merge conflicts

template: preprocess message and collect system

.github/workflows/release.yaml

@@ -147,7 +147,7 @@ jobs:
         run: |
           $ErrorActionPreference = "Stop"
           write-host "downloading AMD HIP Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-23.Q4-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP"
           Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
           write-host "Completed AMD HIP"
@@ -304,11 +304,6 @@ jobs:
           write-host "Installing plugin"
           & "${env:RUNNER_TEMP}\plugin\*\kmscng.msi" /quiet
           write-host "plugin installed"
-      - name: remove unwanted mingw dll.a files
-        run: |
-          Get-ChildItem -Path "C:\mingw64" -Recurse -Filter "libpthread.dll.a" -File | Remove-Item -Force
-          Get-ChildItem -Path "C:\mingw64" -Recurse -Filter "libwinpthread.dll.a" -File | Remove-Item -Force
-          Get-ChildItem -Path "C:\mingw64" -Recurse -Filter "libstdc++.dll.a" -File | Remove-Item -Force
       - uses: actions/setup-go@v5
         with:
           go-version-file: go.mod

.github/workflows/test.yaml

@@ -169,7 +169,7 @@ jobs:
         run: |
           $ErrorActionPreference = "Stop"
           write-host "downloading AMD HIP Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-23.Q4-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP"
           Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
           write-host "Completed AMD HIP"

app/ollama.iss

@@ -127,6 +127,10 @@ Type: filesandordirs; Name: "{%USERPROFILE}\.ollama\models"
 Type: filesandordirs; Name: "{%USERPROFILE}\.ollama\history"
 ; NOTE: if the user has a custom OLLAMA_MODELS it will be preserved
 
+[InstallDelete]
+Type: filesandordirs; Name: "{%TEMP}\ollama*"
+Type: filesandordirs; Name: "{%LOCALAPPDATA}\Programs\Ollama"
+
 [Messages]
 WizardReady=Ollama Windows Preview
 ReadyLabel1=%nLet's get you up and running with your own large language models.

cmd/cmd.go

@@ -657,7 +657,7 @@ func showInfo(resp *api.ShowResponse) {
 
 	modelData := [][]string{
 		{"arch", arch},
-		{"parameters", resp.Details.ParameterSize},
+		{"parameters", format.Parameters(uint64(resp.ModelInfo["general.parameter_count"].(float64)))},
 		{"quantization", resp.Details.QuantizationLevel},
 		{"context length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.context_length", arch)].(float64))},
 		{"embedding length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.embedding_length", arch)].(float64))},
@@ -671,7 +671,7 @@ func showInfo(resp *api.ShowResponse) {
 	if resp.ProjectorInfo != nil {
 		projectorData := [][]string{
 			{"arch", "clip"},
-			{"parameters", format.HumanNumber(uint64(resp.ProjectorInfo["general.parameter_count"].(float64)))},
+			{"parameters", format.Parameters(uint64(resp.ProjectorInfo["general.parameter_count"].(float64)))},
 		}
 
 		if projectorType, ok := resp.ProjectorInfo["clip.projector_type"]; ok {

convert/convert.go

@@ -1,134 +1,200 @@
 package convert
 
 import (
+	"cmp"
+	"encoding/binary"
 	"encoding/json"
-	"errors"
 	"fmt"
 	"io"
 	"log/slog"
 	"os"
 	"path/filepath"
+	"slices"
+	"strings"
 
+	"google.golang.org/protobuf/proto"
+
+	"github.com/ollama/ollama/convert/sentencepiece"
 	"github.com/ollama/ollama/llm"
 )
 
-type Parameters struct {
-	Architectures []string `json:"architectures"`
-	VocabSize     uint32   `json:"vocab_size"`
+const (
+	_ int32 = iota
+	tokenTypeNormal
+	tokenTypeUnknown
+	tokenTypeControl
+	tokenTypeUserDefined
+	tokenTypeUnused
+	tokenTypeByte
+)
+
+type Params struct {
+	Architectures     []string `json:"architectures"`
+	VocabSize         int      `json:"vocab_size"`
+	HiddenSize        int      `json:"hidden_size"`       // n_embd
+	HiddenLayers      int      `json:"num_hidden_layers"` // n_layer
+	ContextSize       int      `json:"max_position_embeddings"`
+	IntermediateSize  int      `json:"intermediate_size"`
+	AttentionHeads    int      `json:"num_attention_heads"` // n_head
+	KeyValHeads       int      `json:"num_key_value_heads"`
+	NormEPS           float64  `json:"rms_norm_eps"`
+	BoSTokenID        int      `json:"bos_token_id"`
+	EoSTokenID        int      `json:"eos_token_id"`
+	HeadDimension     int      `json:"head_dim"`
+	PaddingTokenID    int      `json:"pad_token_id"`
+	RopeFrequencyBase float64  `json:"rope_theta"`
+
+	Experts     int `json:"num_local_experts"`
+	ExpertsUsed int `json:"num_experts_per_tok"`
+
+	PreTokenizer string
+
+	ByteOrder
 }
 
-func (Parameters) KV(t *Tokenizer) llm.KV {
-	kv := llm.KV{
-		"general.file_type":            uint32(1),
-		"general.quantization_version": uint32(2),
-		"tokenizer.ggml.pre":           t.Pre,
-		"tokenizer.ggml.model":         t.Vocabulary.Model,
-		"tokenizer.ggml.tokens":        t.Vocabulary.Tokens,
-		"tokenizer.ggml.scores":        t.Vocabulary.Scores,
-		"tokenizer.ggml.token_type":    t.Vocabulary.Types,
-	}
-
-	if t.Template != "" {
-		kv["tokenizer.chat_template"] = t.Template
-	}
-
-	for _, sv := range t.SpecialVocabulary {
-		kv[fmt.Sprintf("tokenizer.ggml.%s_token_id", sv.Key())] = uint32(sv.ID)
-		kv[fmt.Sprintf("tokenizer.ggml.add_%s_token", sv.Key())] = sv.AddToken
-	}
-
-	return kv
+type ByteOrder interface {
+	binary.ByteOrder
+	binary.AppendByteOrder
 }
 
-func (Parameters) specialTypes() []string {
-	return []string{
-		"bos", "eos", "unk", "sep", "pad", "cls", "mask",
-	}
+type ModelArch interface {
+	GetTensors() error
+	LoadVocab() error
+	WriteGGUF(io.WriteSeeker) error
 }
 
-func (Parameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []*llm.Tensor) error {
-	return llm.WriteGGUF(ws, kv, ts)
+type ModelFormat interface {
+	GetLayerName(string) (string, error)
+	GetTensors(string, *Params) ([]llm.Tensor, error)
+	GetParams(string) (*Params, error)
+	GetModelArch(string, string, *Params) (ModelArch, error)
 }
 
-type Converter interface {
-	// KV maps parameters to LLM key-values
-	KV(*Tokenizer) llm.KV
-	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
-	Tensors([]Tensor) []*llm.Tensor
-
-	// tensorName returns the LLM tensor name for a specific input name
-	tensorName(string) string
-	// specialTypes returns any special token types the model uses
-	specialTypes() []string
-	writeFile(io.WriteSeeker, llm.KV, []*llm.Tensor) error
+type ModelData struct {
+	Path    string
+	Name    string
+	Params  *Params
+	Vocab   *Vocab
+	Tensors []llm.Tensor
+	Format  ModelFormat
 }
 
-func ConvertAdapter(d string, ws io.WriteSeeker) error {
-	c := &adapter{}
-
-	ts, err := parseNPZ(d)
+func GetModelFormat(dirname string) (ModelFormat, error) {
+	files, err := filepath.Glob(filepath.Join(dirname, "*"))
 	if err != nil {
-		return err
+		return nil, err
 	}
 
-	return c.writeFile(ws, c.KV(nil), c.Tensors(ts))
-}
-
-func Convert(d string, ws io.WriteSeeker) error {
-	f, err := os.Open(filepath.Join(d, "config.json"))
-	if err != nil {
-		return err
-	}
-	defer f.Close()
-
-	var p Parameters
-	if err := json.NewDecoder(f).Decode(&p); err != nil {
-		return err
-	}
-
-	if len(p.Architectures) < 1 {
-		return errors.New("unknown architecture")
-	}
-
-	var c Converter
-	switch p.Architectures[0] {
-	case "LlamaForCausalLM", "MistralForCausalLM":
-		c = &llama{}
-	case "MixtralForCausalLM":
-		c = &mixtral{}
-	case "GemmaForCausalLM":
-		c = &gemma{}
-	default:
-		return errors.New("unsupported architecture")
-	}
-
-	bts, err := os.ReadFile(filepath.Join(d, "config.json"))
-	if err != nil {
-		return err
-	}
-
-	if err := json.Unmarshal(bts, c); err != nil {
-		return err
-	}
-
-	t, err := parseTokenizer(d, c.specialTypes())
-	if err != nil {
-		return err
-	}
-
-	if vocabSize := int(p.VocabSize); vocabSize > len(t.Vocabulary.Tokens) {
-		slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", p.VocabSize, "actual", len(t.Vocabulary.Tokens))
-		for i := range vocabSize - len(t.Vocabulary.Tokens) {
-			t.Vocabulary.Tokens = append(t.Vocabulary.Tokens, fmt.Sprintf("[PAD%d]", i))
-			t.Vocabulary.Scores = append(t.Vocabulary.Scores, -1)
-			t.Vocabulary.Types = append(t.Vocabulary.Types, tokenTypeUserDefined)
+	for _, fn := range files {
+		if strings.HasSuffix(fn, ".safetensors") {
+			return &SafetensorFormat{}, nil
+		} else if strings.HasSuffix(fn, ".bin") || strings.HasSuffix(fn, ".pth") {
+			slog.Debug("model is torch")
+			return &TorchFormat{}, nil
 		}
 	}
 
-	ts, err := parseTensors(d)
+	return nil, fmt.Errorf("couldn't determine model format")
+}
+
+// Details on gguf's tokenizer can be found at:
+// https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#tokenizer
+type Vocab struct {
+	Tokens []string
+	Scores []float32
+	Types  []int32
+	Merges []string
+}
+
+func LoadSentencePieceTokens(dirpath string, params *Params) (*Vocab, error) {
+	slog.Info(fmt.Sprintf("reading vocab from %s", filepath.Join(dirpath, "tokenizer.model")))
+	in, err := os.ReadFile(filepath.Join(dirpath, "tokenizer.model"))
 	if err != nil {
-		return err
+		return nil, err
 	}
 
-	return c.writeFile(ws, c.KV(t), c.Tensors(ts))
+	// To regenerate sentencepiece from the protobufs use:
+	// protoc -I=./ --go_out=./ sentencepiece_model.proto
+	modelProto := &sentencepiece.ModelProto{}
+	if err := proto.Unmarshal(in, modelProto); err != nil {
+		return nil, err
+	}
+
+	v := &Vocab{
+		Tokens: make([]string, 0),
+		Scores: make([]float32, 0),
+		Types:  make([]int32, 0),
+	}
+
+	pieces := modelProto.GetPieces()
+	for _, p := range pieces {
+		v.Tokens = append(v.Tokens, p.GetPiece())
+		v.Scores = append(v.Scores, p.GetScore())
+		t := p.GetType()
+		switch t {
+		case sentencepiece.ModelProto_SentencePiece_UNKNOWN:
+		case sentencepiece.ModelProto_SentencePiece_CONTROL:
+		case sentencepiece.ModelProto_SentencePiece_UNUSED:
+		case sentencepiece.ModelProto_SentencePiece_BYTE:
+		default:
+			t = sentencepiece.ModelProto_SentencePiece_NORMAL
+		}
+		v.Types = append(v.Types, int32(t))
+	}
+
+	slog.Info(fmt.Sprintf("vocab size: %d", len(v.Tokens)))
+
+	// add any additional tokens
+	addIn, err := os.ReadFile(filepath.Join(dirpath, "added_tokens.json"))
+	if os.IsNotExist(err) {
+		return v, nil
+	} else if err != nil {
+		return nil, err
+	}
+
+	slog.Info("reading user defined tokens")
+
+	var extraTokenData map[string]int
+	if err := json.Unmarshal(addIn, &extraTokenData); err != nil {
+		return nil, err
+	}
+
+	type token struct {
+		key string
+		pos int
+	}
+
+	extraTokens := make([]token, 0)
+	for k, id := range extraTokenData {
+		extraTokens = append(extraTokens, token{k, id})
+	}
+
+	slices.SortFunc(extraTokens, func(a, b token) int {
+		return cmp.Compare(a.pos, b.pos)
+	})
+
+	numToks := len(v.Tokens)
+
+	for cnt, t := range extraTokens {
+		// the token id should match the specific index for the total number of tokens
+		if t.pos != cnt+numToks {
+			return nil, fmt.Errorf("token ID '%d' for '%s' doesn't match total token size", t.pos, t.key)
+		}
+		v.Tokens = append(v.Tokens, t.key)
+		v.Scores = append(v.Scores, -1000.0)
+		v.Types = append(v.Types, tokenTypeUserDefined)
+	}
+	slog.Info(fmt.Sprintf("vocab size w/ extra tokens: %d", len(v.Tokens)))
+
+	if params.VocabSize > len(v.Tokens) {
+		missingTokens := params.VocabSize - len(v.Tokens)
+		slog.Warn(fmt.Sprintf("vocab is missing %d tokens", missingTokens))
+		for cnt := range missingTokens {
+			v.Tokens = append(v.Tokens, fmt.Sprintf("<dummy%05d>", cnt+1))
+			v.Scores = append(v.Scores, -1)
+			v.Types = append(v.Types, tokenTypeUserDefined)
+		}
+	}
+
+	return v, nil
 }

convert/convert_adapter.go

@@ -1,56 +0,0 @@
-package convert
-
-import (
-	"io"
-	"strings"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type adapter struct {
-	Parameters
-}
-
-var _ Converter = (*adapter)(nil)
-
-func (p *adapter) writeFile(ws io.WriteSeeker, kv llm.KV, ts []*llm.Tensor) error {
-	return llm.WriteGGLA(ws, kv, ts)
-}
-
-func (p *adapter) KV(t *Tokenizer) llm.KV {
-	// todo - need a way to pass these in
-	kv := llm.KV{
-		"r":     uint32(8),
-		"alpha": uint32(160),
-	}
-	return kv
-}
-
-func (p *adapter) Tensors(ts []Tensor) []*llm.Tensor {
-	var out []*llm.Tensor
-	for _, t := range ts {
-		name := p.tensorName(t.Name())
-
-		out = append(out, &llm.Tensor{
-			Name:     name,
-			Kind:     t.Kind(),
-			Shape:    t.Shape(),
-			WriterTo: t,
-		})
-	}
-
-	return out
-}
-
-func (p *adapter) tensorName(n string) string {
-	return strings.NewReplacer(
-		"model.layers", "blk",
-		"self_attn.q_proj", "attn_q.weight",
-		"self_attn.k_proj", "attn_k.weight",
-		"self_attn.v_proj", "attn_v.weight",
-		"self_attn.o_proj", "attn_output.weight",
-		"lora_a", "loraA",
-		"lora_b", "loraB",
-		".npy", "",
-	).Replace(n)
-}

convert/convert_gemma.go

@@ -1,103 +0,0 @@
-package convert
-
-import (
-	"strings"
-
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type gemma struct {
-	Parameters
-	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
-	HiddenSize            uint32  `json:"hidden_size"`
-	HiddenLayers          uint32  `json:"num_hidden_layers"`
-	IntermediateSize      uint32  `json:"intermediate_size"`
-	NumAttentionHeads     uint32  `json:"num_attention_heads"`
-	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
-	RMSNormEPS            float32 `json:"rms_norm_eps"`
-	HeadDim               uint32  `json:"head_dim"`
-}
-
-var _ Converter = (*gemma)(nil)
-
-func (p *gemma) KV(t *Tokenizer) llm.KV {
-	kv := p.Parameters.KV(t)
-	kv["general.architecture"] = "gemma"
-	kv["general.name"] = "gemma"
-	kv["gemma.context_length"] = p.MaxPositionEmbeddings
-	kv["gemma.embedding_length"] = p.HiddenSize
-	kv["gemma.block_count"] = p.HiddenLayers
-	kv["gemma.feed_forward_length"] = p.IntermediateSize
-	kv["gemma.attention.head_count"] = p.NumAttentionHeads
-	kv["gemma.attention.head_count_kv"] = p.NumKeyValueHeads
-	kv["gemma.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
-	kv["gemma.attention.key_length"] = p.HeadDim
-	kv["gemma.attention.value_length"] = p.HeadDim
-	kv["tokenizer.ggml.eot_token_id"] = uint32(107)
-	kv["tokenizer.ggml.middle_token_id"] = uint32(68)
-	kv["tokenizer.ggml.prefix_token_id"] = uint32(67)
-	kv["tokenizer.ggml.suffix_token_id"] = uint32(69)
-	return kv
-}
-
-func (p *gemma) Tensors(ts []Tensor) []*llm.Tensor {
-	var out []*llm.Tensor
-	for _, t := range ts {
-		name := p.tensorName(t.Name())
-		if strings.HasSuffix(name, "_norm.weight") {
-			t.SetRepacker(p.addOne)
-		}
-
-		out = append(out, &llm.Tensor{
-			Name:     name,
-			Kind:     t.Kind(),
-			Shape:    t.Shape(),
-			WriterTo: t,
-		})
-	}
-
-	return out
-}
-
-func (p *gemma) tensorName(n string) string {
-	return strings.NewReplacer(
-		"model.embed_tokens", "token_embd",
-		"model.norm", "output_norm",
-		"model.layers", "blk",
-		"input_layernorm", "attn_norm",
-		"self_attn.q_proj", "attn_q",
-		"self_attn.k_proj", "attn_k",
-		"self_attn.v_proj", "attn_v",
-		"self_attn.o_proj", "attn_output",
-		"mlp.gate_proj", "ffn_gate",
-		"mlp.down_proj", "ffn_down",
-		"mlp.up_proj", "ffn_up",
-		"post_attention_layernorm", "ffn_norm",
-		"block_sparse_moe.gate", "ffn_inp",
-	).Replace(n)
-}
-
-func (*gemma) addOne(_ string, data []float32, shape []uint64) ([]float32, error) {
-	n := tensor.New(tensor.WithShape(int(shape[0])), tensor.WithBacking(data))
-	ones := tensor.Ones(tensor.Float32, int(shape[0]))
-
-	n, err := n.Add(ones)
-	if err != nil {
-		return nil, err
-	}
-
-	ts, err := native.SelectF32(n, 0)
-	if err != nil {
-		return nil, err
-	}
-
-	var f32s []float32
-	for _, t := range ts {
-		f32s = append(f32s, t...)
-	}
-
-	return f32s, nil
-}

convert/convert_llama.go

@@ -1,176 +0,0 @@
-package convert
-
-import (
-	"cmp"
-	"fmt"
-	"strings"
-
-	"github.com/ollama/ollama/llm"
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-)
-
-type llama struct {
-	Parameters
-	NLayers               uint32  `json:"n_layers"`
-	NumHiddenLayers       uint32  `json:"num_hidden_layers"`
-	NLayer                uint32  `json:"n_layer"`
-	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
-	NCtx                  uint32  `json:"n_ctx"`
-	HiddenSize            uint32  `json:"hidden_size"`
-	NEmbd                 uint32  `json:"n_embd"`
-	IntermediateSize      uint32  `json:"intermediate_size"`
-	NInner                uint32  `json:"n_inner"`
-	NumAttentionHeads     uint32  `json:"num_attention_heads"`
-	NHead                 uint32  `json:"n_head"`
-	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
-	RopeTheta             float32 `json:"rope_theta"`
-	RopeScaling           struct {
-		Type   string  `json:"type"`
-		Factor float32 `json:"factor"`
-	} `json:"rope_scaling"`
-	RMSNormEPS       float32 `json:"rms_norm_eps"`
-	LayerNormEPS     float32 `json:"layer_norm_eps"`
-	LayerNormEpsilon float32 `json:"layer_norm_epsilon"`
-	NormEpsilon      float32 `json:"norm_epsilon"`
-}
-
-var _ Converter = (*llama)(nil)
-
-func (p *llama) KV(t *Tokenizer) llm.KV {
-	kv := p.Parameters.KV(t)
-	kv["general.architecture"] = "llama"
-	kv["general.name"] = "llama"
-	kv["llama.vocab_size"] = p.VocabSize
-
-	kv["llama.block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
-
-	if contextLength := cmp.Or(p.MaxPositionEmbeddings, p.NCtx); contextLength > 0 {
-		kv["llama.context_length"] = contextLength
-	}
-
-	if embeddingLength := cmp.Or(p.HiddenSize, p.NEmbd); embeddingLength > 0 {
-		kv["llama.embedding_length"] = cmp.Or(p.HiddenSize, p.NEmbd)
-	}
-
-	if feedForwardLength := cmp.Or(p.IntermediateSize, p.NInner); feedForwardLength > 0 {
-		kv["llama.feed_forward_length"] = cmp.Or(p.IntermediateSize, p.NInner)
-	}
-
-	if headCount := cmp.Or(p.NumAttentionHeads, p.NHead); headCount > 0 {
-		kv["llama.attention.head_count"] = cmp.Or(p.NumAttentionHeads, p.NHead)
-		kv["llama.rope.dimension_count"] = p.HiddenSize / headCount
-	}
-
-	if p.RopeTheta > 0 {
-		kv["llama.rope.freq_base"] = p.RopeTheta
-	}
-
-	if p.RopeScaling.Type == "linear" {
-		kv["llama.rope.scaling.type"] = p.RopeScaling.Type
-		kv["llama.rope.scaling.factor"] = p.RopeScaling.Factor
-	}
-
-	if p.NumKeyValueHeads > 0 {
-		kv["llama.attention.head_count_kv"] = p.NumKeyValueHeads
-	}
-
-	if p.RMSNormEPS > 0 {
-		kv["llama.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
-	}
-
-	if layerNormEpsilon := cmp.Or(p.LayerNormEPS, p.LayerNormEpsilon, p.NormEpsilon); layerNormEpsilon > 0 {
-		kv["llama.attention.layer_norm_epsilon"] = layerNormEpsilon
-	}
-
-	if len(t.Merges) > 0 {
-		kv["tokenizer.ggml.merges"] = t.Merges
-	}
-
-	return kv
-}
-
-func (p *llama) Tensors(ts []Tensor) []*llm.Tensor {
-	var out []*llm.Tensor
-	for _, t := range ts {
-		name := p.tensorName(t.Name())
-		if strings.HasSuffix(name, "attn_q.weight") ||
-			strings.HasSuffix(name, "attn_k.weight") {
-			t.SetRepacker(p.repack)
-		}
-
-		out = append(out, &llm.Tensor{
-			Name:     name,
-			Kind:     t.Kind(),
-			Shape:    t.Shape(),
-			WriterTo: t,
-		})
-	}
-
-	return out
-}
-
-func (p *llama) tensorName(n string) string {
-	return strings.NewReplacer(
-		"lm_head", "output",
-		"model.embed_tokens", "token_embd",
-		"model.norm", "output_norm",
-		"model.layers", "blk",
-		"input_layernorm", "attn_norm",
-		"self_attn.q_proj", "attn_q",
-		"self_attn.k_proj", "attn_k",
-		"self_attn.v_proj", "attn_v",
-		"self_attn.o_proj", "attn_output",
-		"mlp.gate_proj", "ffn_gate",
-		"mlp.down_proj", "ffn_down",
-		"mlp.up_proj", "ffn_up",
-		"post_attention_layernorm", "ffn_norm",
-		// mixtral
-		"block_sparse_moe.gate", "ffn_gate_inp",
-	).Replace(n)
-}
-
-func (p *llama) repack(name string, data []float32, shape []uint64) ([]float32, error) {
-	var dims []int
-	for _, dim := range shape {
-		dims = append(dims, int(dim))
-	}
-
-	var heads uint32
-	if strings.HasSuffix(name, "q_proj.weight") {
-		heads = p.NumAttentionHeads
-	} else if strings.HasSuffix(name, "k_proj.weight") {
-		heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
-	} else {
-		return nil, fmt.Errorf("unknown tensor for repack: %s", name)
-	}
-
-	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-	if err := n.Reshape(append([]int{int(heads), 2, dims[0] / int(heads) / 2}, dims[1:]...)...); err != nil {
-		return nil, err
-	}
-
-	if err := n.T(0, 2, 1, 3); err != nil {
-		return nil, err
-	}
-
-	if err := n.Reshape(dims...); err != nil {
-		return nil, err
-	}
-
-	if err := n.Transpose(); err != nil {
-		return nil, err
-	}
-
-	ts, err := native.SelectF32(n, 1)
-	if err != nil {
-		return nil, err
-	}
-
-	var f32s []float32
-	for _, t := range ts {
-		f32s = append(f32s, t...)
-	}
-
-	return f32s, nil
-}

convert/convert_mixtral.go

@@ -1,89 +0,0 @@
-package convert
-
-import (
-	"fmt"
-	"io"
-	"slices"
-	"strings"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type mixtral struct {
-	llama
-	NumLocalExperts    uint32 `json:"num_local_experts"`
-	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
-}
-
-var _ Converter = (*mixtral)(nil)
-
-func (p *mixtral) KV(t *Tokenizer) llm.KV {
-	kv := p.llama.KV(t)
-
-	if p.NumLocalExperts > 0 {
-		kv["llama.expert_count"] = p.NumLocalExperts
-	}
-
-	if p.NumExpertsPerToken > 0 {
-		kv["llama.expert_used_count"] = p.NumExpertsPerToken
-	}
-
-	return kv
-}
-
-func (p *mixtral) Tensors(ts []Tensor) []*llm.Tensor {
-	oldnew := []string{
-		"model.layers", "blk",
-		"w1", "ffn_gate_exps",
-		"w2", "ffn_down_exps",
-		"w3", "ffn_up_exps",
-	}
-
-	for i := range p.NumLocalExperts {
-		oldnew = append(oldnew, fmt.Sprintf(".block_sparse_moe.experts.%d.", i), ".")
-	}
-
-	// group experts of the same layer (model.layers.%d) and type (w[123]) into a single tensor
-	namer := strings.NewReplacer(oldnew...)
-	experts := make(map[string]experts)
-
-	// merge experts into a single tensor while removing them from ts
-	ts = slices.DeleteFunc(ts, func(t Tensor) bool {
-		if !strings.Contains(t.Name(), ".block_sparse_moe.experts.") {
-			return false
-		}
-
-		name := namer.Replace(t.Name())
-		experts[name] = append(experts[name], t)
-		return true
-	})
-
-	var out []*llm.Tensor
-	for n, e := range experts {
-		// TODO(mxyng): sanity check experts
-		out = append(out, &llm.Tensor{
-			Name:     n,
-			Kind:     e[0].Kind(),
-			Shape:    append([]uint64{uint64(len(e))}, e[0].Shape()...),
-			WriterTo: e,
-		})
-	}
-
-	return append(out, p.llama.Tensors(ts)...)
-}
-
-type experts []Tensor
-
-func (e experts) WriteTo(w io.Writer) (int64, error) {
-	// TODO(mxyng): experts _should_ be numerically sorted by expert but this should check
-	for _, t := range e {
-		// the canonical merged experts tensor stacks all experts along a new, 0 axis,
-		// e.g. `tensor.Stack(0, e[0], e[1:]...)`, which requires allocating temporary buffers
-		// this accomplishes the same thing by writing each expert tensor in sequence
-		if _, err := t.WriteTo(w); err != nil {
-			return 0, err
-		}
-	}
-
-	return 0, nil
-}

convert/convert_test.go

@@ -1,34 +1,48 @@
+//go:build slow
+
 package convert
 
 import (
-	"bytes"
-	"crypto/sha256"
-	"encoding/json"
-	"errors"
-	"flag"
-	"fmt"
-	"io"
-	"log/slog"
-	"math"
 	"os"
 	"path/filepath"
-	"slices"
 	"testing"
 
 	"github.com/ollama/ollama/llm"
-	"golang.org/x/exp/maps"
 )
 
-func convertFull(t *testing.T, d string) (*os.File, llm.KV, llm.Tensors) {
+func convertFull(t *testing.T, p string) (llm.KV, llm.Tensors) {
 	t.Helper()
 
+	mf, err := GetModelFormat(p)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	params, err := mf.GetParams(p)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	arch, err := mf.GetModelArch("", p, params)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	if err := arch.LoadVocab(); err != nil {
+		t.Fatal(err)
+	}
+
+	if err := arch.GetTensors(); err != nil {
+		t.Fatal(err)
+	}
+
 	f, err := os.CreateTemp(t.TempDir(), "f16")
 	if err != nil {
 		t.Fatal(err)
 	}
 	defer f.Close()
 
-	if err := Convert(d, f); err != nil {
+	if err := arch.WriteGGUF(f); err != nil {
 		t.Fatal(err)
 	}
 
@@ -36,200 +50,54 @@ func convertFull(t *testing.T, d string) (*os.File, llm.KV, llm.Tensors) {
 	if err != nil {
 		t.Fatal(err)
 	}
-	t.Cleanup(func() { r.Close() })
+	defer r.Close()
 
-	m, _, err := llm.DecodeGGML(r, math.MaxInt)
+	m, _, err := llm.DecodeGGML(r)
 	if err != nil {
 		t.Fatal(err)
 	}
 
-	if _, err := r.Seek(0, io.SeekStart); err != nil {
-		t.Fatal(err)
-	}
-
-	return r, m.KV(), m.Tensors()
-}
-
-func TestMain(m *testing.M) {
-	var level slog.Level
-	flag.TextVar(&level, "level", slog.LevelInfo, "log level")
-	flag.Parse()
-	slog.SetLogLoggerLevel(level)
-	os.Exit(m.Run())
+	return m.KV(), m.Tensors()
 }
 
 func TestConvertFull(t *testing.T) {
-	cases := []string{
-		"Meta-Llama-3-8B-Instruct",
-		"Mistral-7B-Instruct-v0.2",
-		"Mixtral-8x7B-Instruct-v0.1",
-		"gemma-2b-it",
+	cases := []struct {
+		path    string
+		arch    string
+		tensors int
+		layers  int
+	}{
+		{"Meta-Llama-3-8B-Instruct", "llama", 291, 35},
+		{"Mistral-7B-Instruct-v0.2", "llama", 291, 35},
+		{"Mixtral-8x7B-Instruct-v0.1", "llama", 291, 35},
+		{"gemma-2b-it", "gemma", 164, 20},
 	}
 
-	for i := range cases {
-		tt := cases[i]
-		t.Run(tt, func(t *testing.T) {
-			t.Parallel()
-
-			p := filepath.Join("testdata", tt)
-			if testing.Short() {
-				t.Skip("skipping in short mode")
-			} else if _, err := os.Stat(p); err != nil {
+	for _, tt := range cases {
+		t.Run(tt.path, func(t *testing.T) {
+			p := filepath.Join("testdata", tt.path)
+			if _, err := os.Stat(p); err != nil {
 				t.Skipf("%s not found", p)
 			}
 
-			f, kv, tensors := convertFull(t, p)
-			actual := make(map[string]string)
-			for k, v := range kv {
-				if s, ok := v.(json.Marshaler); !ok {
-					actual[k] = fmt.Sprintf("%v", v)
-				} else {
-					bts, err := json.Marshal(s)
-					if err != nil {
-						t.Fatal(err)
-					}
+			kv, tensors := convertFull(t, p)
 
