 	if name == "" {
 		return nil, nil, nil, fmt.Errorf("model %w", errRequired)
 	}
@@ -187,9 +187,9 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		return
 	}
 
-	images := make([]llm.ImageData, len(req.Images))
+	images := make([]runners.ImageData, len(req.Images))
 	for i := range req.Images {
-		images[i] = llm.ImageData{ID: i, Data: req.Images[i]}
+		images[i] = runners.ImageData{ID: i, Data: req.Images[i]}
 	}
 
 	prompt := req.Prompt
@@ -255,12 +255,12 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		// TODO (jmorganca): avoid building the response twice both here and below
 		var sb strings.Builder
 		defer close(ch)
-		if err := r.Completion(c.Request.Context(), llm.CompletionRequest{
+		if err := r.Completion(c.Request.Context(), runners.CompletionRequest{
 			Prompt:  prompt,
 			Images:  images,
 			Format:  req.Format,
 			Options: opts,
-		}, func(cr llm.CompletionResponse) {
+		}, func(cr runners.CompletionResponse) {
 			res := api.GenerateResponse{
 				Model:      req.Model,
 				CreatedAt:  time.Now().UTC(),
@@ -639,7 +639,7 @@ func (s *Server) CreateHandler(c *gin.Context) {
 	}
 
 	if r.Path == "" && r.Modelfile == "" {
-		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "path or modelfile are required"})
+		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "path or fileutils are required"})
 		return
 	}
 
@@ -647,7 +647,7 @@ func (s *Server) CreateHandler(c *gin.Context) {
 	if r.Path != "" && r.Modelfile == "" {
 		f, err := os.Open(r.Path)
 		if err != nil {
-			c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("error reading modelfile: %s", err)})
+			c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("error reading fileutils: %s", err)})
 			return
 		}
 		defer f.Close()
@@ -851,12 +851,12 @@ 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) (fileutils.KV, error) {
 	maxArraySize := 0
 	if verbose {
 		maxArraySize = -1
 	}
-	kvData, err := llm.LoadModel(digest, maxArraySize)
+	kvData, err := fileutils.LoadModel(digest, maxArraySize)
 	if err != nil {
 		return nil, err
 	}
@@ -1436,12 +1436,12 @@ func (s *Server) ChatHandler(c *gin.Context) {
 	ch := make(chan any)
 	go func() {
 		defer close(ch)
-		if err := r.Completion(c.Request.Context(), llm.CompletionRequest{
+		if err := r.Completion(c.Request.Context(), runners.CompletionRequest{
 			Prompt:  prompt,
 			Images:  images,
 			Format:  req.Format,
 			Options: opts,
-		}, func(r llm.CompletionResponse) {
+		}, func(r runners.CompletionResponse) {
 			res := api.ChatResponse{
 				Model:      req.Model,
 				CreatedAt:  time.Now().UTC(),

server/routes_create_test.go

@@ -16,12 +16,12 @@ import (
 	"github.com/gin-gonic/gin"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fileutils"
 )
 
 var stream bool = false
 
-func createBinFile(t *testing.T, kv map[string]any, ti []llm.Tensor) string {
+func createBinFile(t *testing.T, kv map[string]any, ti []fileutils.Tensor) string {
 	t.Helper()
 
 	f, err := os.CreateTemp(t.TempDir(), "")
@@ -30,7 +30,7 @@ func createBinFile(t *testing.T, kv map[string]any, ti []llm.Tensor) string {
 	}
 	defer f.Close()
 
-	if err := llm.WriteGGUF(f, kv, ti); err != nil {
+	if err := fileutils.WriteGGUF(f, kv, ti); err != nil {
 		t.Fatal(err)
 	}
 
@@ -581,7 +581,7 @@ func TestCreateDetectTemplate(t *testing.T) {
 	t.Run("matched", func(t *testing.T) {
 		w := createRequest(t, s.CreateHandler, api.CreateRequest{
 			Name: "test",
-			Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, llm.KV{
+			Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, fileutils.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)),
 			Stream: &stream,

server/routes_generate_test.go

@@ -16,18 +16,19 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/discover"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fileutils"
+	"github.com/ollama/ollama/runners"
 )
 
 type mockRunner struct {
-	llm.LlamaServer
+	runners.LLMServer
 
 	// CompletionRequest is only valid until the next call to Completion
-	llm.CompletionRequest
-	llm.CompletionResponse
+	runners.CompletionRequest
+	runners.CompletionResponse
 }
 