-					actual[k] = fmt.Sprintf("%x", sha256.Sum256(bts))
-				}
+			if kv.Architecture() != tt.arch {
+				t.Fatalf("expected llama, got %s", kv.Architecture())
 			}
 
-			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 {
-					t.Fatal(err)
-				}
-
-				actual[tensor.Name] = fmt.Sprintf("%x", sha256sum.Sum(nil))
+			if kv.FileType().String() != "F16" {
+				t.Fatalf("expected F16, got %s", kv.FileType())
 			}
 
-			expectFile, err := os.Open(filepath.Join("testdata", fmt.Sprintf("%s.json", tt)))
-			if err != nil {
-				t.Fatal(err)
+			if len(tensors) != tt.tensors {
+				t.Fatalf("expected %d tensors, got %d", tt.tensors, len(tensors))
 			}
 
-			var expect map[string]string
-			if err := json.NewDecoder(expectFile).Decode(&expect); err != nil {
-				t.Fatal(err)
-			}
-
-			keys := maps.Keys(expect)
-			slices.Sort(keys)
-			for _, k := range keys {
-				if v, ok := actual[k]; !ok {
-					t.Errorf("missing %s", k)
-				} else if v != expect[k] {
-					t.Errorf("unexpected %s: want %s, got %s", k, expect[k], v)
-				}
+			layers := tensors.Layers()
+			if len(layers) != tt.layers {
+				t.Fatalf("expected %d layers, got %d", tt.layers, len(layers))
 			}
 		})
 	}
 }
-
-func TestConvertNPZ(t *testing.T) {
-	cases := []string{
-		"adapters.npz",
-	}
-
-	for _, fn := range cases {
-		ts, err := parseNPZ(filepath.Join("testdata", fn))
-		if err != nil {
-			t.Fatal(err)
-		}
-		if len(ts) != 16*2*2 {
-			t.Errorf("got: %d want: %d total layers", len(ts), 16*2*2)
-		}
-
-		a := adapter{}
-
-		for _, m := range ts {
-			at := m.(adapterTensor)
-			if at.path != filepath.Join("testdata", fn) {
-				t.Errorf("got: %s want: %s", at.path, filepath.Join("testdata", fn))
-			}
-			if at.dtype != "F32" {
-				t.Errorf("got: %s but only F32s are currently supported", at.dtype)
-			}
-			if len(at.tensorBase.shape) != 2 {
-				t.Errorf("got: %d want: %d tensor shape", at.tensorBase.shape, 2)
-			}
-		}
-
-		var ws io.WriteSeeker = &memWriter{}
-		err = llm.WriteGGLA(ws, a.KV(nil), a.Tensors(ts))
-		if err != nil {
-			t.Fatal(err)
-		}
-
-		mw := ws.(*memWriter)
-		slog.Info(fmt.Sprintf("buffer len = %d", len(mw.buf)))
-		if len(mw.buf) == 0 {
-			t.Errorf("ggla layer not written correctly")
-		}
-		rs := bytes.NewReader(mw.buf)
-		ggml, _, err := llm.DecodeGGML(rs, len(mw.buf))
-		if err != nil {
-			t.Fatal(err)
-		}
-		if ggml == nil {
-			t.Fatalf("ggla didn't convert to ggml correctly")
-		}
-
-		kv := ggml.KV()
-		if kv == nil {
-			t.Fatalf("no lora KVs were set")
-		}
-
-		r, ok := kv["r"]
-		if !ok || r != uint32(8) {
-			t.Errorf("lora rank was not set correctly")
-		}
-
-		alpha, ok := kv["alpha"]
-		if !ok || alpha != uint32(160) {
-			t.Errorf("lora alpha was not set correctly")
-		}
-
-		gts := ggml.Tensors()
-		if len(ts) != len(gts.Items) {
-			t.Fatalf("got: %d want: %d tensors in ggla", len(gts.Items), len(ts))
-		}
-	}
-}
-
-type memWriter struct {
-	buf []byte
-	pos int
-}
-
-func (m *memWriter) Write(p []byte) (n int, err error) {
-	minCap := m.pos + len(p)
-	if minCap > cap(m.buf) {
-		buf2 := make([]byte, len(m.buf), minCap+len(p)) // add some extra
-		copy(buf2, m.buf)
-		m.buf = buf2
-	}
-	if minCap > len(m.buf) {
-		m.buf = m.buf[:minCap]
-	}
-	copy(m.buf[m.pos:], p)
-	m.pos += len(p)
-	return len(p), nil
-}
-
-func (m *memWriter) Seek(offset int64, whence int) (int64, error) {
-	newPos, offs := 0, int(offset)
-	switch whence {
-	case io.SeekStart:
-		newPos = offs
-	case io.SeekCurrent:
-		newPos = m.pos + offs
-	case io.SeekEnd:
-		newPos = len(m.buf) + offs
-	}
-	if newPos < 0 {
-		return 0, errors.New("negative result pos")
-	}
-	m.pos = newPos
-	return int64(newPos), nil
-}

convert/gemma.go

@@ -0,0 +1,102 @@
+package convert
+
+import (
+	"fmt"
+	"io"
+	"log/slog"
+	"strings"
+
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type GemmaModel struct {
+	ModelData
+}
+
+func addOnes(data []float32, vectorSize int) ([]float32, error) {
+	n := tensor.New(tensor.WithShape(vectorSize), tensor.WithBacking(data))
+	ones := tensor.Ones(tensor.Float32, vectorSize)
+
+	n, err := n.Add(ones)
+	if err != nil {
+		return nil, err
+	}
+
+	ts, err := native.SelectF32(n, 0)
+	if err != nil {
+		return nil, err
+	}
+
+	var f32s []float32
+	for _, t := range ts {
+		f32s = append(f32s, t...)
+	}
+
+	return f32s, nil
+}
+
+func (m *GemmaModel) GetTensors() error {
+	t, err := m.Format.GetTensors(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+
+	slog.Debug(fmt.Sprintf("Total tensors: %d", len(t)))
+	for _, l := range t {
+		if strings.HasSuffix(l.Name, "norm.weight") {
+			wt := l.WriterTo.(safetensorWriterTo)
+			wt.repacker = m.Repack
+			l.WriterTo = wt
+		}
+		m.Tensors = append(m.Tensors, l)
+	}
+
+	return nil
+}
+
+func (m *GemmaModel) LoadVocab() error {
+	v, err := LoadSentencePieceTokens(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+	m.Vocab = v
+	return nil
+}
+
+func (m *GemmaModel) Repack(_ string, data []float32, shape []uint64) ([]float32, error) {
+	return addOnes(data, int(shape[0]))
+}
+
+func (m *GemmaModel) WriteGGUF(ws io.WriteSeeker) error {
+	kv := llm.KV{
+		"general.architecture":                   "gemma",
+		"general.name":                           m.Name,
+		"gemma.context_length":                   uint32(m.Params.ContextSize),
+		"gemma.embedding_length":                 uint32(m.Params.HiddenSize),
+		"gemma.block_count":                      uint32(m.Params.HiddenLayers),
+		"gemma.feed_forward_length":              uint32(m.Params.IntermediateSize),
+		"gemma.attention.head_count":             uint32(m.Params.AttentionHeads),
+		"gemma.attention.head_count_kv":          uint32(m.Params.KeyValHeads),
+		"gemma.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
+		"gemma.attention.key_length":             uint32(m.Params.HeadDimension),
+		"gemma.attention.value_length":           uint32(m.Params.HeadDimension),
+		"general.file_type":                      uint32(1),
+		"tokenizer.ggml.model":                   "llama",
+
+		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
+		"tokenizer.ggml.scores":     m.Vocab.Scores,
+		"tokenizer.ggml.token_type": m.Vocab.Types,
+
+		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
+		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
+		"tokenizer.ggml.padding_token_id": uint32(m.Params.PaddingTokenID),
+		"tokenizer.ggml.unknown_token_id": uint32(3),
+		"tokenizer.ggml.add_bos_token":    true,
+		"tokenizer.ggml.add_eos_token":    false,
+	}
+
+	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
+}

convert/llama.go

@@ -0,0 +1,159 @@
+package convert
+
+import (
+	"cmp"
+	"errors"
+	"fmt"
+	"io"
+	"os"
+	"path/filepath"
+	"regexp"
+	"strings"
+
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type LlamaModel struct {
+	ModelData
+}
+
+func (m *LlamaModel) GetTensors() error {
+	t, err := m.Format.GetTensors(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+
+	pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
+	re, err := regexp.Compile(pattern)
+	if err != nil {
+		return err
+	}
+
+	for _, l := range t {
+		matches := re.FindAllStringSubmatch(l.Name, -1)
+		if len(matches) > 0 {
+			switch m.Format.(type) {
+			case *TorchFormat:
+				wt := l.WriterTo.(torchWriterTo)
+				wt.repacker = m.Repack
+				l.WriterTo = wt
+			case *SafetensorFormat:
+				wt := l.WriterTo.(safetensorWriterTo)
+				wt.repacker = m.Repack
+				l.WriterTo = wt
+			}
+		}
+		m.Tensors = append(m.Tensors, l)
+	}
+
+	return nil
+}
+
+func (m *LlamaModel) LoadVocab() (err error) {
+	pre, ts, merges, err := parseTokens(filepath.Join(m.Path, "tokenizer.json"))
+	if errors.Is(err, os.ErrNotExist) {
+		return nil
+	} else if err != nil {
+		return err
+	}
+
+	m.Vocab = &Vocab{}
+	for _, t := range ts {
+		m.Vocab.Tokens = append(m.Vocab.Tokens, t.Content)
+		m.Vocab.Types = append(m.Vocab.Types, t.Type())
+	}
+
+	m.Vocab.Merges = merges
+	m.Params.PreTokenizer = pre
+	return nil
+}
+
+func (m *LlamaModel) WriteGGUF(ws io.WriteSeeker) error {
+	kv := llm.KV{
+		"general.architecture":                   "llama",
+		"general.name":                           m.Name,
+		"llama.vocab_size":                       uint32(len(m.Vocab.Tokens)),
+		"llama.context_length":                   uint32(m.Params.ContextSize),
+		"llama.embedding_length":                 uint32(m.Params.HiddenSize),
+		"llama.block_count":                      uint32(m.Params.HiddenLayers),
+		"llama.feed_forward_length":              uint32(m.Params.IntermediateSize),
+		"llama.rope.freq_base":                   float32(m.Params.RopeFrequencyBase),
+		"llama.rope.dimension_count":             uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
+		"llama.attention.head_count":             uint32(m.Params.AttentionHeads),
+		"llama.attention.head_count_kv":          uint32(m.Params.KeyValHeads),
+		"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
+		"general.file_type":                      uint32(1),
+		"tokenizer.ggml.model":                   "gpt2",
+
+		"tokenizer.ggml.pre":        m.Params.PreTokenizer,
+		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
+		"tokenizer.ggml.token_type": m.Vocab.Types,
+
+		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
+		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
+		"tokenizer.ggml.unknown_token_id": uint32(0),
+	}
+
+	if len(m.Vocab.Merges) > 0 {
+		kv["tokenizer.ggml.merges"] = m.Vocab.Merges
+	} else {
+		kv["tokenizer.ggml.scores"] = m.Vocab.Scores
+	}
+
+	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
+}
+
+func (m *LlamaModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) {
+	return llamaRepack(name, m.Params, data, shape)
+}
+
+func llamaRepack(name string, params *Params, data []float32, shape []uint64) ([]float32, error) {
+	var dims []int
+	for _, dim := range shape {
+		if dim != 0 {
+			dims = append(dims, int(dim))
+		}
+	}
+
+	var heads int
+	switch {
+	case strings.HasSuffix(name, "attn_q.weight"):
+		heads = params.AttentionHeads
+	case strings.HasSuffix(name, "attn_k.weight"):
+		heads = cmp.Or(params.KeyValHeads, params.AttentionHeads)
+	default:
+		return nil, fmt.Errorf("unknown tensor name: %s", name)
+	}
+
+	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+	if err := n.Reshape(append([]int{heads, 2, dims[0] / heads / 2}, dims[1:]...)...); err != nil {
+		return nil, err
+	}
+
+	if err := n.T(0, 2, 1, 3); err != nil {
+		return nil, err
+	}
+
+	if err := n.Reshape(dims...); err != nil {
+		return nil, err
+	}
+
+	if err := n.Transpose(); err != nil {
+		return nil, err
+	}
+
+	ts, err := native.SelectF32(n, 1)
+	if err != nil {
+		return nil, err
+	}
+
+	var f32s []float32
+	for _, t := range ts {
+		f32s = append(f32s, t...)
+	}
+
+	return f32s, nil
+}

convert/mistral.go

@@ -0,0 +1,79 @@
+package convert
+
+import (
+	"io"
+	"regexp"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type MistralModel struct {
+	ModelData
+}
+
+func (m *MistralModel) GetTensors() error {
+	t, err := m.Format.GetTensors(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+
+	pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
+	re, err := regexp.Compile(pattern)
+	if err != nil {
+		return err
+	}
+
+	for _, l := range t {
+		matches := re.FindAllStringSubmatch(l.Name, -1)
+		if len(matches) > 0 {
+			wt := l.WriterTo.(safetensorWriterTo)
+			wt.repacker = m.Repack
+			l.WriterTo = wt
+		}
+		m.Tensors = append(m.Tensors, l)
+	}
+
+	return nil
+}
+
+func (m *MistralModel) LoadVocab() error {
+	v, err := LoadSentencePieceTokens(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+	m.Vocab = v
+	return nil
+}
+
+func (m *MistralModel) WriteGGUF(ws io.WriteSeeker) error {
+	kv := llm.KV{
+		"general.architecture":                   "llama",
+		"general.name":                           m.Name,
+		"llama.context_length":                   uint32(m.Params.ContextSize),
+		"llama.embedding_length":                 uint32(m.Params.HiddenSize),
+		"llama.block_count":                      uint32(m.Params.HiddenLayers),
+		"llama.feed_forward_length":              uint32(m.Params.IntermediateSize),
+		"llama.rope.dimension_count":             uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
+		"llama.attention.head_count":             uint32(m.Params.AttentionHeads),
+		"llama.attention.head_count_kv":          uint32(m.Params.KeyValHeads),
+		"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
+		"general.file_type":                      uint32(1),
+		"tokenizer.ggml.model":                   "llama",
+
+		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
+		"tokenizer.ggml.scores":     m.Vocab.Scores,
+		"tokenizer.ggml.token_type": m.Vocab.Types,
+
+		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
+		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
+		"tokenizer.ggml.add_bos_token":    true,
+		"tokenizer.ggml.add_eos_token":    false,
+		"tokenizer.ggml.unknown_token_id": uint32(0),
+	}
+
+	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
+}
+
+func (m *MistralModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) {
+	return llamaRepack(name, m.Params, data, shape)
+}

convert/mixtral.go

@@ -0,0 +1,87 @@
+package convert
+
+import (
+	"io"
+	"regexp"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type MixtralModel struct {
+	ModelData
+}
+
+func (m *MixtralModel) GetTensors() error {
+	t, err := m.Format.GetTensors(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+
+	pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
+	re, err := regexp.Compile(pattern)
+	if err != nil {
+		return err
+	}
+
+	for _, l := range t {
+		matches := re.FindAllStringSubmatch(l.Name, -1)
+		if len(matches) > 0 {
+			wt := l.WriterTo.(safetensorWriterTo)
+			wt.repacker = m.Repack
+			l.WriterTo = wt
+		}
+		m.Tensors = append(m.Tensors, l)
+	}
+
+	return nil
+}
+
+func (m *MixtralModel) LoadVocab() error {
+	v, err := LoadSentencePieceTokens(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+	m.Vocab = v
+	return nil
+}
+
+func (m *MixtralModel) WriteGGUF(ws io.WriteSeeker) error {
+	kv := llm.KV{
+		"general.architecture":          "llama",
+		"general.name":                  m.Name,
+		"llama.block_count":             uint32(m.Params.HiddenLayers),
+		"llama.context_length":          uint32(m.Params.ContextSize),
+		"llama.embedding_length":        uint32(m.Params.HiddenSize),
+		"llama.feed_forward_length":     uint32(m.Params.IntermediateSize),
+		"llama.attention.head_count":    uint32(m.Params.AttentionHeads),
+		"llama.attention.head_count_kv": uint32(m.Params.KeyValHeads),
+
+		"llama.rope.freq_base":                   float32(m.Params.RopeFrequencyBase),
+		"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
+
+		"llama.expert_count":      uint32(m.Params.Experts),
+		"llama.expert_used_count": uint32(m.Params.ExpertsUsed),
+
+		"llama.vocab_size":           uint32(len(m.Vocab.Tokens)),
+		"llama.rope.dimension_count": uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
+
+		"general.file_type":    uint32(1),
+		"tokenizer.ggml.model": "llama",
+
+		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
+		"tokenizer.ggml.scores":     m.Vocab.Scores,
+		"tokenizer.ggml.token_type": m.Vocab.Types,
+
+		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
+		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
+		"tokenizer.ggml.unknown_token_id": uint32(0),
+		"tokenizer.ggml.add_bos_token":    true,
+		"tokenizer.ggml.add_eos_token":    false,
+	}
+
+	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
+}
+
+func (m *MixtralModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) {
+	return llamaRepack(name, m.Params, data, shape)
+}

convert/reader.go

@@ -1,74 +0,0 @@
-package convert
-
-import (
-	"errors"
-	"io"
-	"path/filepath"
-	"strings"
-)
-
-type Tensor interface {
-	Name() string
-	Shape() []uint64
-	Kind() uint32
-	SetRepacker(repacker)
-	WriteTo(io.Writer) (int64, error)
-}
-
-type tensorBase struct {
-	name  string
-	shape []uint64
-	repacker
-}
-
-func (t tensorBase) Name() string {
-	return t.name
-}
-
-func (t tensorBase) Shape() []uint64 {
-	return t.shape
-}
-
-func (t tensorBase) Kind() uint32 {
-	if strings.HasSuffix(t.name, ".block_sparse_moe.gate.weight") {
-		return 0
-	}
-
-	switch len(t.shape) {
-	case 0:
-		panic("invalid tensor shape")
-	case 1:
-		return 0
-	default:
-		return 1
-	}
-}
-
-func (t *tensorBase) SetRepacker(fn repacker) {
-	t.repacker = fn
-}
-
-type repacker func(string, []float32, []uint64) ([]float32, error)
-
-func parseTensors(d string) ([]Tensor, error) {
-	patterns := map[string]func(...string) ([]Tensor, error){
-		"model-*-of-*.safetensors": parseSafetensors,
-		"model.safetensors":        parseSafetensors,
-		"pytorch_model-*-of-*.bin": parseTorch,
-		"pytorch_model.bin":        parseTorch,
-		"consolidated.*.pth":       parseTorch,
-	}
-
-	for pattern, parseFn := range patterns {
-		matches, err := filepath.Glob(filepath.Join(d, pattern))
-		if err != nil {
-			return nil, err
-		}
-
-		if len(matches) > 0 {
-			return parseFn(matches...)
-		}
-	}
-
-	return nil, errors.New("unknown tensor format")
-}

convert/reader_npz.go

@@ -1,140 +0,0 @@
-package convert
-
-import (
-	"encoding/binary"
-	"fmt"
-	"io"
-	"log/slog"
-	"strings"
-
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-	"github.com/sbinet/npyio/npz"
-)
-
-type adapterTensor struct {
-	path  string
-	dtype string
-	*tensorBase
-}
-
-func DetectNPZ(fn string) (bool, error) {
-	f, err := npz.Open(fn)
-	if err != nil {
-		return false, err
-	}
-	defer f.Close()
-
-	if len(f.Keys()) > 0 && strings.HasSuffix(f.Keys()[0], ".npy") {
-		return true, nil
-	}
-
-	return false, nil
-}
-
-func parseNPZ(fn string) ([]Tensor, error) {
-	var ts []Tensor
-
-	f, err := npz.Open(fn)
-	if err != nil {
-		return nil, err
-	}
-	defer f.Close()
-
-	for _, name := range f.Keys() {
-		slog.Info(fmt.Sprintf("reading layer '%s'", name))
-		h := f.Header(name)
-
-		shape := make([]uint64, 2)
-		for cnt, v := range h.Descr.Shape {
-			// llamacpp expects the loraB layer to be reversed
-			if strings.Contains(name, "lora_b") {
-				shape[len(shape)-cnt-1] = uint64(v)
-			} else {
-				shape[cnt] = uint64(v)
-			}
-		}
-
-		dtypeMap := map[string]string{
-			"<f2": "F16",
-			"<f4": "F32",
-		}
-		dtype, ok := dtypeMap[h.Descr.Type]
-		if !ok {
-			return nil, fmt.Errorf("Unknown type '%s' for '%s'", h.Descr.Type, name)
-		}
-
-		ts = append(ts, adapterTensor{
-			path:  fn,
-			dtype: dtype,
-			tensorBase: &tensorBase{
-				name:  name,
-				shape: shape,
-			},
-		})
-	}
-	return ts, nil
-}
-
-func (t adapterTensor) Kind() uint32 {
-	switch t.dtype {
-	case "F32":
-		return 0
-	case "F16":
-		return 1
-	}
-	return 0
-}
-
-func (t adapterTensor) WriteTo(w io.Writer) (int64, error) {
-	f, err := npz.Open(t.path)
-	if err != nil {
-		return 0, err
-	}
-	defer f.Close()
-
-	switch t.dtype {
-	case "F32":
-		var f32s []float32
-		err = f.Read(t.tensorBase.name, &f32s)
-		if err != nil {
-			return 0, err
-		}
-
-		// ggla expects the loraB to be transposed
-		if strings.Contains(t.tensorBase.name, "lora_b") {
-			f32s, err = transpose(f32s, t.tensorBase.shape)
-			if err != nil {
-				return 0, err
-			}
-		}
-
-		return 0, binary.Write(w, binary.LittleEndian, f32s)
-	}
-
-	return 0, fmt.Errorf("unknown data type: %s", t.dtype)
-}
-
-func transpose(f32s []float32, shape []uint64) ([]float32, error) {
-	if len(shape) != 2 {
-		return nil, fmt.Errorf("only 2 dimensions supported for transpose")
-	}
-
-	// the shape is already backward
-	n := tensor.New(tensor.WithShape(int(shape[1]), int(shape[0])), tensor.WithBacking(f32s))
-	if err := n.T(1, 0); err != nil {
-		return nil, err
-	}
-	if err := n.Transpose(); err != nil {
-		return nil, err
-	}
-	ts, err := native.SelectF32(n, 1)
-	if err != nil {
-		return nil, err
-	}
-	f32s = make([]float32, 0)
-	for _, t := range ts {
-		f32s = append(f32s, t...)
-	}
-	return f32s, nil
-}

convert/reader_safetensors.go

@@ -1,140 +0,0 @@
-package convert
-
-import (
-	"bytes"
-	"encoding/binary"
-	"encoding/json"
-	"fmt"
-	"io"
-	"os"
-	"slices"
-
-	"github.com/d4l3k/go-bfloat16"
-	"github.com/x448/float16"
-	"golang.org/x/exp/maps"
-)
-
-type safetensorMetadata struct {
-	Type    string   `json:"dtype"`
-	Shape   []uint64 `json:"shape"`
-	Offsets []int64  `json:"data_offsets"`
-}
-
-func parseSafetensors(ps ...string) ([]Tensor, error) {
-	var ts []Tensor
-	for _, p := range ps {
-		f, err := os.Open(p)
-		if err != nil {
-			return nil, err
-		}
-		defer f.Close()
-
-		var n int64
-		if err := binary.Read(f, binary.LittleEndian, &n); err != nil {
-			return nil, err
-		}
-
-		b := bytes.NewBuffer(make([]byte, 0, n))
-		if _, err = io.CopyN(b, f, n); err != nil {
-			return nil, err
-		}
-
-		var headers map[string]safetensorMetadata
-		if err := json.NewDecoder(b).Decode(&headers); err != nil {
-			return nil, err
-		}
-
-		keys := maps.Keys(headers)
-		slices.Sort(keys)
-
-		for _, key := range keys {
-			if value := headers[key]; value.Type != "" {
-				ts = append(ts, safetensor{
-					path:   p,
-					dtype:  value.Type,
-					offset: safetensorsPad(n, value.Offsets[0]),
-					size:   safetensorsPad(n, value.Offsets[1]) - safetensorsPad(n, value.Offsets[0]),
-					tensorBase: &tensorBase{
-						name:  key,
-						shape: value.Shape,
-					},
-				})
-			}
-		}
-	}
-
-	return ts, nil
-}
-
-func safetensorsPad(n, s int64) int64 {
-	return 8 + n + s
-}
-
-type safetensor struct {
-	path   string
-	dtype  string
-	offset int64
-	size   int64
-	*tensorBase
-}
-
-func (st safetensor) WriteTo(w io.Writer) (int64, error) {
-	f, err := os.Open(st.path)
-	if err != nil {
-		return 0, err
-	}
-	defer f.Close()
-
-	if _, err = f.Seek(st.offset, io.SeekStart); err != nil {
-		return 0, err
-	}
-
-	var f32s []float32
-	switch st.dtype {
-	case "F32":
-		f32s = make([]float32, st.size/4)
-		if err = binary.Read(f, binary.LittleEndian, f32s); err != nil {
-			return 0, err
-		}
-	case "F16":
-		u16s := make([]uint16, st.size/2)
-		if err = binary.Read(f, binary.LittleEndian, u16s); err != nil {
-			return 0, err
-		}
-
-		for _, b := range u16s {
-			f32s = append(f32s, float16.Frombits(b).Float32())
-		}
-
-	case "BF16":
-		u8s := make([]uint8, st.size)
-		if err = binary.Read(f, binary.LittleEndian, u8s); err != nil {
-			return 0, err
-		}
-
-		f32s = bfloat16.DecodeFloat32(u8s)
-	default:
-		return 0, fmt.Errorf("unknown data type: %s", st.dtype)
-	}
-
-	if st.repacker != nil {
-		f32s, err = st.repacker(st.Name(), f32s, st.Shape())
-		if err != nil {
-			return 0, err
-		}
-	}
-
-	switch st.Kind() {
-	case 0:
-		return 0, binary.Write(w, binary.LittleEndian, f32s)
-	case 1:
-		f16s := make([]uint16, len(f32s))
-		for i := range f32s {
-			f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
-		}
-
-		return 0, binary.Write(w, binary.LittleEndian, f16s)
-	default:
-		return 0, fmt.Errorf("unknown storage type: %d", st.Kind())
-	}
-}

convert/reader_torch.go

@@ -1,46 +0,0 @@
-package convert
-
-import (
-	"io"
-
-	"github.com/nlpodyssey/gopickle/pytorch"
-	"github.com/nlpodyssey/gopickle/types"
-)
-
-func parseTorch(ps ...string) ([]Tensor, error) {
-	var ts []Tensor
-	for _, p := range ps {
-		pt, err := pytorch.Load(p)
-		if err != nil {
-			return nil, err
-		}
-
-		for _, k := range pt.(*types.Dict).Keys() {
-			t := pt.(*types.Dict).MustGet(k)
-
-			var shape []uint64
-			for dim := range t.(*pytorch.Tensor).Size {
-				shape = append(shape, uint64(dim))
-			}
-
-			ts = append(ts, torch{
-				storage: t.(*pytorch.Tensor).Source,
-				tensorBase: &tensorBase{
-					name:  k.(string),
-					shape: shape,
-				},
-			})
-		}
-	}
-
-	return ts, nil
-}
-
-type torch struct {
-	storage pytorch.StorageInterface
-	*tensorBase
-}
-
-func (pt torch) WriteTo(w io.Writer) (int64, error) {
-	return 0, nil
-}

convert/safetensors.go

@@ -0,0 +1,309 @@
+package convert
+
+import (
+	"bytes"
+	"encoding/binary"
+	"encoding/json"
+	"fmt"
+	"io"
+	"os"
+	"path/filepath"
+	"regexp"
+	"slices"
+	"strings"
+
+	"github.com/d4l3k/go-bfloat16"
+	"github.com/x448/float16"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type safetensorWriterTo struct {
+	t *llm.Tensor
+
+	params *Params
+	bo     ByteOrder
+
+	filename string
+	dtype    string
+
+	offset, size int64
+	repacker     func(string, []float32, []uint64) ([]float32, error)
+}
+
+type safetensorMetadata struct {
+	Type    string   `json:"dtype"`
+	Shape   []uint64 `json:"shape"`
+	Offsets []int64  `json:"data_offsets"`
+}
+
+type SafetensorFormat struct{}
+
+func (m *SafetensorFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
+	var tensors []llm.Tensor
+	matches, err := filepath.Glob(filepath.Join(dirpath, "*.safetensors"))
+	if err != nil {
+		return nil, err
+	}
+
+	var offset uint64
+	for _, f := range matches {
+		var t []llm.Tensor
+		var err error
+		t, offset, err = m.readTensors(f, offset, params)
+		if err != nil {
+			return nil, err
+		}
+
+		tensors = append(tensors, t...)
+	}
+	return tensors, nil
+}
+
+func (m *SafetensorFormat) readTensors(fn string, offset uint64, params *Params) ([]llm.Tensor, uint64, error) {
+	f, err := os.Open(fn)
+	if err != nil {
+		return nil, 0, err
+	}
+	defer f.Close()
+
+	var n int64
+	if err := binary.Read(f, binary.LittleEndian, &n); err != nil {
+		return nil, 0, err
+	}
+
+	b := bytes.NewBuffer(make([]byte, 0, n))
+	if _, err = io.CopyN(b, f, n); err != nil {
+		return nil, 0, err
+	}
+
+	var headers map[string]safetensorMetadata
+	if err := json.NewDecoder(b).Decode(&headers); err != nil {
+		return nil, 0, err
+	}
+
+	var keys []string
+	for key := range headers {
+		if !strings.HasSuffix(key, "self_attn.rotary_embd.inv_freq") {
+			keys = append(keys, key)
+		}
+	}
+
+	slices.Sort(keys)
+
+	var tensors []llm.Tensor
+	for _, key := range keys {
+		value := headers[key]
+
+		var kind uint32
+		switch len(value.Shape) {
+		case 0:
+			// valuedata
+			continue
+		case 2:
+			kind = 1
+		}
+
+		name, err := m.GetLayerName(key)
+		if err != nil {
+			return nil, 0, err
+		}
+
+		shape := make([]uint64, len(value.Shape))
+		copy(shape, value.Shape)
+
+		pad := func(s int64) int64 {
+			return 8 + n + s
+		}
+
+		t := llm.Tensor{
+			Name:   name,
+			Kind:   kind,
+			Offset: offset,
+			Shape:  shape,
+		}
+
+		t.WriterTo = safetensorWriterTo{
+			t:        &t,
+			params:   params,
+			bo:       params.ByteOrder,
+			filename: fn,
+			dtype:    value.Type,
+			offset:   pad(value.Offsets[0]),
+			size:     pad(value.Offsets[1]) - pad(value.Offsets[0]),
+		}
+
+		offset += t.Size()
+		tensors = append(tensors, t)
+	}
+
+	return tensors, offset, nil
+}
+
+func (m *SafetensorFormat) GetParams(dirpath string) (*Params, error) {
+	f, err := os.Open(filepath.Join(dirpath, "config.json"))
+	if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	var params Params
+
+	if err := json.NewDecoder(f).Decode(&params); err != nil {
+		return nil, err
+	}
+
+	params.ByteOrder = binary.LittleEndian
+	return &params, nil
+}
+
+func (m *SafetensorFormat) GetLayerName(n string) (string, error) {
+	directMap := map[string]string{
+		"model.embed_tokens.weight": "token_embd.weight",
+		"lm_head.weight":            "output.weight",
+		"model.norm.weight":         "output_norm.weight",
+	}
+
+	tMap := map[string]string{
+		"model.layers.(\\d+).input_layernorm.weight":                    "blk.$1.attn_norm.weight",
+		"model.layers.(\\d+).mlp.down_proj.weight":                      "blk.$1.ffn_down.weight",
+		"model.layers.(\\d+).mlp.gate_proj.weight":                      "blk.$1.ffn_gate.weight",
+		"model.layers.(\\d+).mlp.up_proj.weight":                        "blk.$1.ffn_up.weight",
+		"model.layers.(\\d+).post_attention_layernorm.weight":           "blk.$1.ffn_norm.weight",
+		"model.layers.(\\d+).self_attn.k_proj.weight":                   "blk.$1.attn_k.weight",
+		"model.layers.(\\d+).self_attn.o_proj.weight":                   "blk.$1.attn_output.weight",
+		"model.layers.(\\d+).self_attn.q_proj.weight":                   "blk.$1.attn_q.weight",
+		"model.layers.(\\d+).self_attn.v_proj.weight":                   "blk.$1.attn_v.weight",
+		"model.layers.(\\d+).block_sparse_moe.gate.weight":              "blk.$1.ffn_gate_inp.weight",
+		"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w1.weight": "blk.$1.ffn_gate.$2.weight",
+		"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w2.weight": "blk.$1.ffn_down.$2.weight",
+		"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w3.weight": "blk.$1.ffn_up.$2.weight",
+	}
+
+	v, ok := directMap[n]
+	if ok {
+		return v, nil
+	}
+
+	// quick hack to rename the layers to gguf format
+	for k, v := range tMap {
+		re := regexp.MustCompile(k)
+		newName := re.ReplaceAllString(n, v)
+		if newName != n {
+			return newName, nil
+		}
+	}
+
+	return "", fmt.Errorf("couldn't find a layer name for '%s'", n)
+}
+
+func (r safetensorWriterTo) WriteTo(w io.Writer) (n int64, err error) {
+	f, err := os.Open(r.filename)
+	if err != nil {
+		return 0, err
+	}
+	defer f.Close()
+
+	if _, err = f.Seek(r.offset, io.SeekStart); err != nil {
+		return 0, err
+	}
+
+	var f32s []float32
+	switch r.dtype {
+	case "F32":
+		f32s = make([]float32, r.size/4)
+		if err = binary.Read(f, r.bo, f32s); err != nil {
+			return 0, err
+		}
+	case "F16":
+		u16s := make([]uint16, r.size/2)
+		if err = binary.Read(f, r.bo, u16s); err != nil {
+			return 0, err
+		}
+
+		for _, b := range u16s {
+			f32s = append(f32s, float16.Frombits(b).Float32())
+		}
+
+	case "BF16":
+		u8s := make([]uint8, r.size)
+		if err = binary.Read(f, r.bo, u8s); err != nil {
+			return 0, err
+		}
+
+		f32s = bfloat16.DecodeFloat32(u8s)
+	default:
+		return 0, fmt.Errorf("unknown data type: %s", r.dtype)
+	}
+
+	if r.repacker != nil {
+		f32s, err = r.repacker(r.t.Name, f32s, r.t.Shape)
+		if err != nil {
+			return 0, err
+		}
+	}
+
+	switch r.t.Kind {
+	case 0:
+		return 0, binary.Write(w, r.bo, f32s)
+	case 1:
+		f16s := make([]uint16, len(f32s))
+		for i := range f32s {
+			f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
+		}
+
+		return 0, binary.Write(w, r.bo, f16s)
+	default:
+		return 0, fmt.Errorf("unknown storage type: %d", r.t.Kind)
+	}
+}
+
+func (m *SafetensorFormat) GetModelArch(name, dirPath string, params *Params) (ModelArch, error) {
+	switch len(params.Architectures) {
+	case 0:
+		return nil, fmt.Errorf("No architecture specified to convert")
+	case 1:
+		switch params.Architectures[0] {
+		case "LlamaForCausalLM":
+			return &LlamaModel{
+				ModelData{
+					Name:   name,
+					Path:   dirPath,
+					Params: params,
+					Format: m,
+				},
+			}, nil
+		case "MistralForCausalLM":
+			return &MistralModel{
+				ModelData{
+					Name:   name,
+					Path:   dirPath,
+					Params: params,
+					Format: m,
+				},
+			}, nil
+		case "MixtralForCausalLM":
+			return &MixtralModel{
+				ModelData{
+					Name:   name,
+					Path:   dirPath,
+					Params: params,
+					Format: m,
+				},
+			}, nil
+		case "GemmaForCausalLM":
+			return &GemmaModel{
+				ModelData{
+					Name:   name,
+					Path:   dirPath,
+					Params: params,
+					Format: m,
+				},
+			}, nil
+		default:
+			return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
+		}
+	}
+
+	return nil, fmt.Errorf("Unknown error")
+}

convert/testdata/Meta-Llama-3-8B-Instruct.json

@@ -1,313 +0,0 @@
-{
-  "general.architecture": "llama",
-  "general.file_type": "1",
-  "general.quantization_version": "2",
-  "llama.block_count": "32",
-  "llama.context_length": "8192",
-  "llama.embedding_length": "4096",
-  "llama.feed_forward_length": "14336",
-  "llama.rope.dimension_count": "128",
-  "llama.rope.freq_base": "500000",
-  "llama.vocab_size": "128256",
-  "llama.attention.head_count": "32",
-  "llama.attention.head_count_kv": "8",
-  "llama.attention.layer_norm_rms_epsilon": "1e-05",
-  "tokenizer.ggml.model": "gpt2",
-  "tokenizer.ggml.pre": "llama-bpe",
-  "tokenizer.ggml.bos_token_id": "128000",
-  "tokenizer.ggml.eos_token_id": "128009",
-  "tokenizer.ggml.merges": "d0cbac1fcc9dcf03724b8db5c9bfb593ae1cf68fb9bc72eb1d15274dcbbf618b",
-  "tokenizer.ggml.token_type": "d70a88809fd7da6f1f028622685cd64268a7a922c5d343c96f25b66327358978",
-  "tokenizer.ggml.tokens": "765b529dbcbc42dd202ce657341c63807b51f3b07e09898f6aa6196326865d5a",
-  "token_embd.weight": "b53102a11d9064bbd404833e3464b1b13e08ce73300b442312cccde2f19b2698",
-  "blk.0.attn_norm.weight": "7318df3cca9e8d153ff0a503026a1265e63d20b2a8c1dd7a2769585082b5d1ee",
-  "blk.0.ffn_down.weight": "b950806a1fc722c9fad7fd0b20c3c0a7fb50f14395e1e7663a590bfd62e20900",
-  "blk.0.ffn_gate.weight": "e73e580af6d4f08e060a74a3c25efdf5d3bed99e183d95a5a85ae859014839fd",
-  "blk.0.ffn_up.weight": "c8158af679ef99746da1befb67eebb19489e0bbe6ce7d97e13e348508244e516",
-  "blk.0.ffn_norm.weight": "7ec69c3c31e95e49a3359003b0033f6b9e85561a3e3fd83e7476661ecdd756bb",
-  "blk.0.attn_k.weight": "2732303257bac969b4964e0e32ec08b5a7f5c031bb02bf6ac4467b3ea0ebcf1e",
-  "blk.0.attn_output.weight": "ecda1d43b4ccc91cd5b366d7e7a275353990ac78561a07c83d9c77031aba12dc",
-  "blk.0.attn_q.weight": "569b1f5faf92b6f00910cf7effb2d5862f91038ce5c3b0019fc10e5d79fbd5e1",
-  "blk.0.attn_v.weight": "aa8416c5ef7e32fb54a1f20d6ac651656845d4af240564b397c39bd83e06e3b8",
-  "blk.1.attn_norm.weight": "03327e02862908c2a44b2f52decdb924bf4201f400b46f8037a9cb2e1d7a61ff",
-  "blk.1.ffn_down.weight": "5a83a87603f38c99f8e1e370a2d5f967bb45ac51d881a609304a7811027321e0",
-  "blk.1.ffn_gate.weight": "31da0572c79e655186c721c231376f85e56cdcc6257c28d08c8c5b40d5c22b40",
-  "blk.1.ffn_up.weight": "e0c811d64ca155c8de10a868e72015d43888834804614ee1aa2953129ffbc90f",
-  "blk.1.ffn_norm.weight": "5861f313d6137d6f0f904d423df47fffc6069e224ff746e1b637ac9c7f0af862",
-  "blk.1.attn_k.weight": "5fbbec0acca6457b9416ebdcd90e526885d0224537b7628f6be376a7f275313d",
-  "blk.1.attn_output.weight": "b237c9763fa3f75166a6f70b70f1566e77d0d89dfa164ed1b3137393e90575c3",
-  "blk.1.attn_q.weight": "c0a9cf4a98b4882b16f3eb2b49d933793dcc5357abb246fd3fe3134ed2b12e1c",
-  "blk.1.attn_v.weight": "96867111727200cac1af7865189dd41fd62b47584e5e5f33a91f1d34509cbd40",
-  "blk.2.attn_norm.weight": "f392f8a88ee3a95b1cc19c40dd4ef66317037b0faaa1800f610779e129ee0539",
-  "blk.2.ffn_down.weight": "73823eef46632aedcc8c1cb08a736b6aa97ca97842cd1fdfc5567d8dec459662",
-  "blk.2.ffn_gate.weight": "f4909ae19fc3848b00bb8b9050122e74f8e903b89e22937036f4cc9fea20a718",
-  "blk.2.ffn_up.weight": "16f4904a3d814ea68f00519724fc4943e48444a84c786bda39aa5efc298a7d84",
-  "blk.2.ffn_norm.weight": "e3ccdf56e75cb969f6f69c39caf6daf7c4e70e89e25df0f4d2e4bc60e159aafe",
-  "blk.2.attn_k.weight": "c3beb1e0a11bcf007ef0f0d8f6bdd3082d8b29090cd29597846b5d51e308a8e5",
-  "blk.2.attn_output.weight": "bb9f66c32cff51154fea92933c2cd62549236f8cb1a767f9ef28d3f99809b343",
-  "blk.2.attn_q.weight": "8eba394132eef2a05c5a92d62d2376000f7948448d7a2dc74e6b608203add20d",
-  "blk.2.attn_v.weight": "88f61f77c53567c617db3eef8f30621109a750e679f6784f7911739bd42c2f02",
-  "blk.3.attn_norm.weight": "7b996675b7ca75fa24107b3ebe0788653ede0f49ac83b8659d71ff54d591f81a",
-  "blk.3.ffn_down.weight": "2cb332bc05e4821962fdc9dcbcc7cc12630f32117711b687d18fb53c0bc4fbf4",
-  "blk.3.ffn_gate.weight": "340b387c7f208c8f0a6db904ef8d87c1e84b7d6ad57177abd32d86c8d18b760f",
-  "blk.3.ffn_up.weight": "07484433f8a7ee061c55aa0de2ecc009f769b0617c9c0ec096e9bb2946df9f0e",
-  "blk.3.ffn_norm.weight": "4f1a4ade36b393af341240bc894a2aab09cff7e4d56dc4658445deb107f9371b",
-  "blk.3.attn_k.weight": "483dcd96acb4528df84b9842970994630dbd82b8715ace394aa8b39fcf8d6291",
-  "blk.3.attn_output.weight": "beaff0810687923585642ee11d929cbf3b43dc6f87f30ddb552c222ab57bdbb3",
-  "blk.3.attn_q.weight": "0739355002f6fce520863add697e0ff25fc88215322dc3f993be7bb68dcce7e8",
-  "blk.3.attn_v.weight": "c216d17b6d90ee3e07f82598b8161fae34de2f392dbb0f745b682b578c324767",
-  "blk.4.attn_norm.weight": "91ab405bc4ba15bf63af233f266aa43aaab43789a9e6596e14a357c2ac7df217",
-  "blk.4.ffn_down.weight": "620f34ee75cdc73aecb8949af5fbb0d2437fd81422b6d8eb7acfc52addb9fc68",
-  "blk.4.ffn_gate.weight": "f6feec7bc9acadf35ec22532f8998d8e50f31afedabb19263590dcf8b9a92eee",
-  "blk.4.ffn_up.weight": "4a72af7cd28fd07b038f6cc4406678d120517280236ea85d9e76eff40ab2cc22",
-  "blk.4.ffn_norm.weight": "1805b37b44d5d682bdbd2fadeafb763ee001617d7870848cc487079ee34b21f9",
-  "blk.4.attn_k.weight": "a1e4f9d97cdf4c1b0d177cf00c4e32d1be30c1984a239b3c9bd73f8848888853",
-  "blk.4.attn_output.weight": "a1547e2497c423b0aff0eee71d9300d6fdf4e4986679418b6e637b69a9a6720b",
-  "blk.4.attn_q.weight": "0677483a9264ea6803d03d304d87a54632242cb516e8b76b6e3e8284c2f4de04",
-  "blk.4.attn_v.weight": "02691ba3af344fcc1969428ab0df811ac94aaa2fd91b0dc4ec1ac0a58806980d",
-  "blk.5.attn_norm.weight": "ba9c028335e5c895b87a5bd1448ca429248f9746ed97bdcb8679923206117156",
-  "blk.5.ffn_down.weight": "ccfdc9006acad1940a6bc05042a3947f1066acd671e0bb53b7684e9eea9ef5c9",
-  "blk.5.ffn_gate.weight": "623157679f1e742ccc3807c0b0153ddc8450104de75ec62f1370ec3807c09cf4",
-  "blk.5.ffn_up.weight": "05748804c65091f963729b58b085f58351891cac8a2861f5eae26b06aa60b2a0",
-  "blk.5.ffn_norm.weight": "84bae55af2efc8b8429f09056c8c04990c466dae31cb3f9356038b8957f1b406",
-  "blk.5.attn_k.weight": "8c766180c726b037d587fc52371de6e3307140c52409011609d1225624b6a3eb",
-  "blk.5.attn_output.weight": "490b582b3b1dc151ae55aee8b6743dad6c01fb49e43afefb6e68394b74be3d73",
-  "blk.5.attn_q.weight": "6f7b8ca4d9025ec836a44bbcca46be30c66b471a9fb62943ddff8288b3731409",
-  "blk.5.attn_v.weight": "9f70df3ba00c9e723214b3da83ff435a2163fff5915f75515c9664c05c866c27",
-  "blk.6.attn_norm.weight": "1a4a66613a682df6f061fc7c4d986f9f7e9175b62f0c42fc1ef31db536bd5942",
-  "blk.6.ffn_down.weight": "c56f25e4e49b443dbc82d88311ee63bc1f5002cc67e52f4787fd5f003aedeac1",
-  "blk.6.ffn_gate.weight": "31a5cf1aa9b831a81588d508550f51fc425f9517c43254d4ef7096d38029cf04",
-  "blk.6.ffn_up.weight": "ce135f3a1163e0c9297a615bdbe68a67ead21edce8debbfa9f6e15e6af8d4c94",
-  "blk.6.ffn_norm.weight": "4e328ce0648c94e732bc40501858ef6262ad1161e2e407b0cdcf4813fa9d45d8",
-  "blk.6.attn_k.weight": "1eb1c4c9f9c4c7ff7f5429075e0dc6a7782bed55109fa88df209a817dd8ef960",
-  "blk.6.attn_output.weight": "3d32986b56873b88655ee1edabdd413fdd9ab18b82108c9ce90bdbc2d3a6f3a3",
-  "blk.6.attn_q.weight": "8432f583b3a2809c99c393f9beb077cb0534dd5d247c17108f2986cadc6651f6",
-  "blk.6.attn_v.weight": "5045381513815bb91839dbac8335ffe49bbc7b0008369de7ea97eb676c5e2b36",
-  "blk.7.attn_norm.weight": "3dabd003638ec2499bfc8a48c49eef34276caab4fe76894eb963207848c2fdaf",
-  "blk.7.ffn_down.weight": "194fae858608bdcffd235be59ab119d0b91c8549f864ea06dae69249e099935f",
-  "blk.7.ffn_gate.weight": "00b24c29c30246892bce0791be804a89701d4c1332777e0bcdad5d9d5666604f",
-  "blk.7.ffn_up.weight": "44d7082a5280080c90cef9e19d410391de34f212ca0736377769b8ddd0c82d5e",
-  "blk.7.ffn_norm.weight": "21fe8a7fd6911c64e0d15a788b3b4cb6d71dd6ec51de65f760ee89afbb6ae53e",
-  "blk.7.attn_k.weight": "57a149eec5f6744a9526cd3925ac073f9d12db0fbcb5afe042ef4dc846458c44",
-  "blk.7.attn_output.weight": "0e9c28a3e81a2880251ce5eed77bcb8be8aaa1a51c9cb6de820b47ed83849fc2",
-  "blk.7.attn_q.weight": "15ee75263ee4e2a43eb322bc159ae004bb7d77e3a7e63ee4ddab700430693fff",
-  "blk.7.attn_v.weight": "440aa970bba4bff429fd7b7b1de21f2ad14fb2952b776cfa4acee68d7c6e9b8f",
-  "blk.8.attn_norm.weight": "af5b44825633c42c1ae964c82bb2be6a242d3a751f0a91f1bae4f593e8f5b6ec",
-  "blk.8.ffn_down.weight": "b11c14c76adca94fa200496dd2c10743becb23aab6642443ef1ae6d8710edbc1",
-  "blk.8.ffn_gate.weight": "7bb03d3325bf8637ae2fa1296b0651356515578d46a7c5ca65c7a923d7de27bc",
-  "blk.8.ffn_up.weight": "b956ef0a0669b5a9c9bf3a8da2d1c24f52d331cfb7354f6d7c51bd65be355e30",
-  "blk.8.ffn_norm.weight": "c78c3d748302edfef76f71ea5cb2055c94352122eee8b9b1173779a1814d224e",
-  "blk.8.attn_k.weight": "c0fba6a596ed9c1c32a7055c31a935a8b31e42b77282ee47c1f03ee3bde736b5",
-  "blk.8.attn_output.weight": "83cf9947080c5d8d571f04a842bc3dcfe7bbb0195fb25b346e22635e8649f2d4",
-  "blk.8.attn_q.weight": "47409350a576b333d97b7c877d69f47f46df504f3765102dfc0be9e521c7ecd6",
-  "blk.8.attn_v.weight": "1999dff91404fdcf1ecb34d9eaaaa9244ec7658a74dec8feb7cfd1fddba0347e",
-  "blk.9.attn_norm.weight": "1e6e29d5c3889ab4e1b0a5b9998cba60179b0f1fca133515df49cbc19d092593",
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-}

convert/testdata/Mistral-7B-Instruct-v0.2.json

@@ -1,313 +0,0 @@
-{
-  "general.architecture": "llama",
-  "general.file_type": "1",
-  "general.quantization_version": "2",
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-  "llama.attention.layer_norm_rms_epsilon": "1e-05",
-  "llama.rope.dimension_count": "128",
-  "tokenizer.ggml.model": "llama",
-  "tokenizer.ggml.add_bos_token": "true",
-  "tokenizer.ggml.add_eos_token": "false",
-  "tokenizer.ggml.bos_token_id": "1",
-  "tokenizer.ggml.eos_token_id": "2",
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-}

convert/testdata/Mixtral-8x7B-Instruct-v0.1.json

@@ -1,348 +0,0 @@
-{
-  "general.architecture": "llama",
-  "general.file_type": "1",
-  "general.quantization_version": "2",
-  "llama.block_count": "32",
-  "llama.context_length": "32768",
-  "llama.embedding_length": "4096",
-  "llama.feed_forward_length": "14336",
-  "llama.rope.dimension_count": "128",
-  "llama.rope.freq_base": "1e+06",
-  "llama.attention.head_count": "32",
-  "llama.attention.head_count_kv": "8",
-  "llama.attention.layer_norm_rms_epsilon": "1e-05",
-  "llama.expert_count": "8",
-  "llama.expert_used_count": "2",
-  "tokenizer.ggml.model": "llama",
-  "tokenizer.ggml.add_bos_token": "true",
-  "tokenizer.ggml.add_eos_token": "false",
-  "tokenizer.ggml.bos_token_id": "1",
-  "tokenizer.ggml.eos_token_id": "2",
-  "tokenizer.ggml.unknown_token_id": "0",
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convert/testdata/gemma-2b-it.json

@@ -1,188 +0,0 @@
-{
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-  "general.file_type": "1",
-  "general.quantization_version": "2",
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-  "blk.16.attn_v.weight": "98b33fcb0ee3483cff1b06ecb44d7b7ffb4d34c268248e4d73dfdf82b2065b2f",
-  "blk.16.ffn_down.weight": "14006f5e4acb2f9416271ae562e299359cd2585739c7fc77ccbca54495563948",
-  "blk.16.ffn_gate.weight": "12f8abae2d301d8f88bedb6af98b1daecc7b0b8d05148594f931f30958d77aca",
-  "blk.16.ffn_norm.weight": "129a15a046ee96d06de288bd43c80f77a6b0fb3a159c7367154c6e4aaf362672",
-  "blk.16.ffn_up.weight": "b4a5911a45f3871ef1d4efb7dc7108645a564b70f818eccf45beebef2e844ee9",
-  "blk.17.attn_k.weight": "5e1bfcff0146ebdde3817b656952892eb671e14e75afc92fa53f84f8eecbec4c",
-  "blk.17.attn_norm.weight": "60bc988fab7c4b29ee9de599df41a8de00caa94fcd74677da011fac82f60f465",
-  "blk.17.attn_output.weight": "ba49b40d6a0b5685f749c24b0edbed3adc44dbe13b5d5e5fa1e56169fc746555",
-  "blk.17.attn_q.weight": "82bb415d24efcd14d03ace03f907bb70db6a204c76a0bdd1892e0fba165db87d",
-  "blk.17.attn_v.weight": "73dbe54beb91a899884e275ea81ffc5187a20cb7d5b68d5c299b783096999d94",
-  "blk.17.ffn_down.weight": "7c086166241e0664f8963fd1ca4ed74c737abfb2525ec20f8435821ff50158f3",
-  "blk.17.ffn_gate.weight": "51a32f78244d42a539f619c5ce661db9e6cf41636280a826d439b5444edcd28c",
-  "blk.17.ffn_norm.weight": "c4bb247fccd1ecc84875028af63dd20aaf5cbd17eb94a9bc36679c09285dccab",
-  "blk.17.ffn_up.weight": "b5886182790bc6fbadd63de9bc4ffee416f3b69a66280d197ab8c18edf769abf",
-  "output_norm.weight": "481f3097d0a20412e35b3a739b1b958487bcd41ff67744baa3c9acbddd2ee4d4"
-}

convert/tokenizer.go

@@ -3,148 +3,19 @@ package convert
 import (
 	"cmp"
 	"crypto/sha256"
-	"encoding/hex"
 	"encoding/json"
-	"errors"
 	"fmt"
 	"log/slog"
 	"os"
-	"path/filepath"
 	"slices"
-)
 
-const (
-	_ int32 = iota
-	tokenTypeNormal
-	tokenTypeUnknown
-	tokenTypeControl
-	tokenTypeUserDefined
-	tokenTypeUnused
-	tokenTypeByte
+	"golang.org/x/exp/maps"
 )
 
 type Tokenizer struct {
-	*Vocabulary
-	SpecialVocabulary []*SpecialVocabulary
-	Merges            []string
-
-	Pre      string
-	Template string
-}
-
-func parseTokenizer(d string, specialTypes []string) (*Tokenizer, error) {
-	v, err := parseVocabulary(d)
-	if err != nil {
-		return nil, err
-	}
-
-	t := &Tokenizer{
-		Vocabulary: v,
-		Pre:        "default",
-	}
-
-	addedTokens := make(map[string]token)
-	if f, err := os.Open(filepath.Join(d, "tokenizer.json")); errors.Is(err, os.ErrNotExist) {
-	} else if err != nil {
-		return nil, err
-	} else {
-		defer f.Close()
-
-		var tt tokenizer
-		if err := json.NewDecoder(f).Decode(&tt); err != nil {
-			return nil, err
-		}
-
-		for _, t := range tt.AddedTokens {
-			addedTokens[t.Content] = t
-		}
-
-		t.Merges = tt.Model.Merges
-
-		sha256sum := sha256.New()
-		for _, pt := range tt.PreTokenizer.PreTokenizers {
-			switch pt.Type {
-			case "Split":
-				if pt.Pattern.Regex != "" {
-					sha256sum.Write([]byte(pt.Pattern.Regex))
-				}
-			}
-		}
-
-		switch digest := hex.EncodeToString(sha256sum.Sum(nil)); digest {
-		case "d98f9631be1e9607a9848c26c1f9eac1aa9fc21ac6ba82a2fc0741af9780a48f":
-			t.Pre = "llama-bpe"
-		case "03df5c5863ad70781dcfdef491ead25140f895fe8010964be0daefe27be32b02":
-			t.Pre = "deepseek-llm"
-		case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
-			t.Pre = "deepseek-coder"
-		case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
-			// noop, empty pretokenizer
-		default:
-			slog.Warn("unknown pretokenizer, using default", "digest", digest)
-		}
-	}
-
-	if f, err := os.Open(filepath.Join(d, "tokenizer_config.json")); errors.Is(err, os.ErrNotExist) {
-	} else if err != nil {
-		return nil, err
-	} else {
-		defer f.Close()
-
-		var p map[string]json.RawMessage
-		if err := json.NewDecoder(f).Decode(&p); err != nil {
-			return nil, err
-		}
-
-		if template, ok := p["chat_template"]; ok {
-			if err := json.Unmarshal(template, &t.Template); err != nil {
-				return nil, err
-			}
-		}
-
-		for _, st := range specialTypes {
-			sv := SpecialVocabulary{Type: st}
-			if bts, ok := p[fmt.Sprintf("add_%s_token", st)]; ok {
-				if err := json.Unmarshal(bts, &sv.AddToken); err != nil {
-					return nil, err
-				}
-			}
-
-			if bts, ok := p[fmt.Sprintf("%s_token", st)]; ok {
-				var content string
-				if err := json.Unmarshal(bts, &content); err != nil {
-					var mm map[string]any
-					if err := json.Unmarshal(bts, &mm); err != nil {
-						continue
-					}
-
-					content, ok = mm["content"].(string)
-					if !ok {
-						continue
-					}
-				}
-
-				sv.Content = content
-			}
-
-			if id, ok := addedTokens[sv.Content]; ok {
-				sv.ID = id.ID
-				t.SpecialVocabulary = append(t.SpecialVocabulary, &sv)
-			}
-		}
-	}
-
-	return t, nil
-}
-
-type tokenizer struct {
-	Version     string  `json:"version"`
-	AddedTokens []token `json:"added_tokens"`
-	Model       struct {
-		Type   string         `json:"type"`
-		Vocab  map[string]int `json:"vocab"`
-		Merges []string       `json:"merges"`
-	} `json:"model"`
+	Version     string         `json:"version"`
+	AddedTokens []Token        `json:"added_tokens"`
+	Model       TokenizerModel `json:"model"`
 
 	PreTokenizer struct {
 		PreTokenizers []struct {
@@ -156,106 +27,80 @@ type tokenizer struct {
 	} `json:"pre_tokenizer"`
 }
 
-type token struct {
+type TokenizerModel struct {
+	Type   string         `json:"type"`
+	Vocab  map[string]int `json:"vocab"`
+	Merges []string       `json:"merges"`
+	Tokens []Token
+}
+
+type Token struct {
 	ID          int    `json:"id"`
 	Content     string `json:"content"`
 	Special     bool   `json:"special"`
 	UserDefined bool
 }
 
-type Vocabulary struct {
-	Model  string
-	Tokens []string
-	Scores []float32
-	Types  []int32
+func (t *Token) Type() int32 {
+	switch {
+	case t.Special:
+		return tokenTypeControl
+	case t.UserDefined:
+		return tokenTypeUserDefined
+	default:
+		return tokenTypeNormal
+	}
 }
 
-func parseVocabularyFromTokenizer(p string) (*Vocabulary, error) {
-	f, err := os.Open(filepath.Join(p, "tokenizer.json"))
+func (t *Tokenizer) maxID() int {
+	return max(
+		slices.Max(maps.Values(t.Model.Vocab)),
+		slices.MaxFunc(t.AddedTokens, func(a, b Token) int {
+			return cmp.Compare(a.ID, b.ID)
+		}).ID,
+	)
+}
+
+func parseTokens(dirpath string) (pre string, tokens []Token, merges []string, err error) {
+	f, err := os.Open(dirpath)
 	if err != nil {
-		return nil, err
+		panic(err)
 	}
 	defer f.Close()
 
-	var t tokenizer
+	var t Tokenizer
 	if err := json.NewDecoder(f).Decode(&t); err != nil {
-		return nil, err
+		return "", nil, nil, err
 	}
 
-	var tokens []token
+	tokens = make([]Token, t.maxID()+1)
 	for k, v := range t.Model.Vocab {
-		tokens = append(tokens, token{
-			ID:      v,
-			Content: k,
-		})
+		tokens[v] = Token{ID: v, Content: k, Special: false, UserDefined: false}
 	}
 
-	for _, t := range t.AddedTokens {
-		t.UserDefined = true
-		tokens = append(tokens, t)
+	for _, v := range t.AddedTokens {
+		v.UserDefined = true
+		tokens[v.ID] = v
 	}
 
-	slices.SortFunc(tokens, func(i, j token) int {
-		return cmp.Compare(i.ID, j.ID)
-	})
-
-	v := Vocabulary{Model: "gpt2"}
-	for _, t := range tokens {
-		v.Tokens = append(v.Tokens, t.Content)
-		v.Scores = append(v.Scores, float32(t.ID))
-
-		switch {
-		case t.Special:
-			v.Types = append(v.Types, tokenTypeControl)
-		case t.UserDefined:
-			v.Types = append(v.Types, tokenTypeUserDefined)
-		default:
-			v.Types = append(v.Types, tokenTypeNormal)
+	sha256sum := sha256.New()
+	for _, pt := range t.PreTokenizer.PreTokenizers {
+		if pt.Type == "Split" && pt.Pattern.Regex != "" {
+			sha256sum.Write([]byte(pt.Pattern.Regex))
 		}
 	}
 
-	return &v, nil
-}
-
-func parseVocabulary(d string) (*Vocabulary, error) {
-	patterns := map[string]func(string) (*Vocabulary, error){
-		"tokenizer.model": parseSentencePiece,
-		"tokenizer.json":  parseVocabularyFromTokenizer,
+	switch digest := fmt.Sprintf("%x", sha256sum.Sum(nil)); digest {
+	case "d98f9631be1e9607a9848c26c1f9eac1aa9fc21ac6ba82a2fc0741af9780a48f":
+		pre = "llama-bpe"
+	case "03df5c5863ad70781dcfdef491ead25140f895fe8010964be0daefe27be32b02":
+		pre = "deepseek-llm"
+	case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
+		pre = "deepseek-coder"
+	default:
+		slog.Warn("unknown pretokenizer, using default", "digest", digest)
+		pre = "default"
 	}
 