-func (m *mockRunner) Completion(_ context.Context, r llm.CompletionRequest, fn func(r llm.CompletionResponse)) error {
+func (m *mockRunner) Completion(_ context.Context, r runners.CompletionRequest, fn func(r runners.CompletionResponse)) error {
 	m.CompletionRequest = r
 	fn(m.CompletionResponse)
 	return nil
@@ -41,8 +42,8 @@ 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, *fileutils.GGML, []string, []string, api.Options, int) (runners.LLMServer, error) {
+	return func(gpus discover.GpuInfoList, model string, ggml *fileutils.GGML, projectors, system []string, opts api.Options, numParallel int) (runners.LLMServer, error) {
 		return mock, nil
 	}
 }
@@ -51,7 +52,7 @@ func TestGenerateChat(t *testing.T) {
 	gin.SetMode(gin.TestMode)
 
 	mock := mockRunner{
-		CompletionResponse: llm.CompletionResponse{
+		CompletionResponse: runners.CompletionResponse{
 			Done:               true,
 			DoneReason:         "stop",
 			PromptEvalCount:    1,
@@ -72,7 +73,7 @@ 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 *fileutils.GGML, gpus discover.GpuInfoList, numParallel int) {
 				// add small delay to simulate loading
 				time.Sleep(time.Millisecond)
 				req.successCh <- &runnerRef{
@@ -91,7 +92,7 @@ func TestGenerateChat(t *testing.T) {
 {{- if .System }}System: {{ .System }} {{ end }}
 {{- if .Prompt }}User: {{ .Prompt }} {{ end }}
 {{- if .Response }}Assistant: {{ .Response }} {{ end }}"""
-`, createBinFile(t, llm.KV{
+`, createBinFile(t, fileutils.KV{
 			"general.architecture":          "llama",
 			"llama.block_count":             uint32(1),
 			"llama.context_length":          uint32(8192),
@@ -101,7 +102,7 @@ func TestGenerateChat(t *testing.T) {
 			"tokenizer.ggml.tokens":         []string{""},
 			"tokenizer.ggml.scores":         []float32{0},
 			"tokenizer.ggml.token_type":     []int32{0},
-		}, []llm.Tensor{
+		}, []fileutils.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))},
@@ -146,10 +147,10 @@ func TestGenerateChat(t *testing.T) {
 	t.Run("missing capabilities chat", func(t *testing.T) {
 		w := createRequest(t, s.CreateHandler, api.CreateRequest{
 			Model: "bert",
-			Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, llm.KV{
+			Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, fileutils.KV{
 				"general.architecture": "bert",
 				"bert.pooling_type":    uint32(0),
-			}, []llm.Tensor{})),
+			}, []fileutils.Tensor{})),
 			Stream: &stream,
 		})
 
@@ -349,7 +350,7 @@ func TestGenerate(t *testing.T) {
 	gin.SetMode(gin.TestMode)
 