-	for pattern, parseFn := range patterns {
-		matches, err := filepath.Glob(filepath.Join(d, pattern))
-		if err != nil {
-			return nil, err
-		}
-
-		if len(matches) > 0 {
-			return parseFn(d)
-		}
-	}
-
-	return nil, errors.New("unknown tensor format")
-}
-
-type SpecialVocabulary struct {
-	Type     string
-	ID       int
-	Content  string
-	AddToken bool
-}
-
-func (sv SpecialVocabulary) Key() string {
-	switch t := sv.Type; t {
-	case "bos", "eos", "cls", "mask":
-		return t
-	case "unk":
-		return "unknown"
-	case "sep":
-		//nolint:misspell // this is an upstream typo
-		return "seperator"
-	case "pad":
-		return "padding"
-	}
-
-	panic("unknown special vocabulary type")
+	return pre, tokens, t.Model.Merges, nil
 }

convert/tokenizer_spm.go

@@ -1,83 +0,0 @@
-package convert
-
-import (
-	"cmp"
-	"encoding/json"
-	"errors"
-	"fmt"
-	"os"
-	"path/filepath"
-	"slices"
-
-	"google.golang.org/protobuf/proto"
-
-	"github.com/ollama/ollama/convert/sentencepiece"
-)
-
-func parseSentencePiece(d string) (*Vocabulary, error) {
-	bts, err := os.ReadFile(filepath.Join(d, "tokenizer.model"))
-	if err != nil {
-		return nil, err
-	}
-
-	var spm sentencepiece.ModelProto
-	if err := proto.Unmarshal(bts, &spm); err != nil {
-		return nil, err
-	}
-
-	v := Vocabulary{Model: "llama"}
-	for _, piece := range spm.GetPieces() {
-		v.Tokens = append(v.Tokens, piece.GetPiece())
-		v.Scores = append(v.Scores, piece.GetScore())
-
-		switch t := piece.GetType(); t {
-		case sentencepiece.ModelProto_SentencePiece_UNKNOWN,
-			sentencepiece.ModelProto_SentencePiece_CONTROL,
-			sentencepiece.ModelProto_SentencePiece_UNUSED,
-			sentencepiece.ModelProto_SentencePiece_BYTE:
-			v.Types = append(v.Types, int32(t))
-		default:
-			v.Types = append(v.Types, int32(sentencepiece.ModelProto_SentencePiece_NORMAL))
-		}
-	}
-
-	f, err := os.Open(filepath.Join(d, "added_tokens.json"))
-	if errors.Is(err, os.ErrNotExist) {
-		return &v, nil
-	} else if err != nil {
-		return nil, err
-	}
-	defer f.Close()
-
-	var atm map[string]int
-	if err := json.NewDecoder(f).Decode(&atm); err != nil {
-		return nil, err
-	}
-
-	type t struct {
-		id      int
-		content string
-	}
-
-	var ts []t
-	for content, id := range atm {
-		ts = append(ts, t{id, content})
-	}
-
-	slices.SortFunc(ts, func(i, j t) int {
-		return cmp.Compare(i.id, j.id)
-	})
-
-	n := len(v.Tokens)
-	for i, t := range ts {
-		if t.id != i+n {
-			return nil, fmt.Errorf("invalid token id: %d", t.id)
-		}
-
-		v.Tokens = append(v.Tokens, t.content)
-		v.Scores = append(v.Scores, -1000.0)
-		v.Types = append(v.Types, tokenTypeUserDefined)
-	}
-
-	return &v, nil
-}

convert/torch.go

@@ -0,0 +1,287 @@
+package convert
+
+import (
+	"encoding/binary"
+	"encoding/json"
+	"fmt"
+	"io"
+	"log/slog"
+	"os"
+	"path/filepath"
+	"regexp"
+	"strings"
+
+	"github.com/nlpodyssey/gopickle/pytorch"
+	"github.com/nlpodyssey/gopickle/types"
+	"github.com/x448/float16"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type torchWriterTo struct {
+	t *llm.Tensor
+
+	params *Params
+	bo     ByteOrder
+
+	storage  pytorch.StorageInterface
+	repacker func(string, []float32, []uint64) ([]float32, error)
+}
+
+type TorchFormat struct{}
+
+func (tf *TorchFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
+	slog.Debug("getting torch tensors")
+
+	var files []string
+	if pt, _ := filepath.Glob(filepath.Join(dirpath, "consolidated*.pth")); len(pt) > 0 {
+		files = append(files, pt...)
+	} else if pt, _ := filepath.Glob(filepath.Join(dirpath, "pytorch_model*.pth")); len(pt) > 0 {
+		files = append(files, pt...)
+	}
+
+	var offset uint64
+	var tensors []llm.Tensor
+	for _, fn := range files {
+		m, err := pytorch.Load(fn)
+		if err != nil {
+			slog.Error(fmt.Sprintf("error unpickling: %q", err))
+			return []llm.Tensor{}, err
+		}
+
+		for _, k := range m.(*types.Dict).Keys() {
+			if strings.HasSuffix(k.(string), "self_attn.rotary_emb.inv_freq") {
+				continue
+			}
+
+			t, _ := m.(*types.Dict).Get(k)
+			tshape := t.(*pytorch.Tensor).Size
+
+			var size uint64
+			var kind uint32
+			switch len(tshape) {
+			case 0:
+				continue
+			case 1:
+				// convert to float32
+				kind = 0
+				size = uint64(tshape[0] * 4)
+			case 2:
+				// convert to float16
+				kind = 1
+				size = uint64(tshape[0] * tshape[1] * 2)
+			}
+
+			ggufName, err := tf.GetLayerName(k.(string))
+			if err != nil {
+				slog.Error(err.Error())
+				return nil, err
+			}
+			slog.Debug(fmt.Sprintf("'%35s': '%30s' %10d [%#v]", k.(string), ggufName, size, tshape))
+
+			shape := []uint64{0, 0, 0, 0}
+			for i := range tshape {
+				shape[i] = uint64(tshape[i])
+			}
+
+			tensor := llm.Tensor{
+				Name:   ggufName,
+				Kind:   kind,
+				Offset: offset, // calculate the offset
+				Shape:  shape,
+			}
+
+			tensor.WriterTo = torchWriterTo{
+				t:       &tensor,
+				params:  params,
+				bo:      params.ByteOrder,
+				storage: t.(*pytorch.Tensor).Source,
+			}
+
+			tensors = append(tensors, tensor)
+			offset += size
+		}
+	}
+
+	return tensors, nil
+}
+
+func getAltParams(dirpath string) (*Params, error) {
+	f, err := os.Open(filepath.Join(dirpath, "params.json"))
+	if err != nil {
+		slog.Error("no params.json")
+		return nil, err
+	}
+	defer f.Close()
+
+	type TorchParams struct {
+		HiddenSize     int     `json:"dim"`
+		AttentionHeads int     `json:"n_heads"`
+		KeyValHeads    int     `json:"n_kv_heads"`
+		HiddenLayers   int     `json:"n_layers"`
+		RopeTheta      float64 `json:"rope_theta"`
+		NormEPS        float64 `json:"norm_eps"`
+	}
+
+	var tparams TorchParams
+
+	d := json.NewDecoder(f)
+	err = d.Decode(&tparams)
+	if err != nil {
+		return nil, err
+	}
+
+	params := &Params{
+		Architectures:  []string{"LlamaForCausalLM"},
+		HiddenSize:     tparams.HiddenSize,
+		AttentionHeads: tparams.AttentionHeads,
+		KeyValHeads:    tparams.KeyValHeads,
+		HiddenLayers:   tparams.HiddenLayers,
+		NormEPS:        tparams.NormEPS,
+	}
+
+	switch {
+	case tparams.RopeTheta == 1000000:
+		// Codellama
+		params.ContextSize = 16384
+	case tparams.NormEPS == 1e-06:
+		// llama2
+		slog.Debug("Found llama2 - setting context size to 4096")
+		params.ContextSize = 4096
+	default:
+		params.ContextSize = 2048
+	}
+
+	params.ByteOrder = binary.LittleEndian
+	return params, nil
+}
+
+func (m *TorchFormat) GetParams(dirpath string) (*Params, error) {
+	f, err := os.Open(filepath.Join(dirpath, "config.json"))
+	if err != nil {
+		if os.IsNotExist(err) {
+			// try params.json instead
+			return getAltParams(dirpath)
+		} else {
+			return nil, err
+		}
+	}
+
+	var params Params
+	d := json.NewDecoder(f)
+	err = d.Decode(&params)
+	if err != nil {
+		return nil, err
+	}
+
+	params.ByteOrder = binary.LittleEndian
+	return &params, nil
+}
+
+func (m *TorchFormat) GetLayerName(n string) (string, error) {
+	directMap := map[string]string{
+		"tok_embeddings.weight":     "token_embd.weight",
+		"output.weight":             "output.weight",
+		"norm.weight":               "output_norm.weight",
+		"rope.freqs":                "rope_freqs.weight",
+		"model.embed_tokens.weight": "token_embd.weight",
+		"lm_head.weight":            "output.weight",
+		"model.norm.weight":         "output_norm.weight",
+	}
+
+	lMap := map[string]string{
+		"layers.(\\d+).attention_norm.weight":                 "blk.$1.attn_norm.weight",
+		"layers.(\\d+).attention_output_norm.weight":          "blk.$1.attn_norm.weight",
+		"layers.(\\d+).feed_forward.w2.weight":                "blk.$1.ffn_down.weight",
+		"layers.(\\d+).feed_forward.w1.weight":                "blk.$1.ffn_gate.weight",
+		"layers.(\\d+).feed_forward.w3.weight":                "blk.$1.ffn_up.weight",
+		"layers.(\\d+).ffn_norm.weight":                       "blk.$1.ffn_norm.weight",
+		"layers.(\\d+).attention.wk.weight":                   "blk.$1.attn_k.weight",
+		"layers.(\\d+).attention.wo.weight":                   "blk.$1.attn_output.weight",
+		"layers.(\\d+).attention.wq.weight":                   "blk.$1.attn_q.weight",
+		"layers.(\\d+).attention.wv.weight":                   "blk.$1.attn_v.weight",
+		"model.layers.(\\d+).input_layernorm.weight":          "blk.$1.attn_norm.weight",
+		"model.layers.(\\d+).mlp.down_proj.weight":            "blk.$1.ffn_down.weight",
+		"model.layers.(\\d+).mlp.gate_proj.weight":            "blk.$1.ffn_gate.weight",
+		"model.layers.(\\d+).mlp.up_proj.weight":              "blk.$1.ffn_up.weight",
+		"model.layers.(\\d+).post_attention_layernorm.weight": "blk.$1.ffn_norm.weight",
+		"model.layers.(\\d+).self_attn.k_proj.weight":         "blk.$1.attn_k.weight",
+		"model.layers.(\\d+).self_attn.o_proj.weight":         "blk.$1.attn_output.weight",
+		"model.layers.(\\d+).self_attn.q_proj.weight":         "blk.$1.attn_q.weight",
+		"model.layers.(\\d+).self_attn.v_proj.weight":         "blk.$1.attn_v.weight",
+	}
+
+	v, ok := directMap[n]
+	if ok {
+		return v, nil
+	}
+
+	// quick hack to rename the layers to gguf format
+	for k, v := range lMap {
+		re := regexp.MustCompile(k)
+		newName := re.ReplaceAllString(n, v)
+		if newName != n {
+			return newName, nil
+		}
+	}
+
+	return "", fmt.Errorf("couldn't find a layer name for '%s'", n)
+}
+
+func (r torchWriterTo) WriteTo(w io.Writer) (n int64, err error) {
+	var f32s []float32
+	switch s := r.storage.(type) {
+	case *pytorch.FloatStorage:
+		f32s = s.Data
+	case *pytorch.HalfStorage:
+		f32s = s.Data
+	case *pytorch.BFloat16Storage:
+		f32s = s.Data
+	default:
+		return 0, fmt.Errorf("unknown data type: %T", s)
+	}
+
+	if r.repacker != nil {
+		f32s, err = r.repacker(r.t.Name, f32s, r.t.Shape)
+		if err != nil {
+			return 0, err
+		}
+	}
+
+	switch r.t.Kind {
+	case 0:
+		return 0, binary.Write(w, r.bo, f32s)
+	case 1:
+		f16s := make([]uint16, len(f32s))
+		for i := range f32s {
+			f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
+		}
+
+		return 0, binary.Write(w, r.bo, f16s)
+	default:
+		return 0, fmt.Errorf("unknown storage type: %d", r.t.Kind)
+	}
+}
+
+func (m *TorchFormat) GetModelArch(name, dirPath string, params *Params) (ModelArch, error) {
+	switch len(params.Architectures) {
+	case 0:
+		return nil, fmt.Errorf("No architecture specified to convert")
+	case 1:
+		switch params.Architectures[0] {
+		case "LlamaForCausalLM":
+			return &LlamaModel{
+				ModelData{
+					Name:   name,
+					Path:   dirPath,
+					Params: params,
+					Format: m,
+				},
+			}, nil
+		default:
+			return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
+		}
+	}
+
+	return nil, fmt.Errorf("Unknown error")
+}

docs/faq.md

@@ -272,4 +272,4 @@ The following server settings may be used to adjust how Ollama handles concurren
 - `OLLAMA_NUM_PARALLEL` - The maximum number of parallel requests each model will process at the same time.  The default will auto-select either 4 or 1 based on available memory.
 - `OLLAMA_MAX_QUEUE` - The maximum number of requests Ollama will queue when busy before rejecting additional requests. The default is 512
 
-Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting.  Once ROCm v6 is available, Windows Radeon will follow the defaults above.  You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPUs VRAM.
+Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting.  Once ROCm v6.2 is available, Windows Radeon will follow the defaults above.  You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPUs VRAM.

format/format.go

@@ -9,9 +9,10 @@ const (
 	Thousand = 1000
 	Million  = Thousand * 1000
 	Billion  = Million * 1000
+	Trillion = Billion * 1000
 )
 
-func HumanNumber(b uint64) string {
+func RoundedParameter(b uint64) string {
 	switch {
 	case b >= Billion:
 		number := float64(b) / Billion
@@ -31,3 +32,33 @@ func HumanNumber(b uint64) string {
 		return fmt.Sprintf("%d", b)
 	}
 }
+
+func Parameters(b uint64) string {
+	switch {
+	case b >= Trillion:
+		number := float64(b) / Trillion
+		return fmt.Sprintf("%sT", decimalPlace(number))
+	case b >= Billion:
+		number := float64(b) / Billion
+		return fmt.Sprintf("%sB", decimalPlace(number))
+	case b >= Million:
+		number := float64(b) / Million
+		return fmt.Sprintf("%sM", decimalPlace(number))
+	case b >= Thousand:
+		number := float64(b) / Thousand
+		return fmt.Sprintf("%sK", decimalPlace(number))
+	default:
+		return fmt.Sprintf("%d", b)
+	}
+}
+
+func decimalPlace(number float64) string {
+	switch {
+	case number >= 100:
+		return fmt.Sprintf("%.0f", number)
+	case number >= 10:
+		return fmt.Sprintf("%.1f", number)
+	default:
+		return fmt.Sprintf("%.2f", number)
+	}
+}

format/format_test.go

@@ -4,7 +4,7 @@ import (
 	"testing"
 )
 
-func TestHumanNumber(t *testing.T) {
+func TestRoundedParameter(t *testing.T) {
 	type testCase struct {
 		input    uint64
 		expected string
@@ -24,7 +24,34 @@ func TestHumanNumber(t *testing.T) {
 
 	for _, tc := range testCases {
 		t.Run(tc.expected, func(t *testing.T) {
-			result := HumanNumber(tc.input)
+			result := RoundedParameter(tc.input)
+			if result != tc.expected {
+				t.Errorf("Expected %s, got %s", tc.expected, result)
+			}
+		})
+	}
+}
+
+func TestParameters(t *testing.T) {
+	type testCase struct {
+		input    uint64
+		expected string
+	}
+
+	testCases := []testCase{
+		{26000000, "26.0M"},
+		{26000000000, "26.0B"},
+		{1000, "1.00K"},
+		{1000000, "1.00M"},
+		{1000000000, "1.00B"},
+		{1000000000000, "1.00T"},
+		{100, "100"},
+		{206000000, "206M"},
+	}
+
+	for _, tc := range testCases {
+		t.Run(tc.expected, func(t *testing.T) {
+			result := Parameters(tc.input)
 			if result != tc.expected {
 				t.Errorf("Expected %s, got %s", tc.expected, result)
 			}

go.mod

@@ -18,10 +18,10 @@ require (
 require (
 	github.com/agnivade/levenshtein v1.1.1
 	github.com/d4l3k/go-bfloat16 v0.0.0-20211005043715-690c3bdd05f1
+	github.com/google/go-cmp v0.6.0
 	github.com/mattn/go-runewidth v0.0.14
 	github.com/nlpodyssey/gopickle v0.3.0
 	github.com/pdevine/tensor v0.0.0-20240510204454-f88f4562727c
-	github.com/sbinet/npyio v0.9.0
 )
 
 require (

go.sum

@@ -171,8 +171,6 @@ github.com/rogpeppe/go-internal v1.8.0 h1:FCbCCtXNOY3UtUuHUYaghJg4y7Fd14rXifAYUA
 github.com/rogpeppe/go-internal v1.8.0/go.mod h1:WmiCO8CzOY8rg0OYDC4/i/2WRWAB6poM+XZ2dLUbcbE=
 github.com/russross/blackfriday/v2 v2.1.0/go.mod h1:+Rmxgy9KzJVeS9/2gXHxylqXiyQDYRxCVz55jmeOWTM=
 github.com/ruudk/golang-pdf417 v0.0.0-20181029194003-1af4ab5afa58/go.mod h1:6lfFZQK844Gfx8o5WFuvpxWRwnSoipWe/p622j1v06w=
-github.com/sbinet/npyio v0.9.0 h1:A7h8OyYsOsc+NPRtynRMSf70xSgATZNpamNp8nQ8Tjc=
-github.com/sbinet/npyio v0.9.0/go.mod h1:vgjQEMRTS9aMS9GdXhr+5jounCmGqjDO2JI+IpSokns=
 github.com/spf13/cobra v1.7.0 h1:hyqWnYt1ZQShIddO5kBpj3vu05/++x6tJ6dg8EC572I=
 github.com/spf13/cobra v1.7.0/go.mod h1:uLxZILRyS/50WlhOIKD7W6V5bgeIt+4sICxh6uRMrb0=
 github.com/spf13/pflag v1.0.5 h1:iy+VFUOCP1a+8yFto/drg2CJ5u0yRoB7fZw3DKv/JXA=

gpu/amd_common.go

@@ -49,9 +49,17 @@ func rocmGetVisibleDevicesEnv(gpuInfo []GpuInfo) (string, string) {
 }
 
 func commonAMDValidateLibDir() (string, error) {
-	// We try to favor system paths first, so that we can wire up the subprocess to use
-	// the system version.  Only use our bundled version if the system version doesn't work
-	// This gives users a more recovery options if versions have subtle problems at runtime
+	// Favor our bundled version
+
+	// Installer payload location if we're running the installed binary
+	exe, err := os.Executable()
+	if err == nil {
+		rocmTargetDir := filepath.Join(filepath.Dir(exe), "rocm")
+		if rocmLibUsable(rocmTargetDir) {
+			slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
+			return rocmTargetDir, nil
+		}
+	}
 
 	// Prefer explicit HIP env var
 	hipPath := os.Getenv("HIP_PATH")
@@ -87,14 +95,5 @@ func commonAMDValidateLibDir() (string, error) {
 		}
 	}
 
-	// Installer payload location if we're running the installed binary
-	exe, err := os.Executable()
-	if err == nil {
-		rocmTargetDir := filepath.Join(filepath.Dir(exe), "rocm")
-		if rocmLibUsable(rocmTargetDir) {
-			slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
-			return rocmTargetDir, nil
-		}
-	}
 	return "", fmt.Errorf("no suitable rocm found, falling back to CPU")
 }

gpu/amd_hip_windows.go

@@ -84,9 +84,8 @@ func (hl *HipLib) AMDDriverVersion() (driverMajor, driverMinor int, err error) {
 	}
 
 	slog.Debug("hipDriverGetVersion", "version", version)
-	// TODO - this isn't actually right, but the docs claim hipDriverGetVersion isn't accurate anyway...
-	driverMajor = version / 1000
-	driverMinor = (version - (driverMajor * 1000)) / 10
+	driverMajor = version / 10000000
+	driverMinor = (version - (driverMajor * 10000000)) / 100000
 
 	return driverMajor, driverMinor, nil
 }

gpu/amd_windows.go

@@ -22,8 +22,8 @@ const (
 
 var (
 	// Used to validate if the given ROCm lib is usable
-	ROCmLibGlobs          = []string{"hipblas.dll", "rocblas"}                 // TODO - probably include more coverage of files here...
-	RocmStandardLocations = []string{"C:\\Program Files\\AMD\\ROCm\\5.7\\bin"} // TODO glob?
+	ROCmLibGlobs          = []string{"hipblas.dll", "rocblas"}                 // This is not sufficient to discern v5 vs v6
+	RocmStandardLocations = []string{"C:\\Program Files\\AMD\\ROCm\\6.1\\bin"} // TODO glob?
 )
 
 func AMDGetGPUInfo() []RocmGPUInfo {
@@ -35,12 +35,11 @@ func AMDGetGPUInfo() []RocmGPUInfo {
 	}
 	defer hl.Release()
 
-	// TODO - this reports incorrect version information, so omitting for now
-	// driverMajor, driverMinor, err := hl.AMDDriverVersion()
-	// if err != nil {
-	// 	// For now this is benign, but we may eventually need to fail compatibility checks
-	// 	slog.Debug("error looking up amd driver version", "error", err)
-	// }
+	driverMajor, driverMinor, err := hl.AMDDriverVersion()
+	if err != nil {
+		// For now this is benign, but we may eventually need to fail compatibility checks
+		slog.Debug("error looking up amd driver version", "error", err)
+	}
 
 	// Note: the HIP library automatically handles subsetting to any HIP_VISIBLE_DEVICES the user specified
 	count := hl.HipGetDeviceCount()
@@ -132,10 +131,8 @@ func AMDGetGPUInfo() []RocmGPUInfo {
 				MinimumMemory:  rocmMinimumMemory,
 				Name:           name,
 				Compute:        gfx,
-
-				// TODO - this information isn't accurate on windows, so don't report it until we find the right way to retrieve
-				// DriverMajor:    driverMajor,
-				// DriverMinor:    driverMinor,
+				DriverMajor:    driverMajor,
+				DriverMinor:    driverMinor,
 			},
 			index: i,
 		}

gpu/gpu.go

@@ -274,6 +274,28 @@ func GetGPUInfo() GpuInfoList {
 				gpuInfo.DriverMajor = driverMajor
 				gpuInfo.DriverMinor = driverMinor
 
+				// query the management library as well so we can record any skew between the two
+				// which represents overhead on the GPU we must set aside on subsequent updates
+				if cHandles.nvml != nil {
+					C.nvml_get_free(*cHandles.nvml, C.int(gpuInfo.index), &memInfo.free, &memInfo.total, &memInfo.used)
+					if memInfo.err != nil {
+						slog.Warn("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
+						C.free(unsafe.Pointer(memInfo.err))
+					} else {
+						if memInfo.free != 0 && uint64(memInfo.free) > gpuInfo.FreeMemory {
+							gpuInfo.OSOverhead = uint64(memInfo.free) - gpuInfo.FreeMemory
+							slog.Info("detected OS VRAM overhead",
+								"id", gpuInfo.ID,
+								"library", gpuInfo.Library,
+								"compute", gpuInfo.Compute,
+								"driver", fmt.Sprintf("%d.%d", gpuInfo.DriverMajor, gpuInfo.DriverMinor),
+								"name", gpuInfo.Name,
+								"overhead", format.HumanBytes2(gpuInfo.OSOverhead),
+							)
+						}
+					}
+				}
+
 				// TODO potentially sort on our own algorithm instead of what the underlying GPU library does...
 				cudaGPUs = append(cudaGPUs, gpuInfo)
 			}
@@ -338,14 +360,17 @@ func GetGPUInfo() GpuInfoList {
 					"before",
 					"total", format.HumanBytes2(cpus[0].TotalMemory),
 					"free", format.HumanBytes2(cpus[0].FreeMemory),
+					"free_swap", format.HumanBytes2(cpus[0].FreeSwap),
 				),
 				slog.Group(
 					"now",
 					"total", format.HumanBytes2(mem.TotalMemory),
 					"free", format.HumanBytes2(mem.FreeMemory),
+					"free_swap", format.HumanBytes2(mem.FreeSwap),
 				),
 			)
 			cpus[0].FreeMemory = mem.FreeMemory
+			cpus[0].FreeSwap = mem.FreeSwap
 		}
 
 		var memInfo C.mem_info_t
@@ -374,9 +399,14 @@ func GetGPUInfo() GpuInfoList {
 				slog.Warn("error looking up nvidia GPU memory")
 				continue
 			}
+			if cHandles.nvml != nil && gpu.OSOverhead > 0 {
+				// When using the management library update based on recorded overhead
+				memInfo.free -= C.uint64_t(gpu.OSOverhead)
+			}
 			slog.Debug("updating cuda memory data",
 				"gpu", gpu.ID,
 				"name", gpu.Name,
+				"overhead", format.HumanBytes2(gpu.OSOverhead),
 				slog.Group(
 					"before",
 					"total", format.HumanBytes2(gpu.TotalMemory),

gpu/gpu_darwin.go

@@ -57,6 +57,7 @@ func GetCPUMem() (memInfo, error) {
 	return memInfo{
 		TotalMemory: uint64(C.getPhysicalMemory()),
 		FreeMemory:  uint64(C.getFreeMemory()),
+		// FreeSwap omitted as Darwin uses dynamic paging
 	}, nil
 }
 

gpu/gpu_linux.go

@@ -50,7 +50,7 @@ var OneapiMgmtName = "libze_intel_gpu.so"
 
 func GetCPUMem() (memInfo, error) {
 	var mem memInfo
-	var total, available, free, buffers, cached uint64
+	var total, available, free, buffers, cached, freeSwap uint64
 	f, err := os.Open("/proc/meminfo")
 	if err != nil {
 		return mem, err
@@ -70,20 +70,21 @@ func GetCPUMem() (memInfo, error) {
 			_, err = fmt.Sscanf(line, "Buffers:%d", &buffers)
 		case strings.HasPrefix(line, "Cached:"):
 			_, err = fmt.Sscanf(line, "Cached:%d", &cached)
+		case strings.HasPrefix(line, "SwapFree:"):
+			_, err = fmt.Sscanf(line, "SwapFree:%d", &freeSwap)
 		default:
 			continue
 		}
 		if err != nil {
 			return mem, err
 		}
-
-		if total > 0 && available > 0 {
-			mem.TotalMemory = total * format.KibiByte
-			mem.FreeMemory = available * format.KibiByte
-			return mem, nil
-		}
 	}
 	mem.TotalMemory = total * format.KibiByte
-	mem.FreeMemory = (free + buffers + cached) * format.KibiByte
+	mem.FreeSwap = freeSwap * format.KibiByte
+	if available > 0 {
+		mem.FreeMemory = available * format.KibiByte
+	} else {
+		mem.FreeMemory = (free + buffers + cached) * format.KibiByte
+	}
 	return mem, nil
 }

gpu/gpu_windows.go

@@ -51,5 +51,5 @@ func GetCPUMem() (memInfo, error) {
 	if r1 == 0 {
 		return memInfo{}, fmt.Errorf("GlobalMemoryStatusEx failed: %w", err)
 	}
-	return memInfo{TotalMemory: memStatus.TotalPhys, FreeMemory: memStatus.AvailPhys}, nil
+	return memInfo{TotalMemory: memStatus.TotalPhys, FreeMemory: memStatus.AvailPhys, FreeSwap: memStatus.AvailPageFile}, nil
 }

gpu/types.go

@@ -10,6 +10,7 @@ import (
 type memInfo struct {
 	TotalMemory uint64 `json:"total_memory,omitempty"`
 	FreeMemory  uint64 `json:"free_memory,omitempty"`
+	FreeSwap    uint64 `json:"free_swap,omitempty"`
 }
 
 // Beginning of an `ollama info` command
@@ -52,7 +53,8 @@ type CPUInfo struct {
 
 type CudaGPUInfo struct {
 	GpuInfo
-	index int //nolint:unused,nolintlint
+	OSOverhead uint64 // Memory overhead between the driver library and management library
+	index      int    //nolint:unused,nolintlint
 }
 type CudaGPUInfoList []CudaGPUInfo
 

llm/generate/gen_linux.sh

@@ -178,7 +178,7 @@ if [ -z "${OLLAMA_SKIP_CUDA_GENERATE}" -a -d "${CUDA_LIB_DIR}" ]; then
         CMAKE_CUDA_DEFS="-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES} ${OLLAMA_CUSTOM_CUDA_DEFS}"
         echo "Building custom CUDA GPU"
     else
-        CMAKE_CUDA_DEFS="-DGGML_CUDA=on -DCMAKE_CUDA_FLAGS=-t8 -DGGML_CUDA_FORCE_MMQ=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES} -DCMAKE_LIBRARY_PATH=/usr/local/cuda/compat"
+        CMAKE_CUDA_DEFS="-DGGML_CUDA=on -DCMAKE_CUDA_FLAGS=-t8 -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES}"
     fi
     CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} ${ARM64_DEFS} ${CMAKE_CUDA_DEFS}"
     BUILD_DIR="../build/linux/${ARCH}/cuda${CUDA_VARIANT}"
@@ -254,7 +254,7 @@ if [ -z "${OLLAMA_SKIP_ROCM_GENERATE}" -a -d "${ROCM_PATH}" ]; then
         ROCM_VARIANT=_v$(ls ${ROCM_PATH}/lib/librocblas.so.*.*.????? | cut -f5 -d. || true)
     fi
     init_vars
-    CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} -DGGML_HIPBLAS=on -DCMAKE_C_COMPILER=$ROCM_PATH/llvm/bin/clang -DCMAKE_CXX_COMPILER=$ROCM_PATH/llvm/bin/clang++ -DAMDGPU_TARGETS=$(amdGPUs) -DGPU_TARGETS=$(amdGPUs)"
+    CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} -DGGML_HIPBLAS=on -DLLAMA_CUDA_NO_PEER_COPY=on -DCMAKE_C_COMPILER=$ROCM_PATH/llvm/bin/clang -DCMAKE_CXX_COMPILER=$ROCM_PATH/llvm/bin/clang++ -DAMDGPU_TARGETS=$(amdGPUs) -DGPU_TARGETS=$(amdGPUs)"
     # Users building from source can tune the exact flags we pass to cmake for configuring llama.cpp
     if [ -n "${OLLAMA_CUSTOM_ROCM_DEFS}" ]; then
         echo "OLLAMA_CUSTOM_ROCM_DEFS=\"${OLLAMA_CUSTOM_ROCM_DEFS}\""

llm/generate/gen_windows.ps1

@@ -6,18 +6,9 @@ function amdGPUs {
     if ($env:AMDGPU_TARGETS) {
         return $env:AMDGPU_TARGETS
     }
-    # TODO - load from some common data file for linux + windows build consistency
+    # Current supported rocblas list from ROCm v6.1.2 on windows
     $GPU_LIST = @(
-        "gfx900"
         "gfx906:xnack-"
-        "gfx908:xnack-"
-        "gfx90a:xnack+"
-        "gfx90a:xnack-"
-        "gfx940"
-        "gfx941"
-        "gfx942"
-        "gfx1010"
-        "gfx1012"
         "gfx1030"
         "gfx1100"
         "gfx1101"
@@ -366,6 +357,7 @@ function build_rocm() {
             "-DCMAKE_C_COMPILER=clang.exe",
             "-DCMAKE_CXX_COMPILER=clang++.exe",
             "-DGGML_HIPBLAS=on",
+            "-DLLAMA_CUDA_NO_PEER_COPY=on",
             "-DHIP_PLATFORM=amd",
             "-DGGML_AVX=on",
             "-DGGML_AVX2=off",
@@ -394,7 +386,6 @@ function build_rocm() {
         sign
         install
 
-        # Assumes v5.7, may need adjustments for v6
         rm -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\"
         md "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\rocblas\library\" -ea 0 > $null
         cp "${env:HIP_PATH}\bin\hipblas.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\"

llm/ggla.go

@@ -1,12 +1,9 @@
 package llm
 
 import (
-	"bytes"
 	"encoding/binary"
 	"errors"
-	"fmt"
 	"io"
-	"log/slog"
 	"slices"
 )
 
@@ -19,7 +16,6 @@ func (c *containerGGLA) Name() string {
 }
 
 func (c *containerGGLA) Decode(rs io.ReadSeeker) (model, error) {
-	slog.Info("decoding ggla")
 	if err := binary.Read(rs, binary.LittleEndian, &c.version); err != nil {
 		return nil, err
 	}
@@ -40,8 +36,6 @@ type ggla struct {
 
 	kv      KV
 	tensors []*Tensor
-
-	tensorOffset uint64
 }
 
 func newGGLA(container *containerGGLA) *ggla {
@@ -56,13 +50,10 @@ func (llm *ggla) KV() KV {
 }
 
 func (llm *ggla) Tensors() Tensors {
-	return Tensors{
-		Items:  llm.tensors,
-		Offset: llm.tensorOffset,
-	}
+	return llm.tensors
 }
 
-func (llm *ggla) decode(rs io.ReadSeeker) error {
+func (llm *ggla) decode(rs io.ReadSeeker) (retErr error) {
 	var r uint32
 	if err := binary.Read(rs, binary.LittleEndian, &r); err != nil {
 		return err
@@ -75,22 +66,21 @@ func (llm *ggla) decode(rs io.ReadSeeker) error {
 	}
 	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) {
-				break
+				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
@@ -121,14 +111,13 @@ func (llm *ggla) decode(rs io.ReadSeeker) error {
 		}
 
 		t.Name = string(name)
-		slog.Info(fmt.Sprintf("%s: [%d, %d] k=%d", t.Name, t.Shape[0], t.Shape[1], t.Kind))
 
 		offset, err := rs.Seek(0, io.SeekCurrent)
 		if err != nil {
 			return err
 		}
 
-		if _, err := rs.Seek((offset+31)&-32, io.SeekStart); err != nil {
+		if _, err := rs.Seek((offset+31)&-32-offset, io.SeekCurrent); err != nil {
 			return err
 		}
 
@@ -145,87 +134,4 @@ func (llm *ggla) decode(rs io.ReadSeeker) error {
 
 		llm.tensors = append(llm.tensors, &t)
 	}
-	return nil
-}
-
-func WriteGGLA(ws io.WriteSeeker, kv KV, ts []*Tensor) error {
-	slog.Debug("writing ggla")
-	if err := binary.Write(ws, binary.LittleEndian, []byte("algg")); err != nil {
-		return err
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, uint32(1)); err != nil {
-		return err
-	}
-
-	var r uint32
-	var alpha uint32
-	var ok bool
-
-	if r, ok = kv["r"].(uint32); !ok {
-		r = 8
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, r); err != nil {
-		return err
-	}
-
-	if alpha, ok = kv["alpha"].(uint32); !ok {
-		alpha = 16
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, alpha); err != nil {
-		return err
-	}
-
-	for _, t := range ts {
-		dims := 0
-		for cnt := range len(t.Shape) {
-			if t.Shape[cnt] > 0 {
-				dims++
-			}
-		}
-
-		if err := binary.Write(ws, binary.LittleEndian, uint32(dims)); err != nil {
-			return err
-		}
-
-		if err := binary.Write(ws, binary.LittleEndian, uint32(len(t.Name))); err != nil {
-			return err
-		}
-
-		if err := binary.Write(ws, binary.LittleEndian, t.Kind); err != nil {
-			return err
-		}
-
-		for cnt := range dims {
-			if err := binary.Write(ws, binary.LittleEndian, uint32(t.Shape[dims-1-cnt])); err != nil {
-				return err
-			}
-		}
-
-		if err := binary.Write(ws, binary.LittleEndian, []byte(t.Name)); err != nil {
-			return err
-		}
-
-		offset, err := ws.Seek(0, io.SeekCurrent)
-		if err != nil {
-			return err
-		}
-
-		var alignment int32 = 32
-		pad := gglaPadding(int32(offset), alignment)
-		if err := binary.Write(ws, binary.LittleEndian, bytes.Repeat([]byte{0}, int(pad))); err != nil {
-			return err
-		}
-
-		if _, err := t.WriteTo(ws); err != nil {
-			return err
-		}
-	}
-	return nil
-}
-
-func gglaPadding(offset, align int32) int32 {
-	return (align - offset%align) % align
 }

llm/ggml.go

@@ -112,14 +112,11 @@ func (kv KV) ChatTemplate() string {
 	return s
 }
 
-type Tensors struct {
-	Items  []*Tensor
-	Offset uint64
-}
+type Tensors []*Tensor
 
 func (ts Tensors) Layers() map[string]Layer {
 	layers := make(map[string]Layer)
-	for _, t := range ts.Items {
+	for _, t := range ts {
 		parts := strings.Split(t.Name, ".")
 		if parts[0] == "blk" {
 			// join first and second part, e.g. blk.%d
@@ -427,6 +424,32 @@ func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload ui
 			4*batch*(3*embedding+vocab)+embedding*vocab*105/128,
 			4*batch*(2*embedding+1+2*embeddingHeadsK*headsKV+context+context*headsKV)+4*embeddingHeadsK*context*headsKV+embedding*embeddingHeadsK*headsKV*9/16,
 		)
+	case "chatglm":
+		fullOffload = 4 * batch * (embedding + vocab)
+		partialOffload = 4*batch*(embedding+vocab) + embedding*vocab*105/128
+		if qkvBias, ok := layers["blk.0"]["attn_qkv.bias"]; ok {
+			fullOffload = max(
+				fullOffload,
+				4*batch*(2+
+					2*embedding+
+					context+
+					context*heads+
+					embeddingHeadsK*heads+
+					qkvBias.Shape[0]),
+			)
+
+			partialOffload = max(
+				partialOffload,
+				4*batch*(1+
+					2*embedding+
+					embeddingHeadsK*heads+
+					context+
+					context*heads)+
+					4*embeddingHeadsK*context+
+					4*context*embeddingHeadsK+
+					4*qkvBias.Shape[0],
+			)
+		}
 	}
 
 	return

llm/gguf.go

@@ -2,16 +2,11 @@ package llm
 
 import (
 	"bytes"
-	"cmp"
 	"encoding/binary"
 	"encoding/json"
 	"fmt"
 	"io"
-	"log/slog"
-	"slices"
 	"strings"
-
-	"golang.org/x/exp/maps"
 )
 
 type containerGGUF struct {
@@ -94,7 +89,6 @@ type gguf struct {
 	tensors []*Tensor
 
 	parameters uint64
-	tensorOffset uint64
 
 	scratch [16 << 10]byte
 }
@@ -106,15 +100,16 @@ func newGGUF(container *containerGGUF) *gguf {
 	}
 }
 
+func NewGGUFV3(bo binary.ByteOrder) *gguf {
+	return newGGUF(&containerGGUF{ByteOrder: bo, Version: 3})
+}
+
 func (llm *gguf) KV() KV {
 	return llm.kv
 }
 
 func (llm *gguf) Tensors() Tensors {
-	return Tensors{
-		Items:  llm.tensors,
-		Offset: llm.tensorOffset,
-	}
+	return llm.tensors
 }
 
 func (llm *gguf) numTensor() uint64 {
@@ -204,7 +199,7 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 			return fmt.Errorf("failed to read tensor dimensions: %w", err)
 		}
 
-		shape := make([]uint64, dims)
+		shape := [4]uint64{1, 1, 1, 1}
 		for i := 0; uint32(i) < dims; i++ {
 			shape[i], err = readGGUF[uint64](llm, rs)
 			if err != nil {
@@ -241,21 +236,13 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 		alignment = 32
 	}
 
-	offset, err := rs.Seek(0, io.SeekCurrent)
-	if err != nil {
-		return err
-	}
-
-	padding := ggufPadding(offset, int64(alignment))
-	llm.tensorOffset = uint64(offset + padding)
-
 	for _, tensor := range llm.tensors {
 		offset, err := rs.Seek(0, io.SeekCurrent)
 		if err != nil {
 			return fmt.Errorf("failed to get current offset: %w", err)
 		}
 
-		padding := ggufPadding(offset, int64(alignment))
+		padding := llm.padding(offset, int64(alignment))
 		if _, err := rs.Seek(padding, io.SeekCurrent); err != nil {
 			return fmt.Errorf("failed to seek to init padding: %w", err)
 		}
@@ -274,12 +261,12 @@ func readGGUF[T any](llm *gguf, r io.Reader) (T, error) {
 	return t, err
 }
 
-func writeGGUF[V any](w io.Writer, t uint32, v V) error {
-	if err := binary.Write(w, binary.LittleEndian, t); err != nil {
+func writeGGUF[V any](llm *gguf, w io.Writer, t uint32, v V) error {
+	if err := binary.Write(w, llm.ByteOrder, t); err != nil {
 		return err
 	}
 
-	return binary.Write(w, binary.LittleEndian, v)
+	return binary.Write(w, llm.ByteOrder, v)
 }
 
 func readGGUFV1String(llm *gguf, r io.Reader) (string, error) {
@@ -343,12 +330,12 @@ func readGGUFString(llm *gguf, r io.Reader) (string, error) {
 	return string(buf), nil
 }
 
-func writeGGUFString(w io.Writer, s string) error {
-	if err := binary.Write(w, binary.LittleEndian, ggufTypeString); err != nil {
+func writeGGUFString(llm *gguf, w io.Writer, s string) error {
+	if err := binary.Write(w, llm.ByteOrder, ggufTypeString); err != nil {
 		return err
 	}
 
-	if err := binary.Write(w, binary.LittleEndian, uint64(len(s))); err != nil {
+	if err := binary.Write(w, llm.ByteOrder, uint64(len(s))); err != nil {
 		return err
 	}
 
@@ -489,21 +476,21 @@ func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
 	return a, nil
 }
 
-func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
-	if err := binary.Write(w, binary.LittleEndian, ggufTypeArray); err != nil {
+func writeGGUFArray[S ~[]E, E any](llm *gguf, w io.Writer, t uint32, s S) error {
+	if err := binary.Write(w, llm.ByteOrder, ggufTypeArray); err != nil {
 		return err
 	}
 
-	if err := binary.Write(w, binary.LittleEndian, t); err != nil {
+	if err := binary.Write(w, llm.ByteOrder, t); err != nil {
 		return err
 	}
 
-	if err := binary.Write(w, binary.LittleEndian, uint64(len(s))); err != nil {
+	if err := binary.Write(w, llm.ByteOrder, uint64(len(s))); err != nil {
 		return err
 	}
 
 	for _, e := range s {
-		if err := binary.Write(w, binary.LittleEndian, e); err != nil {
+		if err := binary.Write(w, llm.ByteOrder, e); err != nil {
 			return err
 		}
 	}
@@ -511,55 +498,193 @@ func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
 	return nil
 }
 
-func WriteGGUF(ws io.WriteSeeker, kv KV, ts []*Tensor) error {
-	if err := binary.Write(ws, binary.LittleEndian, []byte("GGUF")); err != nil {
+var ggufKVOrder = map[string][]string{
+	"llama": {
+		"general.architecture",
+		"general.name",
+		"llama.vocab_size",
+		"llama.context_length",
+		"llama.embedding_length",
+		"llama.block_count",
+		"llama.feed_forward_length",
+		"llama.attention.head_count",
+		"llama.attention.head_count_kv",
+		"llama.attention.layer_norm_rms_epsilon",
+		"llama.rope.freq_base",
+		"llama.rope.dimension_count",
+		"llama.expert_count",
+		"llama.expert_used_count",
+		"gemma.context_length",
+		"gemma.embedding_length",
+		"gemma.block_count",
+		"gemma.feed_forward_length",
+		"gemma.attention.head_count",
+		"gemma.attention.head_count_kv",
+		"gemma.attention.layer_norm_rms_epsilon",
+		"gemma.attention.key_length",
+		"gemma.attention.value_length",
+		"general.file_type",
+		"tokenizer.ggml.pre",
+		"tokenizer.ggml.model",
+		"tokenizer.ggml.tokens",
+		"tokenizer.ggml.scores",
+		"tokenizer.ggml.merges",
+		"tokenizer.ggml.token_type",
+		"tokenizer.ggml.bos_token_id",
+		"tokenizer.ggml.eos_token_id",
+		"tokenizer.ggml.unknown_token_id",
+		"tokenizer.ggml.padding_token_id",
+		"tokenizer.ggml.add_bos_token",
+		"tokenizer.ggml.add_eos_token",
+		"tokenizer.chat_template",
+	},
+}
+
+func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
+	switch llm.Version {
+	case 3:
+		llm.V3.NumTensor = uint64(len(tensors))
+		llm.V3.NumKV = uint64(len(kv))
+	default:
+		return fmt.Errorf("not implemented: ggufv%d", llm.Version)
+	}
+
+	if err := binary.Write(ws, llm.ByteOrder, []byte("GGUF")); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, binary.LittleEndian, uint32(3)); err != nil {
+	if err := binary.Write(ws, llm.ByteOrder, llm.Version); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(ts))); err != nil {
+	if err := binary.Write(ws, llm.ByteOrder, llm.numTensor()); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(kv))); err != nil {
+	if err := binary.Write(ws, llm.ByteOrder, llm.numKV()); err != nil {
 		return err
 	}
 
-	keys := maps.Keys(kv)
-	slices.Sort(keys)
+	kvCheck := make(map[string]bool)
+	for k := range kv {
+		kvCheck[k] = false
+	}
 
-	for _, key := range keys {
-		if err := ggufWriteKV(ws, key, kv[key]); err != nil {
+	for _, k := range ggufKVOrder["llama"] {
+		v, ok := kv[k]
+		if !ok {
+			continue
+		}
+		kvCheck[k] = true
+
+		if err := binary.Write(ws, llm.ByteOrder, uint64(len(k))); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, llm.ByteOrder, []byte(k)); err != nil {
+			return err
+		}
+
+		var err error
+		switch v := v.(type) {
+		case uint32:
+			err = writeGGUF(llm, ws, ggufTypeUint32, v)
+		case float32:
+			err = writeGGUF(llm, ws, ggufTypeFloat32, v)
+		case bool:
+			err = writeGGUF(llm, ws, ggufTypeBool, v)
+		case string:
+			err = writeGGUFString(llm, ws, v)
+		case []int32:
+			err = writeGGUFArray(llm, ws, ggufTypeInt32, v)
+		case []uint32:
+			err = writeGGUFArray(llm, ws, ggufTypeUint32, v)
+		case []float32:
+			err = writeGGUFArray(llm, ws, ggufTypeFloat32, v)
+		case []string:
+			if err := binary.Write(ws, llm.ByteOrder, ggufTypeArray); err != nil {
+				return err
+			}
+
+			if err := binary.Write(ws, llm.ByteOrder, ggufTypeString); err != nil {
+				return err
+			}
+
+			if err := binary.Write(ws, llm.ByteOrder, uint64(len(v))); err != nil {
+				return err
+			}
+
+			for _, e := range v {
+				if err := binary.Write(ws, llm.ByteOrder, uint64(len(e))); err != nil {
+					return err
+				}
+
+				if err := binary.Write(ws, llm.ByteOrder, []byte(e)); err != nil {
+					return err
+				}
+			}
+		default:
+			return fmt.Errorf("improper type for '%s'", k)
+		}
+		if err != nil {
 			return err
 		}
 	}
 
-	slices.SortFunc(ts, func(a, b *Tensor) int {
-		var i, j int
-		if n, err := fmt.Sscanf(a.Name, "blk.%d", &i); err != nil || n != 1 {
-			return cmp.Compare(a.Name, b.Name)
-		} else if n, err := fmt.Sscanf(b.Name, "blk.%d", &j); err != nil || n != 1 {
-			return cmp.Compare(a.Name, b.Name)
+	for k, v := range kvCheck {
+		if !v {
+			return fmt.Errorf("Didn't know how to write kv %s", k)
 		}
+	}
 
-		return cmp.Compare(i, j)
-	})
-
-	var s uint64
-	for _, t := range ts {
-		t.Offset = s
-		if err := ggufWriteTensorInfo(ws, t); err != nil {
+	for _, tensor := range tensors {
+		if err := binary.Write(ws, llm.ByteOrder, uint64(len(tensor.Name))); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, llm.ByteOrder, []byte(tensor.Name)); err != nil {
+			return err
+		}
+
+		var dims int
+		for cnt := range len(tensor.Shape) {
+			if tensor.Shape[cnt] > 0 {
+				dims++
+			}
+		}
+
+		if err := binary.Write(ws, llm.ByteOrder, uint32(dims)); err != nil {
+			return err
+		}
+
+		for i := range dims {
+			if err := binary.Write(ws, llm.ByteOrder, tensor.Shape[dims-1-i]); err != nil {
+				return err
+			}
+		}
+
+		if err := binary.Write(ws, llm.ByteOrder, tensor.Kind); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, llm.ByteOrder, tensor.Offset); err != nil {
 			return err
 		}
-		s += t.Size()
 	}
 
 	var alignment int64 = 32
-	for _, t := range ts {
-		if err := ggufWriteTensor(ws, t, alignment); err != nil {
+	for _, tensor := range tensors {
+		offset, err := ws.Seek(0, io.SeekCurrent)
+		if err != nil {
+			return err
+		}
+
+		padding := llm.padding(offset, alignment)
+		if err := binary.Write(ws, llm.ByteOrder, bytes.Repeat([]byte{0}, int(padding))); err != nil {
+			return err
+		}
+
+		if _, err := tensor.WriteTo(ws); err != nil {
 			return err
 		}
 	}
@@ -567,103 +692,6 @@ func WriteGGUF(ws io.WriteSeeker, kv KV, ts []*Tensor) error {
 	return nil
 }
 
-func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
-	slog.Debug(k, "type", fmt.Sprintf("%T", v))
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(k))); err != nil {
-		return err
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, []byte(k)); err != nil {
-		return err
-	}
-
-	var err error
-	switch v := v.(type) {
-	case uint32:
-		err = writeGGUF(ws, ggufTypeUint32, v)
-	case float32:
-		err = writeGGUF(ws, ggufTypeFloat32, v)
-	case bool:
-		err = writeGGUF(ws, ggufTypeBool, v)
-	case string:
-		err = writeGGUFString(ws, v)
-	case []int32:
-		err = writeGGUFArray(ws, ggufTypeInt32, v)
-	case []uint32:
-		err = writeGGUFArray(ws, ggufTypeUint32, v)
-	case []float32:
-		err = writeGGUFArray(ws, ggufTypeFloat32, v)
-	case []string:
-		if err := binary.Write(ws, binary.LittleEndian, ggufTypeArray); err != nil {
-			return err
-		}
-
-		if err := binary.Write(ws, binary.LittleEndian, ggufTypeString); err != nil {
-			return err
-		}
-
-		if err := binary.Write(ws, binary.LittleEndian, uint64(len(v))); err != nil {
-			return err
-		}
-
-		for _, e := range v {
-			if err := binary.Write(ws, binary.LittleEndian, uint64(len(e))); err != nil {
-				return err
-			}
-
-			if err := binary.Write(ws, binary.LittleEndian, []byte(e)); err != nil {
-				return err
-			}
-		}
-	default:
-		return fmt.Errorf("improper type for '%s'", k)
-	}
-
-	return err
-}
-
-func ggufWriteTensorInfo(ws io.WriteSeeker, t *Tensor) error {
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(t.Name))); err != nil {
-		return err
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, []byte(t.Name)); err != nil {
-		return err
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, uint32(len(t.Shape))); err != nil {
-		return err
-	}
-
-	for i := range len(t.Shape) {
-		if err := binary.Write(ws, binary.LittleEndian, t.Shape[len(t.Shape)-i-1]); err != nil {
-			return err
-		}
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, t.Kind); err != nil {
-		return err
-	}
-
-	return binary.Write(ws, binary.LittleEndian, t.Offset)
-}
-
-func ggufWriteTensor(ws io.WriteSeeker, t *Tensor, alignment int64) error {
-	slog.Debug(t.Name, "kind", t.Kind, "shape", t.Shape, "offset", t.Offset)
-
-	offset, err := ws.Seek(0, io.SeekCurrent)
-	if err != nil {
-		return err
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, bytes.Repeat([]byte{0}, int(ggufPadding(offset, alignment)))); err != nil {
-		return err
-	}
-
-	_, err = t.WriteTo(ws)
-	return err
-}
-
-func ggufPadding(offset, align int64) int64 {
+func (gguf) padding(offset, align int64) int64 {
 	return (align - offset%align) % align
 }

llm/llm.go

@@ -4,8 +4,8 @@ package llm
 // #cgo LDFLAGS: -lllama -lggml -lstdc++ -lpthread
 // #cgo darwin,arm64 LDFLAGS: -L${SRCDIR}/build/darwin/arm64_static -L${SRCDIR}/build/darwin/arm64_static/src -L${SRCDIR}/build/darwin/arm64_static/ggml/src -framework Accelerate -framework Metal
 // #cgo darwin,amd64 LDFLAGS: -L${SRCDIR}/build/darwin/x86_64_static -L${SRCDIR}/build/darwin/x86_64_static/src -L${SRCDIR}/build/darwin/x86_64_static/ggml/src
-// #cgo windows,amd64 LDFLAGS: -L${SRCDIR}/build/windows/amd64_static -L${SRCDIR}/build/windows/amd64_static/src -L${SRCDIR}/build/windows/amd64_static/ggml/src
-// #cgo windows,arm64 LDFLAGS: -L${SRCDIR}/build/windows/arm64_static -L${SRCDIR}/build/windows/arm64_static/src -L${SRCDIR}/build/windows/arm64_static/ggml/src
+// #cgo windows,amd64 LDFLAGS: -static-libstdc++ -static-libgcc -static -L${SRCDIR}/build/windows/amd64_static -L${SRCDIR}/build/windows/amd64_static/src -L${SRCDIR}/build/windows/amd64_static/ggml/src
+// #cgo windows,arm64 LDFLAGS: -static-libstdc++ -static-libgcc -static -L${SRCDIR}/build/windows/arm64_static -L${SRCDIR}/build/windows/arm64_static/src -L${SRCDIR}/build/windows/arm64_static/ggml/src
 // #cgo linux,amd64 LDFLAGS: -L${SRCDIR}/build/linux/x86_64_static -L${SRCDIR}/build/linux/x86_64_static/src -L${SRCDIR}/build/linux/x86_64_static/ggml/src
 // #cgo linux,arm64 LDFLAGS: -L${SRCDIR}/build/linux/arm64_static -L${SRCDIR}/build/linux/arm64_static/src -L${SRCDIR}/build/linux/arm64_static/ggml/src
 // #include <stdlib.h>
@@ -33,7 +33,7 @@ func Quantize(infile, outfile string, ftype fileType) error {
 	params.ftype = ftype.Value()
 
 	if rc := C.llama_model_quantize(cinfile, coutfile, &params); rc != 0 {
-		return fmt.Errorf("llama_model_quantize: %d", rc)
+		return fmt.Errorf("failed to quantize model. This model architecture may not be supported, or you may need to upgrade Ollama to the latest version")
 	}
 
 	return nil

llm/memory_test.go

@@ -2,6 +2,7 @@ package llm
 
 import (
 	"bytes"
+	"encoding/binary"
 	"fmt"
 	"os"
 	"testing"
@@ -19,9 +20,10 @@ func TestEstimateGPULayers(t *testing.T) {
 	f, err := os.CreateTemp(t.TempDir(), modelName)
 	require.NoError(t, err)
 	defer f.Close()
+	gguf := NewGGUFV3(binary.LittleEndian)
 	inputLayerCount := 5
 
-	tensors := []*Tensor{
+	tensors := []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))},
@@ -30,7 +32,7 @@ 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 = gguf.Encode(f, KV{
 		"general.architecture":          "llama",
 		"general.name":                  "name",
 		"llama.context_length":          uint32(32),

llm/patches/10-lora.diff

@@ -1,78 +0,0 @@
-diff --git a/CMakeLists.txt b/CMakeLists.txt
-index 4f6cd687..b8c6896b 100644
---- a/CMakeLists.txt
-+++ b/CMakeLists.txt
-@@ -189,3 +189,4 @@ if (LLAMA_BUILD_EXAMPLES)
-     add_subdirectory(examples)
-     add_subdirectory(pocs)
- endif()
-+add_subdirectory(../ext_server ext_server) # ollama
-diff --git a/src/llama.cpp b/src/llama.cpp
-index 2b9ace28..b0151571 100644
---- a/src/llama.cpp
-+++ b/src/llama.cpp
-@@ -18609,6 +18609,20 @@ static int llama_apply_lora_from_file_internal(
-             return 1;
-         }
- 
-+        // show tensor data
-+        auto show_tensor = [](std::string name, ggml_tensor *t) {
-+            LLAMA_LOG_INFO("%s\n", name.c_str());
-+
-+            for(int i=0; i<3; i++) {
-+                for(int j=0; j<3; j++) {
-+                    float v = ggml_get_f32_nd(t, i, j, 0, 0);
-+                    LLAMA_LOG_INFO("%.8f ", v);
-+                }
-+                LLAMA_LOG_INFO(" ...\n");
-+            }
-+            LLAMA_LOG_INFO(" ...\n");
-+        };
-+
-         // load tensor data
-         auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) {
-             read_buf.resize(ggml_nbytes(tensor));
-@@ -18619,6 +18633,9 @@ static int llama_apply_lora_from_file_internal(
-         load_tensor(metaA, loraA);
-         load_tensor(metaB, loraB);
- 
-+        show_tensor(base_name + ".loraA", loraA);
-+        show_tensor(base_name + ".loraB", loraB);
-+
-         // load base model tensor data
-         if (ml) {
-             ml->load_data_for(base_t);
-@@ -18633,8 +18650,10 @@ static int llama_apply_lora_from_file_internal(
-         }
- 
-         if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
--            LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
--                            " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
-+            LLAMA_LOG_ERROR("%s: incompatible tensors: base [%lld, %lld] loraA [%lld, %lld] loraB [%lld, %lld]\n", __func__,
-+                            base_t->ne[0], base_t->ne[1],
-+                            loraA->ne[0], loraA->ne[1],
-+                            loraB->ne[0], loraB->ne[1]);
-             ggml_free(lora_ctx);
-             ggml_backend_buffer_free(lora_buf);
-             ggml_backend_free(backend_cpu);
-@@ -18643,14 +18662,18 @@ static int llama_apply_lora_from_file_internal(
- 
-         auto build_lora_graph = [&]() {
-             // w = w + BA*s
--            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
-+            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraB, loraA);
-             ggml_set_name(BA, "BA");
- 
-             if (scaling != 1.0f) {
--                BA = ggml_scale(lora_ctx, BA, scaling);
-+                //BA = ggml_scale(lora_ctx, BA, scaling);
-+                BA = ggml_scale(lora_ctx, BA, 20.0);
-                 ggml_set_name(BA, "BA_scaled");
-             }
- 
-+            // transpose matrix before we add
-+            BA = ggml_cont(lora_ctx, ggml_transpose(lora_ctx, BA));
-+
-             ggml_tensor * r;
-             r = ggml_add_inplace(lora_ctx, base_t, BA);
-             ggml_set_name(r, "r_add");

llm/server.go

@@ -88,6 +88,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 	var estimate MemoryEstimate
 	var systemTotalMemory uint64
 	var systemFreeMemory uint64
+	var systemSwapFreeMemory uint64
 
 	systemMemInfo, err := gpu.GetCPUMem()
 	if err != nil {
@@ -95,7 +96,8 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 	} else {
 		systemTotalMemory = systemMemInfo.TotalMemory
 		systemFreeMemory = systemMemInfo.FreeMemory
-		slog.Debug("system memory", "total", format.HumanBytes2(systemTotalMemory), "free", systemFreeMemory)
+		systemSwapFreeMemory = systemMemInfo.FreeSwap
+		slog.Debug("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
@@ -122,6 +124,16 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		}
 	}
 
+	// On linux, over-allocating CPU memory will almost always result in an error
+	if runtime.GOOS == "linux" {
+		systemMemoryRequired := estimate.TotalSize - estimate.VRAMSize
+		available := min(systemTotalMemory, systemFreeMemory+systemSwapFreeMemory)
+		if systemMemoryRequired > available {
+			slog.Warn("model request too large for system", "requested", format.HumanBytes2(systemMemoryRequired), "available", available, "total", format.HumanBytes2(systemTotalMemory), "free", format.HumanBytes2(systemFreeMemory), "swap", format.HumanBytes2(systemSwapFreeMemory))
+			return nil, fmt.Errorf("model requires more system memory (%s) than is available (%s)", format.HumanBytes2(systemMemoryRequired), format.HumanBytes2(available))
+		}
+	}
+
 	estimate.log()
 
 	// Loop through potential servers
@@ -254,10 +266,6 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		params = append(params, "--tensor-split", estimate.TensorSplit)
 	}
 
-	if estimate.TensorSplit != "" {
-		params = append(params, "--tensor-split", estimate.TensorSplit)
-	}
-
 	for i := range len(servers) {
 		dir := availableServers[servers[i]]
 		if dir == "" {
@@ -679,7 +687,7 @@ type CompletionRequest struct {
 	Prompt  string
 	Format  string
 	Images  []ImageData
-	Options api.Options
+	Options *api.Options
 }
 
 type CompletionResponse struct {

openai/openai.go

@@ -338,12 +338,16 @@ func fromCompleteRequest(r CompletionRequest) (api.GenerateRequest, error) {
 	switch stop := r.Stop.(type) {
 	case string:
 		options["stop"] = []string{stop}
-	case []string:
-		options["stop"] = stop
-	default:
-		if r.Stop != nil {
-			return api.GenerateRequest{}, fmt.Errorf("invalid type for 'stop' field: %T", r.Stop)
+	case []any:
+		var stops []string
+		for _, s := range stop {
+			if str, ok := s.(string); ok {
+				stops = append(stops, str)
+			} else {
+				return api.GenerateRequest{}, fmt.Errorf("invalid type for 'stop' field: %T", s)
+			}
 		}
+		options["stop"] = stops
 	}
 
 	if r.MaxTokens != nil {

openai/openai_test.go

@@ -3,7 +3,6 @@ package openai
 import (
 	"bytes"
 	"encoding/json"
-	"fmt"
 	"io"
 	"net/http"
 	"net/http/httptest"
@@ -16,7 +15,133 @@ import (
 	"github.com/stretchr/testify/assert"
 )
 