 	mock := mockRunner{
-		CompletionResponse: llm.CompletionResponse{
+		CompletionResponse: runners.CompletionResponse{
 			Done:               true,
 			DoneReason:         "stop",
 			PromptEvalCount:    1,
@@ -370,7 +371,7 @@ 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 *fileutils.GGML, gpus discover.GpuInfoList, numParallel int) {
 				// add small delay to simulate loading
 				time.Sleep(time.Millisecond)
 				req.successCh <- &runnerRef{
@@ -389,7 +390,7 @@ func TestGenerate(t *testing.T) {
 {{- if .System }}System: {{ .System }} {{ end }}
 {{- if .Prompt }}User: {{ .Prompt }} {{ end }}
 {{- if .Response }}Assistant: {{ .Response }} {{ end }}"""
-`, createBinFile(t, llm.KV{
+`, createBinFile(t, fileutils.KV{
 			"general.architecture":          "llama",
 			"llama.block_count":             uint32(1),
 			"llama.context_length":          uint32(8192),
@@ -399,7 +400,7 @@ func TestGenerate(t *testing.T) {
 			"tokenizer.ggml.tokens":         []string{""},
 			"tokenizer.ggml.scores":         []float32{0},
 			"tokenizer.ggml.token_type":     []int32{0},
-		}, []llm.Tensor{
+		}, []fileutils.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))},
@@ -444,10 +445,10 @@ func TestGenerate(t *testing.T) {
 	t.Run("missing capabilities generate", func(t *testing.T) {
 		w := createRequest(t, s.CreateHandler, api.CreateRequest{
 			Model: "bert",
-			Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, llm.KV{
+			Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, fileutils.KV{
 				"general.architecture": "bert",
 				"bert.pooling_type":    uint32(0),
-			}, []llm.Tensor{})),
+			}, []fileutils.Tensor{})),
 			Stream: &stream,
 		})
 

server/routes_test.go

@@ -16,7 +16,7 @@ import (
 	"testing"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fileutils"
 	"github.com/ollama/ollama/openai"
 	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/types/model"
@@ -83,14 +83,14 @@ func Test_Routes(t *testing.T) {
 		fname := createTestFile(t, "ollama-model")
 
 		r := strings.NewReader(fmt.Sprintf("FROM %s\nPARAMETER seed 42\nPARAMETER top_p 0.9\nPARAMETER stop foo\nPARAMETER stop bar", fname))
-		modelfile, err := parser.ParseFile(r)
+		fileutils, err := parser.ParseFile(r)
 		if err != nil {
 			t.Fatalf("failed to parse file: %v", err)
 		}
 		fn := func(resp api.ProgressResponse) {
 			t.Logf("Status: %s", resp.Status)
 		}
-		err = CreateModel(context.TODO(), model.ParseName(name), "", "", modelfile, fn)
+		err = CreateModel(context.TODO(), model.ParseName(name), "", "", fileutils, fn)
 		if err != nil {
 			t.Fatalf("failed to create model: %v", err)
 		}
@@ -561,8 +561,8 @@ func TestShow(t *testing.T) {
 		Name: "show-model",
 		Modelfile: fmt.Sprintf(
 			"FROM %s\nFROM %s",
-			createBinFile(t, llm.KV{"general.architecture": "test"}, nil),
-			createBinFile(t, llm.KV{"general.type": "projector", "general.architecture": "clip"}, nil),
+			createBinFile(t, fileutils.KV{"general.architecture": "test"}, nil),
+			createBinFile(t, fileutils.KV{"general.type": "projector", "general.architecture": "clip"}, nil),
 		),
 	})
 

server/sched.go

@@ -17,8 +17,9 @@ import (
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/discover"
 	"github.com/ollama/ollama/envconfig"
+	"github.com/ollama/ollama/fileutils"
 	"github.com/ollama/ollama/format"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/runners"
 )
 
 type LlmRequest struct {
@@ -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, ggml *fileutils.GGML, gpus discover.GpuInfoList, numParallel int)
+	newServerFn  func(gpus discover.GpuInfoList, model string, ggml *fileutils.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (runners.LLMServer, error)
 	getGpuFn     func() discover.GpuInfoList
 	getCpuFn     func() discover.GpuInfoList
 	reschedDelay time.Duration
@@ -68,7 +69,7 @@ func InitScheduler(ctx context.Context) *Scheduler {
 		expiredCh:     make(chan *runnerRef, maxQueue),
 		unloadedCh:    make(chan interface{}, maxQueue),
 		loaded:        make(map[string]*runnerRef),
-		newServerFn:   llm.NewLlamaServer,
+		newServerFn:   runners.NewLlamaServer,
 		getGpuFn:      discover.GetGPUInfo,
 		getCpuFn:      discover.GetCPUInfo,
 		reschedDelay:  250 * time.Millisecond,
@@ -187,7 +188,7 @@ func (s *Scheduler) processPending(ctx context.Context) {
 					}
 
 					// Load model for fitting
-					ggml, err := llm.LoadModel(pending.model.ModelPath, 0)
+					ggml, err := fileutils.LoadModel(pending.model.ModelPath, 0)
 					if err != nil {
 						pending.errCh <- err
 						break
@@ -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, ggml *fileutils.GGML, gpus discover.GpuInfoList, numParallel int) {
 	if numParallel < 1 {
 		numParallel = 1
 	}
@@ -422,7 +423,7 @@ func (s *Scheduler) load(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoL
 		// 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, fileutils.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)
@@ -540,7 +541,7 @@ type runnerRef struct {
 	refCount uint // prevent unloading if > 0
 	// unloading bool      // set to true when we are trying to unload the runner
 
-	llama          llm.LlamaServer
+	llama          runners.LLMServer
 	loading        bool                 // True only during initial load, then false forever
 	gpus           discover.GpuInfoList // Recorded at time of provisioning
 	estimatedVRAM  uint64
@@ -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 parallism 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, ggml *fileutils.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 = fileutils.PredictServerFit([]discover.GpuInfo{g}, ggml, 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 = fileutils.PredictServerFit(sgl, ggml, 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, ggml *fileutils.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 := fileutils.PredictServerFit(gl, ggml, 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, ggml *fileutils.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 := fileutils.EstimateGPULayers(gpus, ggml, 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

@@ -14,8 +14,9 @@ import (
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/app/lifecycle"
 	"github.com/ollama/ollama/discover"
+	"github.com/ollama/ollama/fileutils"
 	"github.com/ollama/ollama/format"
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/runners"
 )
 
 func TestMain(m *testing.M) {
@@ -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 ggml *fileutils.GGML // value not used in tests
 	req := &LlmRequest{
 		ctx:             ctx,
 		model:           &Model{ModelPath: "foo"},
@@ -47,7 +48,7 @@ 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, ggml *fileutils.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (runners.LLMServer, error) {
 		return nil, errors.New("something failed to load model blah")
 	}
 	gpus := discover.GpuInfoList{}
@@ -61,7 +62,7 @@ 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, ggml *fileutils.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (runners.LLMServer, error) {
 		return server, nil
 	}
 	s.load(req, ggml, gpus, 0)
@@ -99,10 +100,10 @@ type reqBundle struct {
 	ctxDone func()
 	srv     *mockLlm
 	req     *LlmRequest
-	ggml    *llm.GGML
+	ggml    *fileutils.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, ggml *fileutils.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (runners.LLMServer, 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, fileutils.WriteGGUF(f, fileutils.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{
+	}, []fileutils.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.ggml, err = fileutils.LoadModel(model.ModelPath, 0)
 	require.NoError(t, err)
 
 	if duration == nil {
@@ -419,10 +420,10 @@ func TestExpireRunner(t *testing.T) {
 		sessionDuration: &api.Duration{Duration: 2 * time.Minute},
 	}
 
-	var ggml *llm.GGML
+	var ggml *fileutils.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, ggml *fileutils.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (runners.LLMServer, error) {
 		return server, nil
 	}
 	s.load(req, ggml, gpus, 0)
@@ -729,7 +730,7 @@ 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, ggml *fileutils.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (runners.LLMServer, error) {
 		require.Len(t, gpus, 1)
 		return a.newServer(gpus, model, ggml, adapters, projectors, opts, numParallel)
 	}
@@ -768,7 +769,7 @@ type mockLlm struct {
 
 func (s *mockLlm) Ping(ctx context.Context) error             { return s.pingResp }
 func (s *mockLlm) WaitUntilRunning(ctx context.Context) error { return s.waitResp }
-func (s *mockLlm) Completion(ctx context.Context, req llm.CompletionRequest, fn func(llm.CompletionResponse)) error {
+func (s *mockLlm) Completion(ctx context.Context, req runners.CompletionRequest, fn func(runners.CompletionResponse)) error {
 	return s.completionResp
 }
 

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/fileutils"
 )
 
 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 := fileutils.KV{"tokenizer.chat_template": v}
 				s := kv.ChatTemplate()
 				r, err := Named(s)
 				if err != nil {