-func TestMiddleware(t *testing.T) {
+func TestMiddlewareRequests(t *testing.T) {
+	type testCase struct {
+		Name     string
+		Method   string
+		Path     string
+		Handler  func() gin.HandlerFunc
+		Setup    func(t *testing.T, req *http.Request)
+		Expected func(t *testing.T, req *http.Request)
+	}
+
+	var capturedRequest *http.Request
+
+	captureRequestMiddleware := func() gin.HandlerFunc {
+		return func(c *gin.Context) {
+			bodyBytes, _ := io.ReadAll(c.Request.Body)
+			c.Request.Body = io.NopCloser(bytes.NewReader(bodyBytes))
+			capturedRequest = c.Request
+			c.Next()
+		}
+	}
+
+	testCases := []testCase{
+		{
+			Name:    "chat handler",
+			Method:  http.MethodPost,
+			Path:    "/api/chat",
+			Handler: ChatMiddleware,
+			Setup: func(t *testing.T, req *http.Request) {
+				body := ChatCompletionRequest{
+					Model:    "test-model",
+					Messages: []Message{{Role: "user", Content: "Hello"}},
+				}
+
+				bodyBytes, _ := json.Marshal(body)
+
+				req.Body = io.NopCloser(bytes.NewReader(bodyBytes))
+				req.Header.Set("Content-Type", "application/json")
+			},
+			Expected: func(t *testing.T, req *http.Request) {
+				var chatReq api.ChatRequest
+				if err := json.NewDecoder(req.Body).Decode(&chatReq); err != nil {
+					t.Fatal(err)
+				}
+
+				if chatReq.Messages[0].Role != "user" {
+					t.Fatalf("expected 'user', got %s", chatReq.Messages[0].Role)
+				}
+
+				if chatReq.Messages[0].Content != "Hello" {
+					t.Fatalf("expected 'Hello', got %s", chatReq.Messages[0].Content)
+				}
+			},
+		},
+		{
+			Name:    "completions handler",
+			Method:  http.MethodPost,
+			Path:    "/api/generate",
+			Handler: CompletionsMiddleware,
+			Setup: func(t *testing.T, req *http.Request) {
+				temp := float32(0.8)
+				body := CompletionRequest{
+					Model:       "test-model",
+					Prompt:      "Hello",
+					Temperature: &temp,
+					Stop:        []string{"\n", "stop"},
+				}
+
+				bodyBytes, _ := json.Marshal(body)
+
+				req.Body = io.NopCloser(bytes.NewReader(bodyBytes))
+				req.Header.Set("Content-Type", "application/json")
+			},
+			Expected: func(t *testing.T, req *http.Request) {
+				var genReq api.GenerateRequest
+				if err := json.NewDecoder(req.Body).Decode(&genReq); err != nil {
+					t.Fatal(err)
+				}
+
+				if genReq.Prompt != "Hello" {
+					t.Fatalf("expected 'Hello', got %s", genReq.Prompt)
+				}
+
+				if genReq.Options["temperature"] != 1.6 {
+					t.Fatalf("expected 1.6, got %f", genReq.Options["temperature"])
+				}
+
+				stopTokens, ok := genReq.Options["stop"].([]any)
+
+				if !ok {
+					t.Fatalf("expected stop tokens to be a list")
+				}
+
+				if stopTokens[0] != "\n" || stopTokens[1] != "stop" {
+					t.Fatalf("expected ['\\n', 'stop'], got %v", stopTokens)
+				}
+			},
+		},
+	}
+
+	gin.SetMode(gin.TestMode)
+	router := gin.New()
+
+	endpoint := func(c *gin.Context) {
+		c.Status(http.StatusOK)
+	}
+
+	for _, tc := range testCases {
+		t.Run(tc.Name, func(t *testing.T) {
+			router = gin.New()
+			router.Use(captureRequestMiddleware())
+			router.Use(tc.Handler())
+			router.Handle(tc.Method, tc.Path, endpoint)
+			req, _ := http.NewRequest(tc.Method, tc.Path, nil)
+
+			if tc.Setup != nil {
+				tc.Setup(t, req)
+			}
+
+			resp := httptest.NewRecorder()
+			router.ServeHTTP(resp, req)
+
+			tc.Expected(t, capturedRequest)
+		})
+	}
+}
+
+func TestMiddlewareResponses(t *testing.T) {
 	type testCase struct {
 		Name     string
 		Method   string
@@ -30,159 +155,7 @@ func TestMiddleware(t *testing.T) {
 
 	testCases := []testCase{
 		{
-			Name:     "chat handler",
-			Method:   http.MethodPost,
-			Path:     "/api/chat",
-			TestPath: "/api/chat",
-			Handler:  ChatMiddleware,
-			Endpoint: func(c *gin.Context) {
-				var chatReq api.ChatRequest
-				if err := c.ShouldBindJSON(&chatReq); err != nil {
-					c.JSON(http.StatusBadRequest, gin.H{"error": "invalid request"})
-					return
-				}
-
-				userMessage := chatReq.Messages[0].Content
-				var assistantMessage string
-
-				switch userMessage {
-				case "Hello":
-					assistantMessage = "Hello!"
-				default:
-					assistantMessage = "I'm not sure how to respond to that."
-				}
-
-				c.JSON(http.StatusOK, api.ChatResponse{
-					Message: api.Message{
-						Role:    "assistant",
-						Content: assistantMessage,
-					},
-				})
-			},
-			Setup: func(t *testing.T, req *http.Request) {
-				body := ChatCompletionRequest{
-					Model:    "test-model",
-					Messages: []Message{{Role: "user", Content: "Hello"}},
-				}
-
-				bodyBytes, _ := json.Marshal(body)
-
-				req.Body = io.NopCloser(bytes.NewReader(bodyBytes))
-				req.Header.Set("Content-Type", "application/json")
-			},
-			Expected: func(t *testing.T, resp *httptest.ResponseRecorder) {
-				assert.Equal(t, http.StatusOK, resp.Code)
-
-				var chatResp ChatCompletion
-				if err := json.NewDecoder(resp.Body).Decode(&chatResp); err != nil {
-					t.Fatal(err)
-				}
-
-				if chatResp.Object != "chat.completion" {
-					t.Fatalf("expected chat.completion, got %s", chatResp.Object)
-				}
-
-				if chatResp.Choices[0].Message.Content != "Hello!" {
-					t.Fatalf("expected Hello!, got %s", chatResp.Choices[0].Message.Content)
-				}
-			},
-		},
-		{
-			Name:     "completions handler",
-			Method:   http.MethodPost,
-			Path:     "/api/generate",
-			TestPath: "/api/generate",
-			Handler:  CompletionsMiddleware,
-			Endpoint: func(c *gin.Context) {
-				c.JSON(http.StatusOK, api.GenerateResponse{
-					Response: "Hello!",
-				})
-			},
-			Setup: func(t *testing.T, req *http.Request) {
-				body := CompletionRequest{
-					Model:  "test-model",
-					Prompt: "Hello",
-				}
-
-				bodyBytes, _ := json.Marshal(body)
-
-				req.Body = io.NopCloser(bytes.NewReader(bodyBytes))
-				req.Header.Set("Content-Type", "application/json")
-			},
-			Expected: func(t *testing.T, resp *httptest.ResponseRecorder) {
-				assert.Equal(t, http.StatusOK, resp.Code)
-				var completionResp Completion
-				if err := json.NewDecoder(resp.Body).Decode(&completionResp); err != nil {
-					t.Fatal(err)
-				}
-
-				if completionResp.Object != "text_completion" {
-					t.Fatalf("expected text_completion, got %s", completionResp.Object)
-				}
-
-				if completionResp.Choices[0].Text != "Hello!" {
-					t.Fatalf("expected Hello!, got %s", completionResp.Choices[0].Text)
-				}
-			},
-		},
-		{
-			Name:     "completions handler with params",
-			Method:   http.MethodPost,
-			Path:     "/api/generate",
-			TestPath: "/api/generate",
-			Handler:  CompletionsMiddleware,
-			Endpoint: func(c *gin.Context) {
-				var generateReq api.GenerateRequest
-				if err := c.ShouldBindJSON(&generateReq); err != nil {
-					c.JSON(http.StatusBadRequest, gin.H{"error": "invalid request"})
-					return
-				}
-
-				temperature := generateReq.Options["temperature"].(float64)
-				var assistantMessage string
-
-				switch temperature {
-				case 1.6:
-					assistantMessage = "Received temperature of 1.6"
-				default:
-					assistantMessage = fmt.Sprintf("Received temperature of %f", temperature)
-				}
-
-				c.JSON(http.StatusOK, api.GenerateResponse{
-					Response: assistantMessage,
-				})
-			},
-			Setup: func(t *testing.T, req *http.Request) {
-				temp := float32(0.8)
-				body := CompletionRequest{
-					Model:       "test-model",
-					Prompt:      "Hello",
-					Temperature: &temp,
-				}
-
-				bodyBytes, _ := json.Marshal(body)
-
-				req.Body = io.NopCloser(bytes.NewReader(bodyBytes))
-				req.Header.Set("Content-Type", "application/json")
-			},
-			Expected: func(t *testing.T, resp *httptest.ResponseRecorder) {
-				assert.Equal(t, http.StatusOK, resp.Code)
-				var completionResp Completion
-				if err := json.NewDecoder(resp.Body).Decode(&completionResp); err != nil {
-					t.Fatal(err)
-				}
-
-				if completionResp.Object != "text_completion" {
-					t.Fatalf("expected text_completion, got %s", completionResp.Object)
-				}
-
-				if completionResp.Choices[0].Text != "Received temperature of 1.6" {
-					t.Fatalf("expected Received temperature of 1.6, got %s", completionResp.Choices[0].Text)
-				}
-			},
-		},
-		{
-			Name:     "completions handler with error",
+			Name:     "completions handler error forwarding",
 			Method:   http.MethodPost,
 			Path:     "/api/generate",
 			TestPath: "/api/generate",

scripts/build_windows.ps1

@@ -107,9 +107,12 @@ function gatherDependencies() {
 
     # TODO - this varies based on host build system and MSVC version - drive from dumpbin output
     # currently works for Win11 + MSVC 2019 + Cuda V11
-    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\msvcp140.dll" "${script:DEPS_DIR}\ollama_runners\"
+    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\msvcp140*.dll" "${script:DEPS_DIR}\ollama_runners\"
     cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140.dll" "${script:DEPS_DIR}\ollama_runners\"
     cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140_1.dll" "${script:DEPS_DIR}\ollama_runners\"
+    foreach ($part in $("runtime", "stdio", "filesystem", "math", "convert", "heap", "string", "time", "locale", "environment")) {
+        cp "$env:VCToolsRedistDir\..\..\..\Tools\Llvm\x64\bin\api-ms-win-crt-${part}*.dll" "${script:DEPS_DIR}\ollama_runners\"
+    }
 
 
     cp "${script:SRC_DIR}\app\ollama_welcome.ps1" "${script:SRC_DIR}\dist\"

server/images.go

@@ -34,6 +34,8 @@ import (
 	"github.com/ollama/ollama/version"
 )
 
+var errCapabilityCompletion = errors.New("completion")
+
 type Capability string
 
 const CapabilityCompletion = Capability("completion")
@@ -62,7 +64,10 @@ type Model struct {
 	Template *template.Template
 }
 
-func (m *Model) Has(caps ...Capability) bool {
+// CheckCapabilities checks if the model has the specified capabilities returning an error describing
+// any missing or unknown capabilities
+func (m *Model) CheckCapabilities(caps ...Capability) error {
+	var errs []error
 	for _, cap := range caps {
 		switch cap {
 		case CapabilityCompletion:
@@ -81,15 +86,19 @@ func (m *Model) Has(caps ...Capability) bool {
 			}
 
 			if _, ok := ggml.KV()[fmt.Sprintf("%s.pooling_type", ggml.KV().Architecture())]; ok {
-				return false
+				errs = append(errs, errCapabilityCompletion)
 			}
 		default:
 			slog.Error("unknown capability", "capability", cap)
-			return false
+			return fmt.Errorf("unknown capability: %s", cap)
 		}
 	}
 
-	return true
+	if err := errors.Join(errs...); err != nil {
+		return fmt.Errorf("missing capabilities: %w", errors.Join(errs...))
+	}
+
+	return nil
 }
 
 func (m *Model) String() string {
@@ -457,7 +466,7 @@ func CreateModel(ctx context.Context, name model.Name, modelFileDir, quantizatio
 				if baseLayer.GGML != nil {
 					config.ModelFormat = cmp.Or(config.ModelFormat, baseLayer.GGML.Name())
 					config.ModelFamily = cmp.Or(config.ModelFamily, baseLayer.GGML.KV().Architecture())
-					config.ModelType = cmp.Or(config.ModelType, format.HumanNumber(baseLayer.GGML.KV().ParameterCount()))
+					config.ModelType = cmp.Or(config.ModelType, format.RoundedParameter(baseLayer.GGML.KV().ParameterCount()))
 					config.FileType = cmp.Or(config.FileType, baseLayer.GGML.KV().FileType().String())
 					config.ModelFamilies = append(config.ModelFamilies, baseLayer.GGML.KV().Architecture())
 				}

server/model.go

@@ -129,24 +129,38 @@ func extractFromZipFile(p string, file *os.File, fn func(api.ProgressResponse))
 }
 
 func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {
-	layerType := "application/vnd.ollama.image.model"
-	convertAdapter, err := convert.DetectNPZ(file.Name())
-	if err != nil {
-		return nil, err
-	}
-
 	tempDir, err := os.MkdirTemp(filepath.Dir(file.Name()), "")
 	if err != nil {
 		return nil, err
 	}
 	defer os.RemoveAll(tempDir)
 
-	if !convertAdapter {
-		if err := extractFromZipFile(tempDir, file, fn); err != nil {
-			return nil, err
-		}
-	} else {
-		layerType = "application/vnd.ollama.image.adapter"
+	if err := extractFromZipFile(tempDir, file, fn); err != nil {
+		return nil, err
+	}
+
+	mf, err := convert.GetModelFormat(tempDir)
+	if err != nil {
+		return nil, err
+	}
+
+	params, err := mf.GetParams(tempDir)
+	if err != nil {
+		return nil, err
+	}
+
+	mArch, err := mf.GetModelArch("", tempDir, params)
+	if err != nil {
+		return nil, err
+	}
+
+	fn(api.ProgressResponse{Status: "processing tensors"})
+	if err := mArch.GetTensors(); err != nil {
+		return nil, err
+	}
+
+	if err := mArch.LoadVocab(); err != nil {
+		return nil, err
 	}
 
 	fn(api.ProgressResponse{Status: "converting model"})
@@ -160,22 +174,15 @@ func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(a
 	defer temp.Close()
 	defer os.Remove(temp.Name())
 
-	if convertAdapter {
-		slog.Info("convert adapter")
-		if err := convert.ConvertAdapter(file.Name(), temp); err != nil {
-			return nil, err
-		}
-	} else {
-		if err := convert.Convert(tempDir, temp); err != nil {
-			return nil, err
-		}
+	if err = mArch.WriteGGUF(temp); err != nil {
+		return nil, err
 	}
 
 	if _, err := temp.Seek(0, io.SeekStart); err != nil {
 		return nil, err
 	}
 
-	layer, err := NewLayer(temp, layerType)
+	layer, err := NewLayer(temp, "application/vnd.ollama.image.model")
 	if err != nil {
 		return nil, err
 	}
@@ -194,11 +201,7 @@ func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(a
 	layers = append(layers, &layerGGML{layer, ggml})
 
 	intermediateBlobs[digest] = layer.Digest
-	if !convertAdapter {
-		return detectChatTemplate(layers)
-	}
-
-	return layers, nil
+	return detectChatTemplate(layers)
 }
 
 func parseFromFile(ctx context.Context, file *os.File, digest string, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {

server/prompt.go

@@ -1,217 +1,83 @@
 package server
 
 import (
-	"fmt"
+	"bytes"
+	"context"
 	"log/slog"
-	"strings"
-
-	"text/template/parse"
+	"slices"
 
 	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/template"
 )
 
-// isResponseNode checks if the node contains .Response
-func isResponseNode(node *parse.ActionNode) bool {
-	for _, cmd := range node.Pipe.Cmds {
-		for _, arg := range cmd.Args {
-			if fieldNode, ok := arg.(*parse.FieldNode); ok && len(fieldNode.Ident) > 0 {
-				if fieldNode.Ident[0] == "Response" {
-					return true
-				}
-			}
+type tokenizeFunc func(context.Context, string) ([]int, error)
+
+// 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) (prompt string, images []llm.ImageData, _ error) {
+	// pull out any system messages which should always be included in the prompt
+	var system []api.Message
+	msgs = slices.DeleteFunc(msgs, func(m api.Message) bool {
+		if m.Role == "system" {
+			system = append(system, m)
+			return true
 		}
-	}
-	return false
-}
 
-// formatTemplateForResponse formats the template AST to:
-// 1. remove all nodes after the first .Response (if generate=true)
-// 2. add a .Response node to the end if it doesn't exist
-// TODO(jmorganca): this should recursively cut the template before the first .Response
-func formatTemplateForResponse(tmpl *template.Template, generate bool) {
-	var found bool
-	for i, node := range tmpl.Tree.Root.Nodes {
-		if actionNode, ok := node.(*parse.ActionNode); ok {
-			if isResponseNode(actionNode) {
-				found = true
-				if generate {
-					tmpl.Tree.Root.Nodes = tmpl.Tree.Root.Nodes[:i+1]
-					break
-				}
-			}
+		return false
+	})
+
+	if len(system) == 0 && m.System != "" {
+		// add model system prompt since it wasn't provided
+		system = append(system, api.Message{Role: "system", Content: m.System})
+	}
+
+	// always include the last message
+	n := len(msgs) - 1
+	// in reverse, find all messages that fit into context window
+	for i := n - 1; i >= 0; i-- {
+		var b bytes.Buffer
+		if err := m.Template.Execute(&b, template.Values{Messages: append(system, msgs[i:]...)}); err != nil {
+			return "", nil, err
 		}
-	}
 
-	if !found {
-		// add the response node if it doesn't exist
-		responseFieldNode := &parse.FieldNode{NodeType: parse.NodeField, Ident: []string{"Response"}}
-		responsePipeNode := &parse.PipeNode{NodeType: parse.NodePipe, Cmds: []*parse.CommandNode{{NodeType: parse.NodeCommand, Args: []parse.Node{responseFieldNode}}}}
-		responseActionNode := &parse.ActionNode{NodeType: parse.NodeAction, Pipe: responsePipeNode}
-		tmpl.Tree.Root.Nodes = append(tmpl.Tree.Root.Nodes, responseActionNode)
-	}
-}
-
-// Prompt renders a prompt from a template. If generate is set to true,
-// the response and parts of the template following it are not rendered
-func Prompt(tmpl *template.Template, system, prompt, response string, generate bool) (string, error) {
-	formatTemplateForResponse(tmpl, generate)
-
-	vars := map[string]any{
-		"System":   system,
-		"Prompt":   prompt,
-		"Response": response,
-	}
-
-	var sb strings.Builder
-	if err := tmpl.Execute(&sb, vars); err != nil {
-		return "", err
-	}
-
-	return sb.String(), nil
-}
-
-func countTokens(tmpl *template.Template, system string, prompt string, response string, encode func(string) ([]int, error)) (int, error) {
-	rendered, err := Prompt(tmpl, system, prompt, response, false)
-	if err != nil {
-		return 0, err
-	}
-
-	tokens, err := encode(rendered)
-	if err != nil {
-		slog.Error("failed to encode prompt", "err", err)
-		return 0, err
-	}
-
-	return len(tokens), err
-}
-
-// ChatPrompt builds up a prompt from a series of messages, truncating based on context window size
-func ChatPrompt(tmpl *template.Template, messages []api.Message, window int, encode func(string) ([]int, error)) (string, error) {
-	type prompt struct {
-		System   string
-		Prompt   string
-		Response string
-
-		images []int
-		tokens int
-	}
-
-	var p prompt
-
-	// iterate through messages to build up {system,user,response} prompts
-	var imgId int
-	var prompts []prompt
-	for _, msg := range messages {
-		switch strings.ToLower(msg.Role) {
-		case "system":
-			if p.System != "" || p.Prompt != "" || p.Response != "" {
-				prompts = append(prompts, p)
-				p = prompt{}
-			}
-
-			p.System = msg.Content
-		case "user":
-			if p.Prompt != "" || p.Response != "" {
-				prompts = append(prompts, p)
-				p = prompt{}
-			}
-
-			var sb strings.Builder
-			for range msg.Images {
-				fmt.Fprintf(&sb, "[img-%d] ", imgId)
-				p.images = append(p.images, imgId)
-				imgId += 1
-			}
-
-			sb.WriteString(msg.Content)
-			p.Prompt = sb.String()
-		case "assistant":
-			if p.Response != "" {
-				prompts = append(prompts, p)
-				p = prompt{}
-			}
-
-			p.Response = msg.Content
-		default:
-			return "", fmt.Errorf("invalid role: %s, role must be one of [system, user, assistant]", msg.Role)
-		}
-	}
-
-	// add final prompt
-	if p.System != "" || p.Prompt != "" || p.Response != "" {
-		prompts = append(prompts, p)
-	}
-
-	// calculate token lengths for each prompt, estimating 768 tokens per images
-	for i, p := range prompts {
-		tokens, err := countTokens(tmpl, p.System, p.Prompt, p.Response, encode)
+		s, err := tokenize(ctx, b.String())
 		if err != nil {
-			return "", err
+			return "", nil, err
 		}
 
-		prompts[i].tokens = tokens + len(prompts[i].images)*768
-	}
-
-	// truncate images and prompts starting from the beginning of the list
-	// until either one prompt remains or the total tokens fits the context window
-	// TODO (jmorganca): this doesn't account for the context window room required for the response
-	for {
-		var required int
-		for _, p := range prompts {
-			required += p.tokens
+		c := len(s)
+		if m.ProjectorPaths != nil {
+			for _, m := range msgs[i:] {
+				// images are represented as 768 sized embeddings
+				// TODO: get embedding length from project metadata
+				c += 768 * len(m.Images)
+			}
 		}
 
-		required += 1 // for bos token
-
-		if required <= window {
-			slog.Debug("prompt now fits in context window", "required", required, "window", window)
+		if c > opts.NumCtx {
+			slog.Debug("truncating input messages which exceed context length", "truncated", len(msgs[i:]))
 			break
+		} else {
+			n = i
 		}
-
-		prompt := &prompts[0]
-
-		if len(prompt.images) > 1 {
-			img := prompt.images[0]
-			slog.Debug("prompt longer than context window, removing image", "id", img, "required", required, "window", window)
-			prompt.images = prompt.images[1:]
-			prompt.Prompt = strings.Replace(prompt.Prompt, fmt.Sprintf(" [img-%d]", img), "", 1)
-			prompt.tokens -= 768
-			continue
-		}
-
-		if len(prompts) > 1 {
-			slog.Debug("required tokens longer than context window, removing first prompt", "prompt", prompts[0].tokens, "required", required, "window", window)
-			system := prompt.System
-			prompts = prompts[1:]
-
-			if system != "" && prompts[0].System == "" {
-				prompts[0].System = system
-
-				tokens, err := countTokens(tmpl, prompts[0].System, prompts[0].Prompt, prompts[0].Response, encode)
-				if err != nil {
-					return "", err
-				}
-
-				prompts[0].tokens = tokens + len(prompts[0].images)*768
-			}
-
-			continue
-		}
-
-		// stop truncating if there's only one prompt left
-		break
 	}
 
-	var sb strings.Builder
-	for i, p := range prompts {
-		// last prompt should leave the response unrendered (for completion)
-		rendered, err := Prompt(tmpl, p.System, p.Prompt, p.Response, i == len(prompts)-1)
-		if err != nil {
-			return "", err
-		}
-		sb.WriteString(rendered)
+	// truncate any messages that do not fit into the context window
+	var b bytes.Buffer
+	if err := m.Template.Execute(&b, template.Values{Messages: append(system, msgs[n:]...)}); err != nil {
+		return "", nil, err
 	}
 
-	return sb.String(), nil
+	for _, m := range msgs[n:] {
+		for _, i := range m.Images {
+			images = append(images, llm.ImageData{
+				ID:   len(images),
+				Data: i,
+			})
+		}
+	}
+
+	return b.String(), images, nil
 }

server/prompt_test.go

@@ -1,6 +1,8 @@
 package server
 
 import (
+	"bytes"
+	"context"
 	"strings"
 	"testing"
 
@@ -8,208 +10,195 @@ import (
 	"github.com/ollama/ollama/template"
 )
 
-func TestPrompt(t *testing.T) {
-	tests := []struct {
-		name     string
-		template string
-		system   string
-		prompt   string
-		response string
-		generate bool
-		want     string
-	}{
-		{
-			name:     "simple prompt",
-			template: "[INST] {{ .System }} {{ .Prompt }} [/INST]",
-			system:   "You are a Wizard.",
-			prompt:   "What are the potion ingredients?",
-			want:     "[INST] You are a Wizard. What are the potion ingredients? [/INST]",
-		},
-		{
-			name:     "implicit response",
-			template: "[INST] {{ .System }} {{ .Prompt }} [/INST]",
-			system:   "You are a Wizard.",
-			prompt:   "What are the potion ingredients?",
-			response: "I don't know.",
-			want:     "[INST] You are a Wizard. What are the potion ingredients? [/INST]I don't know.",
-		},
-		{
-			name:     "response",
-			template: "[INST] {{ .System }} {{ .Prompt }} [/INST] {{ .Response }}",
-			system:   "You are a Wizard.",
-			prompt:   "What are the potion ingredients?",
-			response: "I don't know.",
-			want:     "[INST] You are a Wizard. What are the potion ingredients? [/INST] I don't know.",
-		},
-		{
-			name:     "cut",
-			template: "<system>{{ .System }}</system><user>{{ .Prompt }}</user><assistant>{{ .Response }}</assistant>",
-			system:   "You are a Wizard.",
-			prompt:   "What are the potion ingredients?",
-			response: "I don't know.",
-			generate: true,
-			want:     "<system>You are a Wizard.</system><user>What are the potion ingredients?</user><assistant>I don't know.",
-		},
-		{
-			name:     "nocut",
-			template: "<system>{{ .System }}</system><user>{{ .Prompt }}</user><assistant>{{ .Response }}</assistant>",
-			system:   "You are a Wizard.",
-			prompt:   "What are the potion ingredients?",
-			response: "I don't know.",
-			want:     "<system>You are a Wizard.</system><user>What are the potion ingredients?</user><assistant>I don't know.</assistant>",
-		},
+func tokenize(_ context.Context, s string) (tokens []int, err error) {
+	for range strings.Fields(s) {
+		tokens = append(tokens, len(tokens))
 	}
 
-	for _, tc := range tests {
-		t.Run(tc.name, func(t *testing.T) {
-			tmpl, err := template.Parse(tc.template)
-			if err != nil {
-				t.Fatal(err)
-			}
-
-			got, err := Prompt(tmpl, tc.system, tc.prompt, tc.response, tc.generate)
-			if err != nil {
-				t.Errorf("error = %v", err)
-			}
-
-			if got != tc.want {
-				t.Errorf("got = %v, want %v", got, tc.want)
-			}
-		})
-	}
+	return
 }
 
 func TestChatPrompt(t *testing.T) {
-	tests := []struct {
-		name     string
-		template string
-		messages []api.Message
-		window   int
-		want     string
+	type expect struct {
+		prompt string
+		images [][]byte
+	}
+
+	cases := []struct {
+		name  string
+		limit int
+		msgs  []api.Message
+		expect
 	}{
 		{
-			name:     "simple prompt",
-			template: "[INST] {{ .Prompt }} [/INST]",
-			messages: []api.Message{
-				{Role: "user", Content: "Hello"},
+			name:  "messages",
+			limit: 64,
+			msgs: []api.Message{
+				{Role: "user", Content: "You're a test, Harry!"},
+				{Role: "assistant", Content: "I-I'm a what?"},
+				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager."},
+			},
+			expect: expect{
+				prompt: "You're a test, Harry! I-I'm a what? A test. And a thumping good one at that, I'd wager. ",
 			},
-			window: 1024,
-			want:   "[INST] Hello [/INST]",
 		},
 		{
-			name:     "with system message",
-			template: "[INST] {{ if .System }}<<SYS>>{{ .System }}<</SYS>> {{ end }}{{ .Prompt }} [/INST]",
-			messages: []api.Message{
-				{Role: "system", Content: "You are a Wizard."},
-				{Role: "user", Content: "Hello"},
+			name:  "truncate messages",
+			limit: 1,
+			msgs: []api.Message{
+				{Role: "user", Content: "You're a test, Harry!"},
+				{Role: "assistant", Content: "I-I'm a what?"},
+				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager."},
+			},
+			expect: expect{
+				prompt: "A test. And a thumping good one at that, I'd wager. ",
 			},
-			window: 1024,
-			want:   "[INST] <<SYS>>You are a Wizard.<</SYS>> Hello [/INST]",
 		},
 		{
-			name:     "with response",
-			template: "[INST] {{ if .System }}<<SYS>>{{ .System }}<</SYS>> {{ end }}{{ .Prompt }} [/INST] {{ .Response }}",
-			messages: []api.Message{
-				{Role: "system", Content: "You are a Wizard."},
-				{Role: "user", Content: "Hello"},
-				{Role: "assistant", Content: "I am?"},
+			name:  "truncate messages with image",
+			limit: 64,
+			msgs: []api.Message{
+				{Role: "user", Content: "You're a test, Harry!"},
+				{Role: "assistant", Content: "I-I'm a what?"},
+				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager.", Images: []api.ImageData{[]byte("something")}},
+			},
+			expect: expect{
+				prompt: "[img-0] A test. And a thumping good one at that, I'd wager. ",
+				images: [][]byte{
+					[]byte("something"),
+				},
 			},
-			window: 1024,
-			want:   "[INST] <<SYS>>You are a Wizard.<</SYS>> Hello [/INST] I am?",
 		},
 		{
-			name:     "with implicit response",
-			template: "[INST] {{ if .System }}<<SYS>>{{ .System }}<</SYS>> {{ end }}{{ .Prompt }} [/INST]",
-			messages: []api.Message{
-				{Role: "system", Content: "You are a Wizard."},
-				{Role: "user", Content: "Hello"},
-				{Role: "assistant", Content: "I am?"},
+			name:  "truncate messages with images",
+			limit: 64,
+			msgs: []api.Message{
+				{Role: "user", Content: "You're a test, Harry!", Images: []api.ImageData{[]byte("something")}},
+				{Role: "assistant", Content: "I-I'm a what?"},
+				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager.", Images: []api.ImageData{[]byte("somethingelse")}},
+			},
+			expect: expect{
+				prompt: "[img-0] A test. And a thumping good one at that, I'd wager. ",
+				images: [][]byte{
+					[]byte("somethingelse"),
+				},
 			},
-			window: 1024,
-			want:   "[INST] <<SYS>>You are a Wizard.<</SYS>> Hello [/INST]I am?",
 		},
 		{
-			name:     "with conversation",
-			template: "[INST] {{ if .System }}<<SYS>>{{ .System }}<</SYS>> {{ end }}{{ .Prompt }} [/INST] {{ .Response }} ",
-			messages: []api.Message{
-				{Role: "system", Content: "You are a Wizard."},
-				{Role: "user", Content: "What are the potion ingredients?"},
-				{Role: "assistant", Content: "sugar"},
-				{Role: "user", Content: "Anything else?"},
+			name:  "messages with images",
+			limit: 2048,
+			msgs: []api.Message{
+				{Role: "user", Content: "You're a test, Harry!", Images: []api.ImageData{[]byte("something")}},
+				{Role: "assistant", Content: "I-I'm a what?"},
+				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager.", Images: []api.ImageData{[]byte("somethingelse")}},
+			},
+			expect: expect{
+				prompt: "[img-0] You're a test, Harry! I-I'm a what? [img-1] A test. And a thumping good one at that, I'd wager. ",
+				images: [][]byte{
+					[]byte("something"),
+					[]byte("somethingelse"),
+				},
 			},
-			window: 1024,
-			want:   "[INST] <<SYS>>You are a Wizard.<</SYS>> What are the potion ingredients? [/INST] sugar [INST] Anything else? [/INST] ",
 		},
 		{
-			name:     "with truncation",
-			template: "{{ .System }} {{ .Prompt }} {{ .Response }} ",
-			messages: []api.Message{
-				{Role: "system", Content: "You are a Wizard."},
-				{Role: "user", Content: "Hello"},
-				{Role: "assistant", Content: "I am?"},
-				{Role: "user", Content: "Why is the sky blue?"},
-				{Role: "assistant", Content: "The sky is blue from rayleigh scattering"},
+			name:  "message with image tag",
+			limit: 2048,
+			msgs: []api.Message{
+				{Role: "user", Content: "You're a test, Harry! [img]", Images: []api.ImageData{[]byte("something")}},
+				{Role: "assistant", Content: "I-I'm a what?"},
+				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager.", Images: []api.ImageData{[]byte("somethingelse")}},
+			},
+			expect: expect{
+				prompt: "You're a test, Harry! [img-0] I-I'm a what? [img-1] A test. And a thumping good one at that, I'd wager. ",
+				images: [][]byte{
+					[]byte("something"),
+					[]byte("somethingelse"),
+				},
 			},
-			window: 10,
-			want:   "You are a Wizard. Why is the sky blue? The sky is blue from rayleigh scattering",
 		},
 		{
-			name:     "images",
-			template: "{{ .System }} {{ .Prompt }}",
-			messages: []api.Message{
-				{Role: "system", Content: "You are a Wizard."},
-				{Role: "user", Content: "Hello", Images: []api.ImageData{[]byte("base64")}},
+			name:  "messages with interleaved images",
+			limit: 2048,
+			msgs: []api.Message{
+				{Role: "user", Content: "You're a test, Harry!"},
+				{Role: "user", Images: []api.ImageData{[]byte("something")}},
+				{Role: "user", Images: []api.ImageData{[]byte("somethingelse")}},
+				{Role: "assistant", Content: "I-I'm a what?"},
+				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager."},
+			},
+			expect: expect{
+				prompt: "You're a test, Harry!\n\n[img-0]\n\n[img-1] I-I'm a what? A test. And a thumping good one at that, I'd wager. ",
+				images: [][]byte{
+					[]byte("something"),
+					[]byte("somethingelse"),
+				},
 			},
-			window: 1024,
-			want:   "You are a Wizard. [img-0] Hello",
 		},
 		{
-			name:     "images truncated",
-			template: "{{ .System }} {{ .Prompt }}",
-			messages: []api.Message{
-				{Role: "system", Content: "You are a Wizard."},
-				{Role: "user", Content: "Hello", Images: []api.ImageData{[]byte("img1"), []byte("img2")}},
+			name:  "truncate message with interleaved images",
+			limit: 1024,
+			msgs: []api.Message{
+				{Role: "user", Content: "You're a test, Harry!"},
+				{Role: "user", Images: []api.ImageData{[]byte("something")}},
+				{Role: "user", Images: []api.ImageData{[]byte("somethingelse")}},
+				{Role: "assistant", Content: "I-I'm a what?"},
+				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager."},
+			},
+			expect: expect{
+				prompt: "[img-0] I-I'm a what? A test. And a thumping good one at that, I'd wager. ",
+				images: [][]byte{
+					[]byte("somethingelse"),
+				},
 			},
-			window: 1024,
-			want:   "You are a Wizard. [img-0] [img-1] Hello",
 		},
 		{
-			name:     "empty list",
-			template: "{{ .System }} {{ .Prompt }}",
-			messages: []api.Message{},
-			window:   1024,
-			want:     "",
-		},
-		{
-			name:     "empty prompt",
-			template: "[INST] {{ if .System }}<<SYS>>{{ .System }}<</SYS>> {{ end }}{{ .Prompt }} [/INST] {{ .Response }} ",
-			messages: []api.Message{
-				{Role: "user", Content: ""},
+			name:  "message with system prompt",
+			limit: 2048,
+			msgs: []api.Message{
+				{Role: "system", Content: "You are the Test Who Lived."},
+				{Role: "user", Content: "You're a test, Harry!"},
+				{Role: "assistant", Content: "I-I'm a what?"},
+				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager."},
+			},
+			expect: expect{
+				prompt: "You are the Test Who Lived. You're a test, Harry! I-I'm a what? A test. And a thumping good one at that, I'd wager. ",
 			},
-			window: 1024,
-			want:   "",
 		},
 	}
 
-	encode := func(s string) ([]int, error) {
-		words := strings.Fields(s)
-		return make([]int, len(words)), nil
+	tmpl, err := template.Parse(`
+{{- if .System }}{{ .System }} {{ end }}
+{{- if .Prompt }}{{ .Prompt }} {{ end }}
+{{- if .Response }}{{ .Response }} {{ end }}`)
+	if err != nil {
+		t.Fatal(err)
 	}
 
-	for _, tc := range tests {
-		t.Run(tc.name, func(t *testing.T) {
-			tmpl, err := template.Parse(tc.template)
+	for _, tt := range cases {
+		t.Run(tt.name, func(t *testing.T) {
+			model := Model{Template: tmpl, ProjectorPaths: []string{"vision"}}
+			opts := api.Options{Runner: api.Runner{NumCtx: tt.limit}}
+			prompt, images, err := chatPrompt(context.TODO(), &model, tokenize, &opts, tt.msgs)
 			if err != nil {
 				t.Fatal(err)
 			}
 
-			got, err := ChatPrompt(tmpl, tc.messages, tc.window, encode)
-			if err != nil {
-				t.Errorf("error = %v", err)
+			if tt.prompt != prompt {
+				t.Errorf("expected %q, got %q", tt.prompt, prompt)
 			}
 
-			if got != tc.want {
-				t.Errorf("got: %q, want: %q", got, tc.want)
+			if len(images) != len(tt.images) {
+				t.Fatalf("expected %d images, got %d", len(tt.images), len(images))
+			}
+
+			for i := range images {
+				if images[i].ID != i {
+					t.Errorf("expected ID %d, got %d", i, images[i].ID)
+				}
+
+				if !bytes.Equal(images[i].Data, tt.images[i]) {
+					t.Errorf("expected %q, got %q", tt.images[i], images[i])
+				}
 			}
 		})
 	}

server/routes.go

@@ -1,13 +1,13 @@
 package server
 
 import (
+	"bytes"
 	"cmp"
 	"context"
 	"encoding/json"
 	"errors"
 	"fmt"
 	"io"
-	"io/fs"
 	"log/slog"
 	"net"
 	"net/http"
@@ -54,6 +54,8 @@ func init() {
 	gin.SetMode(mode)
 }
 
+var errRequired = errors.New("is required")
+
 func modelOptions(model *Model, requestOpts map[string]interface{}) (api.Options, error) {
 	opts := api.DefaultOptions()
 	if err := opts.FromMap(model.Options); err != nil {
@@ -67,163 +69,140 @@ func modelOptions(model *Model, requestOpts map[string]interface{}) (api.Options
 	return opts, nil
 }
 
-func isSupportedImageType(image []byte) bool {
-	contentType := http.DetectContentType(image)
-	allowedTypes := []string{"image/jpeg", "image/jpg", "image/png"}
-	return slices.Contains(allowedTypes, contentType)
+// 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) {
+	if name == "" {
+		return nil, nil, nil, fmt.Errorf("model %w", errRequired)
+	}
+
+	model, err := GetModel(name)
+	if err != nil {
+		return nil, nil, nil, err
+	}
+
+	if err := model.CheckCapabilities(caps...); err != nil {
+		return nil, nil, nil, fmt.Errorf("%s %w", name, err)
+	}
+
+	opts, err := modelOptions(model, requestOpts)
+	if err != nil {
+		return nil, nil, nil, err
+	}
+
+	runnerCh, errCh := s.sched.GetRunner(ctx, model, opts, keepAlive)
+	var runner *runnerRef
+	select {
+	case runner = <-runnerCh:
+	case err = <-errCh:
+		return nil, nil, nil, err
+	}
+
+	return runner.llama, model, &opts, nil
 }
 
 func (s *Server) GenerateHandler(c *gin.Context) {
-	checkpointStart := time.Now()
 	var req api.GenerateRequest
-	err := c.ShouldBindJSON(&req)
-
-	switch {
-	case errors.Is(err, io.EOF):
+	if err := c.ShouldBindJSON(&req); errors.Is(err, io.EOF) {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "missing request body"})
 		return
-	case err != nil:
+	} else if err != nil {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
 		return
 	}
 
-	// validate the request
-	switch {
-	case req.Model == "":
-		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "model is required"})
+	if req.Format != "" && req.Format != "json" {
+		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "format must be empty or \"json\""})
 		return
-	case len(req.Format) > 0 && req.Format != "json":
-		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "format must be json"})
-		return
-	case req.Raw && (req.Template != "" || req.System != "" || len(req.Context) > 0):
+	} else if req.Raw && (req.Template != "" || req.System != "" || len(req.Context) > 0) {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "raw mode does not support template, system, or context"})
 		return
 	}
 
-	for _, img := range req.Images {
-		if !isSupportedImageType(img) {
-			c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "unsupported image format"})
-			return
-		}
-	}
-
-	model, err := GetModel(req.Model)
-	if err != nil {
-		var pErr *fs.PathError
-		if errors.As(err, &pErr) {
-			c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model '%s' not found, try pulling it first", req.Model)})
-			return
-		}
-		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+	caps := []Capability{CapabilityCompletion}
+	r, m, opts, err := s.scheduleRunner(c.Request.Context(), req.Model, caps, req.Options, req.KeepAlive)
+	if errors.Is(err, errCapabilityCompletion) {
+		c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("%q does not support generate", req.Model)})
+		return
+	} else if err != nil {
+		handleScheduleError(c, req.Model, err)
 		return
 	}
 
-	if !model.Has(CapabilityCompletion) {
-		c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("%s does not support generate", req.Model)})
-		return
-	}
-
-	opts, err := modelOptions(model, req.Options)
-	if err != nil {
-		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-		return
-	}
-
-	rCh, eCh := s.sched.GetRunner(c.Request.Context(), model, opts, req.KeepAlive)
-	var runner *runnerRef
-	select {
-	case runner = <-rCh:
-	case err = <-eCh:
-		handleErrorResponse(c, err)
-		return
-	}
-
-	// an empty request loads the model
-	// note: for a short while template was used in lieu
-	// of `raw` mode so we need to check for it too
-	if req.Prompt == "" && req.Template == "" && req.System == "" {
+	if req.Prompt == "" {
 		c.JSON(http.StatusOK, api.GenerateResponse{
-			CreatedAt:  time.Now().UTC(),
 			Model:      req.Model,
+			CreatedAt:  time.Now().UTC(),
 			Done:       true,
 			DoneReason: "load",
 		})
 		return
 	}
 
-	tmpl, err := template.Parse(req.Template)
-	if err != nil {
-		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-		return
+	images := make([]llm.ImageData, len(req.Images))
+	for i := range req.Images {
+		images[i] = llm.ImageData{ID: i, Data: req.Images[i]}
 	}
 
-	checkpointLoaded := time.Now()
-
-	var prompt string
-	switch {
-	case req.Raw:
-		prompt = req.Prompt
-	case req.Prompt != "":
-		if req.Template == "" {
-			tmpl = model.Template
+	prompt := req.Prompt
+	if !req.Raw {
+		var msgs []api.Message
+		if req.System != "" {
+			msgs = append(msgs, api.Message{Role: "system", Content: req.System})
+		} else if m.System != "" {
+			msgs = append(msgs, api.Message{Role: "system", Content: m.System})
 		}
 
-		if req.System == "" {
-			req.System = model.System
+		for _, i := range images {
+			msgs = append(msgs, api.Message{Role: "user", Content: fmt.Sprintf("[img-%d]", i.ID)})
 		}
 
-		slog.Debug("generate handler", "prompt", req.Prompt)
-		slog.Debug("generate handler", "template", req.Template)
-		slog.Debug("generate handler", "system", req.System)
+		msgs = append(msgs, api.Message{Role: "user", Content: req.Prompt})
 
-		var sb strings.Builder
-		for i := range req.Images {
-			fmt.Fprintf(&sb, "[img-%d] ", i)
+		tmpl := m.Template
+		if req.Template != "" {
+			tmpl, err = template.Parse(req.Template)
+			if err != nil {
+				c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+				return
+			}
 		}
 
-		sb.WriteString(req.Prompt)
-
-		p, err := Prompt(tmpl, req.System, sb.String(), "", true)
-		if err != nil {
-			c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-			return
-		}
-
-		sb.Reset()
+		var b bytes.Buffer
 		if req.Context != nil {
-			prev, err := runner.llama.Detokenize(c.Request.Context(), req.Context)
+			s, err := r.Detokenize(c.Request.Context(), req.Context)
 			if err != nil {
 				c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 				return
 			}
 
-			sb.WriteString(prev)
+			b.WriteString(s)
 		}
 
-		sb.WriteString(p)
+		if err := tmpl.Execute(&b, template.Values{Messages: msgs}); err != nil {
+			c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+			return
+		}
 
-		prompt = sb.String()
+		prompt = b.String()
 	}
 
-	slog.Debug("generate handler", "prompt", prompt)
+	slog.Debug("generate request", "prompt", prompt, "images", images)
 
 	ch := make(chan any)
-	var generated strings.Builder
 	go func() {
 		defer close(ch)
-
-		fn := func(r llm.CompletionResponse) {
-			// Build up the full response
-			if _, err := generated.WriteString(r.Content); err != nil {
-				ch <- gin.H{"error": err.Error()}
-				return
-			}
-
-			resp := api.GenerateResponse{
+		if err := r.Completion(c.Request.Context(), llm.CompletionRequest{
+			Prompt:  prompt,
+			Images:  images,
+			Format:  req.Format,
+			Options: opts,
+		}, func(r llm.CompletionResponse) {
+			ch <- api.GenerateResponse{
 				Model:      req.Model,
 				CreatedAt:  time.Now().UTC(),
-				Done:       r.Done,
 				Response:   r.Content,
+				Done:       r.Done,
 				DoneReason: r.DoneReason,
 				Metrics: api.Metrics{
 					PromptEvalCount:    r.PromptEvalCount,
@@ -232,77 +211,35 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 					EvalDuration:       r.EvalDuration,
 				},
 			}
-
-			if r.Done {
-				resp.TotalDuration = time.Since(checkpointStart)
-				resp.LoadDuration = checkpointLoaded.Sub(checkpointStart)
-
-				if !req.Raw {
-					p, err := Prompt(tmpl, req.System, req.Prompt, generated.String(), false)
-					if err != nil {
-						c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-						return
-					}
-
-					// TODO (jmorganca): encode() should not strip special tokens
-					tokens, err := runner.llama.Tokenize(c.Request.Context(), p)
-					if err != nil {
-						ch <- gin.H{"error": err.Error()}
-						return
-					}
-
-					resp.Context = append(req.Context, tokens...)
-				}
-			}
-
-			ch <- resp
-		}
-
-		var images []llm.ImageData
-		for i := range req.Images {
-			images = append(images, llm.ImageData{
-				ID:   i,
-				Data: req.Images[i],
-			})
-		}
-
-		// Start prediction
-		req := llm.CompletionRequest{
-			Prompt:  prompt,
-			Format:  req.Format,
-			Images:  images,
-			Options: opts,
-		}
-		if err := runner.llama.Completion(c.Request.Context(), req, fn); err != nil {
+		}); err != nil {
 			ch <- gin.H{"error": err.Error()}
 		}
 	}()
 
 	if req.Stream != nil && !*req.Stream {
-		// Accumulate responses into the final response
-		var final api.GenerateResponse
+		var r api.GenerateResponse
 		var sb strings.Builder
-		for resp := range ch {
-			switch r := resp.(type) {
+		for rr := range ch {
+			switch t := rr.(type) {
 			case api.GenerateResponse:
-				sb.WriteString(r.Response)
-				final = r
+				sb.WriteString(t.Response)
+				r = t
 			case gin.H:
-				if errorMsg, ok := r["error"].(string); ok {
-					c.JSON(http.StatusInternalServerError, gin.H{"error": errorMsg})
-					return
-				} else {
-					c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected error format in response"})
-					return
+				msg, ok := t["error"].(string)
+				if !ok {
+					msg = "unexpected error format in response"
 				}
+
+				c.JSON(http.StatusInternalServerError, gin.H{"error": msg})
+				return
 			default:
-				c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected error"})
+				c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected response"})
 				return
 			}
 		}
 
-		final.Response = sb.String()
-		c.JSON(http.StatusOK, final)
+		r.Response = sb.String()
+		c.JSON(http.StatusOK, r)
 		return
 	}
 
@@ -311,44 +248,17 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 
 func (s *Server) EmbeddingsHandler(c *gin.Context) {
 	var req api.EmbeddingRequest
-	err := c.ShouldBindJSON(&req)
-	switch {
-	case errors.Is(err, io.EOF):
+	if err := c.ShouldBindJSON(&req); errors.Is(err, io.EOF) {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "missing request body"})
 		return
-	case err != nil:
+	} else if err != nil {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
 		return
 	}
 
-	if req.Model == "" {
-		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "model is required"})
-		return
-	}
-
-	model, err := GetModel(req.Model)
+	r, _, _, err := s.scheduleRunner(c.Request.Context(), req.Model, []Capability{}, req.Options, req.KeepAlive)
 	if err != nil {
-		var pErr *fs.PathError
-		if errors.As(err, &pErr) {
-			c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model '%s' not found, try pulling it first", req.Model)})
-			return
-		}
-		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-		return
-	}
-
-	opts, err := modelOptions(model, req.Options)
-	if err != nil {
-		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-		return
-	}
-
-	rCh, eCh := s.sched.GetRunner(c.Request.Context(), model, opts, req.KeepAlive)
-	var runner *runnerRef
-	select {
-	case runner = <-rCh:
-	case err = <-eCh:
-		handleErrorResponse(c, err)
+		handleScheduleError(c, req.Model, err)
 		return
 	}
 
@@ -358,17 +268,14 @@ func (s *Server) EmbeddingsHandler(c *gin.Context) {
 		return
 	}
 
-	embedding, err := runner.llama.Embedding(c.Request.Context(), req.Prompt)
+	embedding, err := r.Embedding(c.Request.Context(), req.Prompt)
 	if err != nil {
 		slog.Info(fmt.Sprintf("embedding generation failed: %v", err))
 		c.JSON(http.StatusInternalServerError, gin.H{"error": "failed to generate embedding"})
 		return
 	}
 
-	resp := api.EmbeddingResponse{
-		Embedding: embedding,
-	}
-	c.JSON(http.StatusOK, resp)
+	c.JSON(http.StatusOK, api.EmbeddingResponse{Embedding: embedding})
 }
 
 func (s *Server) PullModelHandler(c *gin.Context) {
@@ -649,9 +556,9 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 		}
 	}
 
-	msgs := make([]api.Message, 0)
-	for _, msg := range m.Messages {
-		msgs = append(msgs, api.Message{Role: msg.Role, Content: msg.Content})
+	msgs := make([]api.Message, len(m.Messages))
+	for i, msg := range m.Messages {
+		msgs[i] = api.Message{Role: msg.Role, Content: msg.Content}
 	}
 
 	n := model.ParseName(req.Model)
@@ -1214,132 +1121,55 @@ func (s *Server) ProcessHandler(c *gin.Context) {
 	c.JSON(http.StatusOK, api.ProcessResponse{Models: models})
 }
 
-// ChatPrompt builds up a prompt from a series of messages for the currently `loaded` model
-func chatPrompt(ctx context.Context, runner *runnerRef, template *template.Template, messages []api.Message, numCtx int) (string, error) {
-	encode := func(s string) ([]int, error) {
-		return runner.llama.Tokenize(ctx, s)
-	}
-
-	prompt, err := ChatPrompt(template, messages, numCtx, encode)
-	if err != nil {
-		return "", err
-	}
-
-	return prompt, nil
-}
-
 func (s *Server) ChatHandler(c *gin.Context) {
-	checkpointStart := time.Now()
-
 	var req api.ChatRequest
-	err := c.ShouldBindJSON(&req)
-	switch {
-	case errors.Is(err, io.EOF):
+	if err := c.ShouldBindJSON(&req); errors.Is(err, io.EOF) {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "missing request body"})
 		return
-	case err != nil:
+	} else if err != nil {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
 		return
 	}
 
-	// validate the request
-	switch {
-	case req.Model == "":
-		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "model is required"})
+	caps := []Capability{CapabilityCompletion}
+	r, m, opts, err := s.scheduleRunner(c.Request.Context(), req.Model, caps, req.Options, req.KeepAlive)
+	if errors.Is(err, errCapabilityCompletion) {
+		c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("%q does not support chat", req.Model)})
 		return
-	case len(req.Format) > 0 && req.Format != "json":
-		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "format must be json"})
+	} else if err != nil {
+		handleScheduleError(c, req.Model, err)
 		return
 	}
 
-	model, err := GetModel(req.Model)
-	if err != nil {
-		var pErr *fs.PathError
-		if errors.As(err, &pErr) {
-			c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model '%s' not found, try pulling it first", req.Model)})
-			return
-		}
-		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-		return
-	}
-
-	if !model.Has(CapabilityCompletion) {
-		c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("%s does not support chat", req.Model)})
-		return
-	}
-
-	opts, err := modelOptions(model, req.Options)
-	if err != nil {
-		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-		return
-	}
-
-	rCh, eCh := s.sched.GetRunner(c.Request.Context(), model, opts, req.KeepAlive)
-	var runner *runnerRef
-	select {
-	case runner = <-rCh:
-	case err = <-eCh:
-		handleErrorResponse(c, err)
-		return
-	}
-
-	checkpointLoaded := time.Now()
-
-	// if the first message is not a system message, then add the model's default system message
-	if len(req.Messages) > 0 && req.Messages[0].Role != "system" {
-		req.Messages = append([]api.Message{
-			{
-				Role:    "system",
-				Content: model.System,
-			},
-		}, req.Messages...)
-	}
-
-	prompt, err := chatPrompt(c.Request.Context(), runner, model.Template, req.Messages, opts.NumCtx)
-	if err != nil {
-		c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
-		return
-	}
-
-	// an empty request loads the model
-	if len(req.Messages) == 0 || prompt == "" {
-		resp := api.ChatResponse{
-			CreatedAt:  time.Now().UTC(),
+	if len(req.Messages) == 0 {
+		c.JSON(http.StatusOK, api.ChatResponse{
 			Model:      req.Model,
+			CreatedAt:  time.Now().UTC(),
+			Message:    api.Message{Role: "assistant"},
 			Done:       true,
 			DoneReason: "load",
-			Message:    api.Message{Role: "assistant"},
-		}
-		c.JSON(http.StatusOK, resp)
+		})
 		return
 	}
 
-	// only send images that are in the prompt
-	var i int
-	var images []llm.ImageData
-	for _, m := range req.Messages {
-		for _, img := range m.Images {
-			if !isSupportedImageType(img) {
-				c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "unsupported image format"})
-				return
-			}
-
-			if strings.Contains(prompt, fmt.Sprintf("[img-%d]", i)) {
-				images = append(images, llm.ImageData{Data: img, ID: i})
-			}
-			i += 1
-		}
+	prompt, images, err := chatPrompt(c.Request.Context(), m, r.Tokenize, opts, req.Messages)
+	if err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		return
 	}
 
-	slog.Debug("chat handler", "prompt", prompt, "images", len(images))
+	slog.Debug("chat request", "images", len(images), "prompt", prompt)
 
 	ch := make(chan any)
-
 	go func() {
 		defer close(ch)
-
-		fn := func(r llm.CompletionResponse) {
-			resp := api.ChatResponse{
+		if err := r.Completion(c.Request.Context(), llm.CompletionRequest{
+			Prompt:  prompt,
+			Images:  images,
+			Format:  req.Format,
+			Options: opts,
+		}, func(r llm.CompletionResponse) {
+			ch <- api.ChatResponse{
 				Model:      req.Model,
 				CreatedAt:  time.Now().UTC(),
 				Message:    api.Message{Role: "assistant", Content: r.Content},
@@ -1352,64 +1182,52 @@ func (s *Server) ChatHandler(c *gin.Context) {
 					EvalDuration:       r.EvalDuration,
 				},
 			}
-
-			if r.Done {
-				resp.TotalDuration = time.Since(checkpointStart)
-				resp.LoadDuration = checkpointLoaded.Sub(checkpointStart)
-			}
-
-			ch <- resp
-		}
-
-		if err := runner.llama.Completion(c.Request.Context(), llm.CompletionRequest{
-			Prompt:  prompt,
-			Format:  req.Format,
-			Images:  images,
-			Options: opts,
-		}, fn); err != nil {
+		}); err != nil {
 			ch <- gin.H{"error": err.Error()}
 		}
 	}()
 
 	if req.Stream != nil && !*req.Stream {
-		// Accumulate responses into the final response
-		var final api.ChatResponse
+		var r api.ChatResponse
 		var sb strings.Builder
-		for resp := range ch {
-			switch r := resp.(type) {
+		for rr := range ch {
+			switch t := rr.(type) {
 			case api.ChatResponse:
-				sb.WriteString(r.Message.Content)
-				final = r
+				sb.WriteString(t.Message.Content)
+				r = t
 			case gin.H:
-				if errorMsg, ok := r["error"].(string); ok {
-					c.JSON(http.StatusInternalServerError, gin.H{"error": errorMsg})
-					return
-				} else {
-					c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected error format in response"})
-					return
+				msg, ok := t["error"].(string)
+				if !ok {
+					msg = "unexpected error format in response"
 				}
+
+				c.JSON(http.StatusInternalServerError, gin.H{"error": msg})
+				return
 			default:
-				c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected error"})
+				c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected response"})
 				return
 			}
 		}
 
-		final.Message = api.Message{Role: "assistant", Content: sb.String()}
-		c.JSON(http.StatusOK, final)
+		r.Message.Content = sb.String()
+		c.JSON(http.StatusOK, r)
 		return
 	}
 
 	streamResponse(c, ch)
 }
 
-func handleErrorResponse(c *gin.Context, err error) {
-	if errors.Is(err, context.Canceled) {
+func handleScheduleError(c *gin.Context, name string, err error) {
+	switch {
+	case errors.Is(err, errRequired):
+		c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
+	case errors.Is(err, context.Canceled):
 		c.JSON(499, gin.H{"error": "request canceled"})
-		return
-	}
-	if errors.Is(err, ErrMaxQueue) {
+	case errors.Is(err, ErrMaxQueue):
 		c.JSON(http.StatusServiceUnavailable, gin.H{"error": err.Error()})
-		return
+	case errors.Is(err, os.ErrNotExist):
+		c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model %q not found, try pulling it first", name)})
+	default:
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 	}
-	c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 }

server/routes_create_test.go

@@ -2,6 +2,7 @@ package server
 
 import (
 	"bytes"
+	"encoding/binary"
 	"encoding/json"
 	"fmt"
 	"io"
@@ -20,7 +21,7 @@ import (
 
 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 []llm.Tensor) string {
 	t.Helper()
 
 	f, err := os.CreateTemp(t.TempDir(), "")
@@ -29,7 +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 := llm.NewGGUFV3(binary.LittleEndian).Encode(f, kv, ti); err != nil {
 		t.Fatal(err)
 	}
 
@@ -544,9 +545,9 @@ func TestCreateDetectTemplate(t *testing.T) {
 		}
 
 		checkFileExists(t, filepath.Join(p, "blobs", "*"), []string{
-			filepath.Join(p, "blobs", "sha256-2f8e594e6f34b1b4d36a246628eeb3365ce442303d656f1fcc69e821722acea0"),
-			filepath.Join(p, "blobs", "sha256-542b217f179c7825eeb5bca3c77d2b75ed05bafbd3451d9188891a60a85337c6"),
 			filepath.Join(p, "blobs", "sha256-553c4a3f747b3d22a4946875f1cc8ed011c2930d83f864a0c7265f9ec0a20413"),
+			filepath.Join(p, "blobs", "sha256-c608dc615584cd20d9d830363dabf8a4783ae5d34245c3d8c115edb3bc7b28e4"),
+			filepath.Join(p, "blobs", "sha256-f836ee110db21567f826332e4cedd746c06d10664fd5a9ea3659e3683a944510"),
 		})
 	})
 

server/sched.go

@@ -133,17 +133,8 @@ func (s *Scheduler) processPending(ctx context.Context) {
 				numParallel = 1
 				slog.Warn("multimodal models don't support parallel requests yet")
 			}
-			// Keep NumCtx and numParallel in sync
-			if numParallel > 1 {
-				pending.opts.NumCtx = pending.origNumCtx * numParallel
-			}
 
 			for {
-				cpus := s.getCpuFn()
-				var systemMem gpu.GpuInfo
-				if len(cpus) > 0 {
-					systemMem = cpus[0]
-				}
 				var runnerToExpire *runnerRef
 				s.loadedMu.Lock()
 				runner := s.loaded[pending.model.ModelPath]
@@ -197,46 +188,15 @@ func (s *Scheduler) processPending(ctx context.Context) {
 						break
 					}
 
-					estimate := llm.EstimateGPULayers(gpus, ggml, pending.model.ProjectorPaths, pending.opts)
-					maxSize := systemMem.FreeMemory
-
-					// Add available GPU memory to the total pool
-					// macOS hardware has unified memory so don't double count
-					if runtime.GOOS != "darwin" {
-						for _, gpu := range gpus {
-							if gpu.Library == "cpu" {
-								continue
-							}
-							if loadedCount == 0 {
-								// If no other models are loaded, set the limit based on what's available
-								maxSize += gpu.FreeMemory
-							} else {
-								// Other models could be unloaded, favor total memory for limit
-								maxSize += gpu.TotalMemory
-							}
-						}
-					}
-
-					// Block attempting to load a model larger than system memory + GPU memory
-					if estimate.TotalSize > maxSize {
-						slog.Warn("model request too large for system", "requested", format.HumanBytes2(estimate.TotalSize), "system", format.HumanBytes2(maxSize))
-
-						// Linux will crash if over-allocating memory - return an error to the user.
-						// TODO (jmorganca): add reasonable upper limits for darwin and windows as well
-						if runtime.GOOS == "linux" {
-							pending.errCh <- fmt.Errorf("requested model (%s) is too large for this system (%s)", format.HumanBytes2(estimate.TotalSize), format.HumanBytes2(maxSize))
-							break
-						}
-					}
-
 					// Evaluate if the model will fit in the available system memory, or if we should unload a model first
 					if len(gpus) == 1 && gpus[0].Library == "cpu" {
 						// simplifying assumption of defaultParallel when in CPU mode
 						if numParallel <= 0 {
 							numParallel = defaultParallel
-							pending.opts.NumCtx = pending.origNumCtx * numParallel
 						}
 
+						pending.opts.NumCtx = pending.origNumCtx * numParallel
+
 						if loadedCount == 0 {
 							slog.Debug("cpu mode with first model, loading")
 							s.loadFn(pending, ggml, gpus, numParallel)

server/sched_test.go

@@ -3,6 +3,7 @@ package server
 import (
 	"bytes"
 	"context"
+	"encoding/binary"
 	"fmt"
 	"log/slog"
 	"os"
@@ -114,7 +115,8 @@ func newScenario(t *testing.T, ctx context.Context, modelName string, estimatedV
 	require.NoError(t, err)
 	defer f.Close()
 
-	require.NoError(t, llm.WriteGGUF(f, llm.KV{
+	gguf := llm.NewGGUFV3(binary.LittleEndian)
+	err = gguf.Encode(f, llm.KV{
 		"general.architecture":          "llama",
 		"general.name":                  "name",
 		"llama.context_length":          uint32(32),
@@ -125,10 +127,10 @@ func newScenario(t *testing.T, ctx context.Context, modelName string, estimatedV
 		"tokenizer.ggml.tokens":         []string{" "},
 		"tokenizer.ggml.scores":         []float32{0},
 		"tokenizer.ggml.token_type":     []int32{0},
-	}, []*llm.Tensor{
+	}, []llm.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))},
-	}))
+	})
 	require.NoError(t, err)
 
 	fname := f.Name()

template/alpaca.gotmpl

@@ -4,4 +4,5 @@
 {{ .Prompt }}
 
 {{ end }}### Response:
-{{ .Response }}
+{{ .Response }}
+

template/chatml.gotmpl

@@ -3,4 +3,4 @@
 {{ end }}{{ if .Prompt }}<|im_start|>user
 {{ .Prompt }}<|im_end|>
 {{ end }}<|im_start|>assistant
-{{ .Response }}<|im_end|>
+{{ .Response }}<|im_end|>

template/chatqa.gotmpl

@@ -2,4 +2,5 @@
 
 {{ end }}{{ if .Prompt }}User: {{ .Prompt }}
 
-{{ end }}Assistant: <|begin_of_text|>{{ .Response }}
+{{ end }}Assistant: {{ .Response }}
+

template/codellama-70b-instruct.gotmpl

@@ -1,8 +1,10 @@
-{{ if .System }} Source: system
+{{ if .System }}Source: system
 
- {{ .System }} <step>{{ end }} Source: user
+ {{ .System }} <step> {{ end }}Source: user
 
  {{ .Prompt }} <step> Source: assistant
+{{- if not .Response }}
 Destination: user
+{{- end }}
 
- {{ .Response }}<step>
+ {{ .Response }} <step> 

template/falcon-instruct.gotmpl

@@ -1,3 +1,5 @@
-{{ if .System }}{{ .System }}
-{{ end }}{{ if .Prompt }}User: {{ .Prompt }}
-{{ end }}Assistant: {{ .Response }}
+{{ if .System }}System: {{ .System }}
+{{ end }}{{ if .Prompt }}User:
+{{ .Prompt }}
+{{ end }}Falcon:
+{{ .Response }}

template/gemma-instruct.gotmpl

@@ -1,4 +1,5 @@
 <start_of_turn>user
-{{ if .System }}{{ .System }} {{ end }}{{ .Prompt }}<end_of_turn>
+{{ if .System }}{{ .System }}
+{{ end }}{{ .Prompt }}<end_of_turn>
 <start_of_turn>model
-{{ .Response }}<end_of_turn>
+{{ .Response }}<end_of_turn>

template/granite-instruct.gotmpl

@@ -1,9 +1,9 @@
-{{ if .System }}
-System:
+{{ if .System }}System:
 {{ .System }}
 
 {{ end }}{{ if .Prompt }}Question:
 {{ .Prompt }}
 
 {{ end }}Answer:
-{{ .Response }}
+{{ .Response }}
+

template/llama2-chat.gotmpl

@@ -1,3 +1,6 @@
-[INST] <<SYS>>{{ .System }}<</SYS>>
+[INST] <<SYS>>
+{{- if .System }}
+{{ .System }}
+{{ end }}<</SYS>>
 
-{{ .Prompt }} [/INST] {{ .Response }}
+{{ .Prompt }} [/INST] {{ .Response }}</s><s>

template/magicoder.gotmpl

@@ -4,4 +4,5 @@
 {{ .Prompt }}
 
 {{ end }}@@ Response
-{{ .Response }}
+{{ .Response }}
+

template/mistral-instruct.gotmpl

@@ -1,6 +1,3 @@
-{{ if .System }}<|im_start|>system
-{{ .System }}<|im_end|>
-{{ end }}{{ if .Prompt }}<|im_start|>user
-{{ .Prompt }}<|im_end|>
-{{ end }}<|im_start|>assistant
-{{ .Response }}<|im_end|>
+[INST] {{ if .System }}{{ .System }}
+
+{{ end }}{{ .Prompt }}[/INST] {{ .Response }}</s>

template/openchat.gotmpl

@@ -1 +1 @@
-{{ .System }}<|end_of_turn|>GPT4 Correct User: {{ .Prompt }}<|end_of_turn|>GPT4 Correct Assistant: {{ .Response }}<|end_of_turn|>
+{{ if .System }}GPT4 Correct System: {{ .System }}<|end_of_turn|>{{ end }}GPT4 Correct User: {{ .Prompt }}<|end_of_turn|>GPT4 Correct Assistant: {{ .Response }}<|end_of_turn|>

template/phi-3.gotmpl

@@ -3,4 +3,4 @@
 {{ end }}{{ if .Prompt }}<|user|>
 {{ .Prompt }}<|end|>
 {{ end }}<|assistant|>
-{{ .Response }}<|end|>
+{{ .Response }}<|end|>

template/solar-instruct.gotmpl

@@ -5,4 +5,5 @@
 {{ .Prompt }}
 
 {{ end }}### Assistant:
-{{ .Response }}
+{{ .Response }}</s>
+

template/starcoder2-instruct.gotmpl

@@ -3,7 +3,6 @@
 {{ end }}{{ if .Prompt }}### Instruction
 {{ .Prompt }}
 
-
 {{ end }}### Response
 {{ .Response }}<|endoftext|>
 

template/template.go

@@ -5,6 +5,7 @@ import (
 	"embed"
 	"encoding/json"
 	"errors"
+	"fmt"
 	"io"
 	"math"
 	"slices"
@@ -14,6 +15,7 @@ import (
 	"text/template/parse"
 
 	"github.com/agnivade/levenshtein"
+	"github.com/ollama/ollama/api"
 	"golang.org/x/exp/maps"
 )
 
@@ -74,30 +76,59 @@ func Named(s string) (*named, error) {
 	return nil, errors.New("no matching template found")
 }
 
+var DefaultTemplate, _ = Parse("{{ .Prompt }}")
+
 type Template struct {
 	*template.Template
 	raw string
 }
 
+// response is a template node that can be added to templates that don't already have one
+var response = parse.ActionNode{
+	NodeType: parse.NodeAction,
+	Pipe: &parse.PipeNode{
+		NodeType: parse.NodePipe,
+		Cmds: []*parse.CommandNode{
+			{
+				NodeType: parse.NodeCommand,
+				Args: []parse.Node{
+					&parse.FieldNode{
+						NodeType: parse.NodeField,
+						Ident:    []string{"Response"},
+					},
+				},
+			},
+		},
+	},
+}
+
+func Parse(s string) (*Template, error) {
+	tmpl := template.New("").Option("missingkey=zero")
+
+	tmpl, err := tmpl.Parse(s)
+	if err != nil {
+		return nil, err
+	}
+
+	t := Template{Template: tmpl, raw: s}
+	if vars := t.Vars(); !slices.Contains(vars, "messages") && !slices.Contains(vars, "response") {
+		// touch up the template and append {{ .Response }}
+		tmpl.Tree.Root.Nodes = append(tmpl.Tree.Root.Nodes, &response)
+	}
+
+	return &t, nil
+}
+
 func (t *Template) String() string {
 	return t.raw
 }
 
-var DefaultTemplate, _ = Parse("{{ .Prompt }}")
-
-func Parse(s string) (*Template, error) {
-	t, err := template.New("").Option("missingkey=zero").Parse(s)
-	if err != nil {
-		return nil, err
-	}
-
-	return &Template{Template: t, raw: s}, nil
-}
-
 func (t *Template) Vars() []string {
 	var vars []string
-	for _, n := range t.Tree.Root.Nodes {
-		vars = append(vars, parseNode(n)...)
+	for _, tt := range t.Templates() {
+		for _, n := range tt.Root.Nodes {
+			vars = append(vars, parseNode(n)...)
+		}
 	}
 
 	set := make(map[string]struct{})
@@ -110,6 +141,108 @@ func (t *Template) Vars() []string {
 	return vars
 }
 
+type Values struct {
+	Messages []api.Message
+
+	// forceLegacy is a flag used to test compatibility with legacy templates
+	forceLegacy bool
+}
+
+func (t *Template) Execute(w io.Writer, v Values) error {
+	system, collated := collate(v.Messages)
+	if !v.forceLegacy && slices.Contains(t.Vars(), "messages") {
+		return t.Template.Execute(w, map[string]any{
+			"System":   system,
+			"Messages": collated,
+		})
+	}
+
+	var b bytes.Buffer
+	var prompt, response string
+	for i, m := range collated {
+		switch m.Role {
+		case "system":
+			system = m.Content
+		case "user":
+			prompt = m.Content
+		case "assistant":
+			response = m.Content
+		}
+
+		if i != len(collated)-1 && prompt != "" && response != "" {
+			if err := t.Template.Execute(&b, map[string]any{
+				"System":   system,
+				"Prompt":   prompt,
+				"Response": response,
+			}); err != nil {
+				return err
+			}
+
+			system = ""
+			prompt = ""
+			response = ""
+		}
+	}
+
+	var cut bool
+	nodes := deleteNode(t.Template.Root.Copy(), func(n parse.Node) bool {
+		switch t := n.(type) {
+		case *parse.ActionNode:
+		case *parse.FieldNode:
+			if slices.Contains(t.Ident, "Response") {
+				cut = true
+			}
+		}
+
+		return cut
+	})
+
+	tree := parse.Tree{Root: nodes.(*parse.ListNode)}
+	if err := template.Must(template.New("").AddParseTree("", &tree)).Execute(&b, map[string]any{
+		"System": "",
+		"Prompt": prompt,
+	}); err != nil {
+		return err
+	}
+
+	_, err := io.Copy(w, &b)
+	return err
+}
+
+// collate messages based on role. consecutive messages of the same role are merged
+// into a single message. collate also collects and returns all system messages.
+// collate mutates message content adding image tags ([img-%d]) as needed
+func collate(msgs []api.Message) (string, []*api.Message) {
+	var n int
+
+	var system []string
+	var collated []*api.Message
+	for i := range msgs {
+		msg := msgs[i]
+		for range msg.Images {
+			imageTag := fmt.Sprintf("[img-%d]", n)
+			if !strings.Contains(msg.Content, "[img]") {
+				msg.Content = strings.TrimSpace("[img] " + msg.Content)
+			}
+
+			msg.Content = strings.Replace(msg.Content, "[img]", imageTag, 1)
+			n++
+		}
+
+		if msg.Role == "system" {
+			system = append(system, msg.Content)
+		}
+
+		if len(collated) > 0 && collated[len(collated)-1].Role == msg.Role {
+			collated[len(collated)-1].Content += "\n\n" + msg.Content
+		} else {
+			collated = append(collated, &msg)
+		}
+	}
+
+	return strings.Join(system, "\n\n"), collated
+}
+
 func parseNode(n parse.Node) []string {
 	switch n := n.(type) {
 	case *parse.ActionNode:
@@ -152,7 +285,78 @@ func parseNode(n parse.Node) []string {
 		return names
 	case *parse.FieldNode:
 		return n.Ident
+	case *parse.TemplateNode:
+		return parseNode(n.Pipe)
 	}
 
 	return nil
 }
+
+// deleteNode walks the node list and deletes nodes that match the predicate
+// this is currently to remove the {{ .Response }} node from templates
+func deleteNode(n parse.Node, fn func(parse.Node) bool) parse.Node {
+	var walk func(n parse.Node) parse.Node
+	walk = func(n parse.Node) parse.Node {
+		if fn(n) {
+			return nil
+		}
+
+		switch t := n.(type) {
+		case *parse.ListNode:
+			var nodes []parse.Node
+			for _, c := range t.Nodes {
+				if n := walk(c); n != nil {
+					nodes = append(nodes, n)
+				}
+			}
+
+			t.Nodes = nodes
+			return t
+		case *parse.IfNode:
+			t.BranchNode = *(walk(&t.BranchNode).(*parse.BranchNode))
+		case *parse.WithNode:
+			t.BranchNode = *(walk(&t.BranchNode).(*parse.BranchNode))
+		case *parse.RangeNode:
+			t.BranchNode = *(walk(&t.BranchNode).(*parse.BranchNode))
+		case *parse.BranchNode:
+			t.List = walk(t.List).(*parse.ListNode)
+			if t.ElseList != nil {
+				t.ElseList = walk(t.ElseList).(*parse.ListNode)
+			}
+		case *parse.ActionNode:
+			n := walk(t.Pipe)
+			if n == nil {
+				return nil
+			}
+
+			t.Pipe = n.(*parse.PipeNode)
+		case *parse.PipeNode:
+			var commands []*parse.CommandNode
+			for _, c := range t.Cmds {
+				var args []parse.Node
+				for _, a := range c.Args {
+					if n := walk(a); n != nil {
+						args = append(args, n)
+					}
+				}
+
+				if len(args) == 0 {
+					return nil
+				}
+
+				c.Args = args
+				commands = append(commands, c)
+			}
+
+			if len(commands) == 0 {
+				return nil
+			}
+
+			t.Cmds = commands
+		}
+
+		return n
+	}
+
+	return walk(n)
+}

template/template_test.go

@@ -8,9 +8,11 @@ import (
 	"os"
 	"path/filepath"
 	"slices"
+	"strings"
 	"testing"
-	"text/template"
 
+	"github.com/google/go-cmp/cmp"
+	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/llm"
 )
 
@@ -46,7 +48,7 @@ func TestNamed(t *testing.T) {
 					t.Fatal(err)
 				}
 
-				tmpl, err := template.New(s).Parse(b.String())
+				tmpl, err := Parse(b.String())
 				if err != nil {
 					t.Fatal(err)
 				}
@@ -59,18 +61,125 @@ func TestNamed(t *testing.T) {
 	}
 }
 
+func TestTemplate(t *testing.T) {
+	cases := make(map[string][]api.Message)
+	for _, mm := range [][]api.Message{
+		{
+			{Role: "user", Content: "Hello, how are you?"},
+		},
+		{
+			{Role: "user", Content: "Hello, how are you?"},
+			{Role: "assistant", Content: "I'm doing great. How can I help you today?"},
+			{Role: "user", Content: "I'd like to show off how chat templating works!"},
+		},
+		{
+			{Role: "system", Content: "You are a helpful assistant."},
+			{Role: "user", Content: "Hello, how are you?"},
+			{Role: "assistant", Content: "I'm doing great. How can I help you today?"},
+			{Role: "user", Content: "I'd like to show off how chat templating works!"},
+		},
+	} {
+		var roles []string
+		for _, m := range mm {
+			roles = append(roles, m.Role)
+		}
+
+		cases[strings.Join(roles, "-")] = mm
+	}
+
+	matches, err := filepath.Glob("*.gotmpl")
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	for _, match := range matches {
+		t.Run(match, func(t *testing.T) {
+			bts, err := os.ReadFile(match)
+			if err != nil {
+				t.Fatal(err)
+			}
+
+			tmpl, err := Parse(string(bts))
+			if err != nil {
+				t.Fatal(err)
+			}
+
+			for n, tt := range cases {
+				var actual bytes.Buffer
+				t.Run(n, func(t *testing.T) {
+					if err := tmpl.Execute(&actual, Values{Messages: tt}); err != nil {
+						t.Fatal(err)
+					}
+
+					expect, err := os.ReadFile(filepath.Join("testdata", match, n))
+					if err != nil {
+						t.Fatal(err)
+					}
+
+					bts := actual.Bytes()
+
+					if slices.Contains([]string{"chatqa.gotmpl", "llama2-chat.gotmpl", "mistral-instruct.gotmpl", "openchat.gotmpl", "vicuna.gotmpl"}, match) && bts[len(bts)-1] == ' ' {
+						t.Log("removing trailing space from output")
+						bts = bts[:len(bts)-1]
+					}
+
+					if diff := cmp.Diff(bts, expect); diff != "" {
+						t.Errorf("mismatch (-got +want):\n%s", diff)
+					}
+				})
+
+				t.Run("legacy", func(t *testing.T) {
+					t.Skip("legacy outputs are currently default outputs")
+					var legacy bytes.Buffer
+					if err := tmpl.Execute(&legacy, Values{Messages: tt, forceLegacy: true}); err != nil {
+						t.Fatal(err)
+					}
+
+					legacyBytes := legacy.Bytes()
+					if slices.Contains([]string{"chatqa.gotmpl", "openchat.gotmpl", "vicuna.gotmpl"}, match) && legacyBytes[len(legacyBytes)-1] == ' ' {
+						t.Log("removing trailing space from legacy output")
+						legacyBytes = legacyBytes[:len(legacyBytes)-1]
+					} else if slices.Contains([]string{"codellama-70b-instruct.gotmpl", "llama2-chat.gotmpl", "mistral-instruct.gotmpl"}, match) {
+						t.Skip("legacy outputs cannot be compared to messages outputs")
+					}
+
+					if diff := cmp.Diff(legacyBytes, actual.Bytes()); diff != "" {
+						t.Errorf("mismatch (-got +want):\n%s", diff)
+					}
+				})
+			}
+		})
+	}
+}
+
 func TestParse(t *testing.T) {
 	cases := []struct {
 		template string
 		vars     []string
 	}{
-		{"{{ .Prompt }}", []string{"prompt"}},
-		{"{{ .System }} {{ .Prompt }}", []string{"prompt", "system"}},
+		{"{{ .Prompt }}", []string{"prompt", "response"}},
+		{"{{ .System }} {{ .Prompt }}", []string{"prompt", "response", "system"}},
 		{"{{ .System }} {{ .Prompt }} {{ .Response }}", []string{"prompt", "response", "system"}},
-		{"{{ with .Tools }}{{ . }}{{ end }} {{ .System }} {{ .Prompt }}", []string{"prompt", "system", "tools"}},
+		{"{{ with .Tools }}{{ . }}{{ end }} {{ .System }} {{ .Prompt }}", []string{"prompt", "response", "system", "tools"}},
 		{"{{ range .Messages }}{{ .Role }} {{ .Content }}{{ end }}", []string{"content", "messages", "role"}},
-		{"{{ range .Messages }}{{ if eq .Role \"system\" }}SYSTEM: {{ .Content }}{{ else if eq .Role \"user\" }}USER: {{ .Content }}{{ else if eq .Role \"assistant\" }}ASSISTANT: {{ .Content }}{{ end }}{{ end }}", []string{"content", "messages", "role"}},
-		{"{{ .Prompt }} {{ .Suffix }}", []string{"prompt", "suffix"}},
+		{`{{- range .Messages }}
+{{- if eq .Role "system" }}SYSTEM:
+{{- else if eq .Role "user" }}USER:
+{{- else if eq .Role "assistant" }}ASSISTANT:
+{{- end }} {{ .Content }}
+{{- end }}`, []string{"content", "messages", "role"}},
+		{`{{- if .Messages }}
+{{- range .Messages }}<|im_start|>{{ .Role }}
+{{ .Content }}<|im_end|>
+{{ end }}<|im_start|>assistant
+{{ else -}}
+{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ if .Prompt }}<|im_start|>user
+{{ .Prompt }}<|im_end|>
+{{ end }}<|im_start|>assistant
+{{ .Response }}<|im_end|>
+{{- end -}}`, []string{"content", "messages", "prompt", "response", "role", "system"}},
 	}
 
 	for _, tt := range cases {
@@ -80,9 +189,172 @@ func TestParse(t *testing.T) {
 				t.Fatal(err)
 			}
 
-			vars := tmpl.Vars()
-			if !slices.Equal(tt.vars, vars) {
-				t.Errorf("expected %v, got %v", tt.vars, vars)
+			if diff := cmp.Diff(tmpl.Vars(), tt.vars); diff != "" {
+				t.Errorf("mismatch (-got +want):\n%s", diff)
+			}
+		})
+	}
+}
+
+func TestExecuteWithMessages(t *testing.T) {
+	type template struct {
+		name     string
+		template string
+	}
+	cases := []struct {
+		name      string
+		templates []template
+		values    Values
+		expected  string
+	}{
+		{
+			"mistral",
+			[]template{
+				{"no response", `[INST] {{ if .System }}{{ .System }}
+
+{{ end }}{{ .Prompt }}[/INST] `},
+				{"response", `[INST] {{ if .System }}{{ .System }}
+
+{{ end }}{{ .Prompt }}[/INST] {{ .Response }}`},
+				{"messages", `[INST] {{ if .System }}{{ .System }}
+
+{{ end }}
+{{- range .Messages }}
+{{- if eq .Role "user" }}{{ .Content }}[/INST] {{ else if eq .Role "assistant" }}{{ .Content }}[INST] {{ end }}
+{{- end }}`},
+			},
+			Values{
+				Messages: []api.Message{
+					{Role: "user", Content: "Hello friend!"},
+					{Role: "assistant", Content: "Hello human!"},
+					{Role: "user", Content: "What is your name?"},
+				},
+			},
+			`[INST] Hello friend![/INST] Hello human![INST] What is your name?[/INST] `,
+		},
+		{
+			"mistral system",
+			[]template{
+				{"no response", `[INST] {{ if .System }}{{ .System }}
+
+{{ end }}{{ .Prompt }}[/INST] `},
+				{"response", `[INST] {{ if .System }}{{ .System }}
+
+{{ end }}{{ .Prompt }}[/INST] {{ .Response }}`},
+				{"messages", `[INST] {{ if .System }}{{ .System }}
+
+{{ end }}
+{{- range .Messages }}
+{{- if eq .Role "user" }}{{ .Content }}[/INST] {{ else if eq .Role "assistant" }}{{ .Content }}[INST] {{ end }}
+{{- end }}`},
+			},
+			Values{
+				Messages: []api.Message{
+					{Role: "system", Content: "You are a helpful assistant!"},
+					{Role: "user", Content: "Hello friend!"},
+					{Role: "assistant", Content: "Hello human!"},
+					{Role: "user", Content: "What is your name?"},
+				},
+			},
+			`[INST] You are a helpful assistant!
+
+Hello friend![/INST] Hello human![INST] What is your name?[/INST] `,
+		},
+		{
+			"chatml",
+			[]template{
+				// this does not have a "no response" test because it's impossible to render the same output
+				{"response", `{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ if .Prompt }}<|im_start|>user
+{{ .Prompt }}<|im_end|>
+{{ end }}<|im_start|>assistant
+{{ .Response }}<|im_end|>
+`},
+				{"messages", `
+{{- range $index, $_ := .Messages }}<|im_start|>{{ .Role }}
+{{ .Content }}<|im_end|>
+{{ end }}<|im_start|>assistant
+`},
+			},
+			Values{
+				Messages: []api.Message{
+					{Role: "system", Content: "You are a helpful assistant!"},
+					{Role: "user", Content: "Hello friend!"},
+					{Role: "assistant", Content: "Hello human!"},
+					{Role: "user", Content: "What is your name?"},
+				},
+			},
+			`<|im_start|>system
+You are a helpful assistant!<|im_end|>
+<|im_start|>user
+Hello friend!<|im_end|>
+<|im_start|>assistant
+Hello human!<|im_end|>
+<|im_start|>user
+What is your name?<|im_end|>
+<|im_start|>assistant
+`,
+		},
+		{
+			"moondream",
+			[]template{
+				// this does not have a "no response" test because it's impossible to render the same output
+				{"response", `{{ if .Prompt }}Question: {{ .Prompt }}
+
+{{ end }}Answer: {{ .Response }}
+
+`},
+				{"messages", `
+{{- range .Messages }}
+{{- if eq .Role "user" }}Question: {{ .Content }}
+
+{{ else if eq .Role "assistant" }}Answer: {{ .Content }}
+
+{{ end }}
+{{- end }}Answer: `},
+			},
+			Values{
+				Messages: []api.Message{
+					{Role: "user", Content: "What's in this image?", Images: []api.ImageData{[]byte("")}},
+					{Role: "assistant", Content: "It's a hot dog."},
+					{Role: "user", Content: "What's in _this_ image?"},
+					{Role: "user", Images: []api.ImageData{[]byte("")}},
+					{Role: "user", Content: "Is it a hot dog?"},
+				},
+			},
+			`Question: [img-0] What's in this image?
+
+Answer: It's a hot dog.
+
+Question: What's in _this_ image?
+
+[img-1]
+
+Is it a hot dog?
+
+Answer: `,
+		},
+	}
+
+	for _, tt := range cases {
+		t.Run(tt.name, func(t *testing.T) {
+			for _, ttt := range tt.templates {
+				t.Run(ttt.name, func(t *testing.T) {
+					tmpl, err := Parse(ttt.template)
+					if err != nil {
+						t.Fatal(err)
+					}
+
+					var b bytes.Buffer
+					if err := tmpl.Execute(&b, tt.values); err != nil {
+						t.Fatal(err)
+					}
+
+					if diff := cmp.Diff(b.String(), tt.expected); diff != "" {
+						t.Errorf("mismatch (-got +want):\n%s", diff)
+					}
+				})
 			}
 		})
 	}

template/testdata/alfred.gotmpl/system-user-assistant-user

@@ -0,0 +1 @@
+<start_system>You are a helpful assistant.<end_message><start_user>Hello, how are you?<end_message><start_assistant>I'm doing great. How can I help you today?<end_message><start_user>I'd like to show off how chat templating works!<end_message><start_assistant>

template/testdata/alfred.gotmpl/user

@@ -0,0 +1 @@
+<start_user>Hello, how are you?<end_message><start_assistant>

template/testdata/alfred.gotmpl/user-assistant-user

@@ -0,0 +1 @@
+<start_user>Hello, how are you?<end_message><start_assistant>I'm doing great. How can I help you today?<end_message><start_user>I'd like to show off how chat templating works!<end_message><start_assistant>

template/testdata/alpaca.gotmpl/system-user-assistant-user

@@ -0,0 +1,12 @@
+You are a helpful assistant.
+
+### Instruction:
+Hello, how are you?
+
+### Response:
+I'm doing great. How can I help you today?
+
+### Instruction:
+I'd like to show off how chat templating works!
+
+### Response:

template/testdata/alpaca.gotmpl/user

@@ -0,0 +1,4 @@
+### Instruction:
+Hello, how are you?
+
+### Response:

template/testdata/alpaca.gotmpl/user-assistant-user

@@ -0,0 +1,10 @@
+### Instruction:
+Hello, how are you?
+
+### Response:
+I'm doing great. How can I help you today?
+
+### Instruction:
+I'd like to show off how chat templating works!
+
+### Response:

template/testdata/chatml.gotmpl/system-user-assistant-user

@@ -0,0 +1,9 @@
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+Hello, how are you?<|im_end|>
+<|im_start|>assistant
+I'm doing great. How can I help you today?<|im_end|>
+<|im_start|>user
+I'd like to show off how chat templating works!<|im_end|>
+<|im_start|>assistant

template/testdata/chatml.gotmpl/user

@@ -0,0 +1,3 @@
+<|im_start|>user
+Hello, how are you?<|im_end|>
+<|im_start|>assistant

template/testdata/chatml.gotmpl/user-assistant-user

@@ -0,0 +1,7 @@
+<|im_start|>user
+Hello, how are you?<|im_end|>
+<|im_start|>assistant
+I'm doing great. How can I help you today?<|im_end|>
+<|im_start|>user
+I'd like to show off how chat templating works!<|im_end|>
+<|im_start|>assistant

template/testdata/chatqa.gotmpl/system-user-assistant-user

@@ -0,0 +1,9 @@
+System: You are a helpful assistant.
+
+User: Hello, how are you?
+
+Assistant: I'm doing great. How can I help you today?
+
+User: I'd like to show off how chat templating works!
+
+Assistant:

template/testdata/chatqa.gotmpl/user

@@ -0,0 +1,3 @@
+User: Hello, how are you?
+
+Assistant:

template/testdata/chatqa.gotmpl/user-assistant-user

@@ -0,0 +1,7 @@
+User: Hello, how are you?
+
+Assistant: I'm doing great. How can I help you today?
+
+User: I'd like to show off how chat templating works!
+
+Assistant:

template/testdata/codellama-70b-instruct.gotmpl/system-user-assistant-user

@@ -0,0 +1,12 @@
+Source: system
+
+ You are a helpful assistant. <step> Source: user
+
+ Hello, how are you? <step> Source: assistant
+
+ I'm doing great. How can I help you today? <step> Source: user
+
+ I'd like to show off how chat templating works! <step> Source: assistant
+Destination: user
+
+ 

template/testdata/codellama-70b-instruct.gotmpl/user

@@ -0,0 +1,6 @@
+Source: user
+
+ Hello, how are you? <step> Source: assistant
+Destination: user
+
+ 

template/testdata/codellama-70b-instruct.gotmpl/user-assistant-user

@@ -0,0 +1,10 @@
+Source: user
+
+ Hello, how are you? <step> Source: assistant
+
+ I'm doing great. How can I help you today? <step> Source: user
+
+ I'd like to show off how chat templating works! <step> Source: assistant
+Destination: user
+
+ 

template/testdata/falcon-instruct.gotmpl/system-user-assistant-user

@@ -0,0 +1,8 @@
+System: You are a helpful assistant.
+User:
+Hello, how are you?
+Falcon:
+I'm doing great. How can I help you today?
+User:
+I'd like to show off how chat templating works!
+Falcon:

template/testdata/falcon-instruct.gotmpl/user

@@ -0,0 +1,3 @@
+User:
+Hello, how are you?
+Falcon:

template/testdata/falcon-instruct.gotmpl/user-assistant-user

@@ -0,0 +1,7 @@
+User:
+Hello, how are you?
+Falcon:
+I'm doing great. How can I help you today?
+User:
+I'd like to show off how chat templating works!
+Falcon:

template/testdata/gemma-instruct.gotmpl/system-user-assistant-user

@@ -0,0 +1,8 @@
+<start_of_turn>user
+You are a helpful assistant.
+Hello, how are you?<end_of_turn>
+<start_of_turn>model
+I'm doing great. How can I help you today?<end_of_turn>
+<start_of_turn>user
+I'd like to show off how chat templating works!<end_of_turn>
+<start_of_turn>model

template/testdata/gemma-instruct.gotmpl/user

@@ -0,0 +1,3 @@
+<start_of_turn>user
+Hello, how are you?<end_of_turn>
+<start_of_turn>model

template/testdata/gemma-instruct.gotmpl/user-assistant-user

@@ -0,0 +1,7 @@
+<start_of_turn>user
+Hello, how are you?<end_of_turn>
+<start_of_turn>model
+I'm doing great. How can I help you today?<end_of_turn>
+<start_of_turn>user
+I'd like to show off how chat templating works!<end_of_turn>
+<start_of_turn>model

template/testdata/granite-instruct.gotmpl/system-user-assistant-user

@@ -0,0 +1,13 @@
+System:
+You are a helpful assistant.
+
+Question:
+Hello, how are you?
+
+Answer:
+I'm doing great. How can I help you today?
+
+Question:
+I'd like to show off how chat templating works!
+
+Answer:

template/testdata/granite-instruct.gotmpl/user

@@ -0,0 +1,4 @@
+Question:
+Hello, how are you?
+
+Answer:

template/testdata/granite-instruct.gotmpl/user-assistant-user

@@ -0,0 +1,10 @@
+Question:
+Hello, how are you?
+
+Answer:
+I'm doing great. How can I help you today?
+
+Question:
+I'd like to show off how chat templating works!
+
+Answer: