Michael Yang
GitHub
commit
0ff42e84b0
29 changed files with 2520 additions and 1685 deletions
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@ -1,200 +1,122 @@
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package convert
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import (
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"cmp"
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"encoding/binary"
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"encoding/json"
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"errors"
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"fmt"
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"io"
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"io/fs"
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"log/slog"
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"os"
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"path/filepath"
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"slices"
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"strings"
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"google.golang.org/protobuf/proto"
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"github.com/ollama/ollama/convert/sentencepiece"
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"github.com/ollama/ollama/llm"
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)
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const (
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_ int32 = iota
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tokenTypeNormal
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tokenTypeUnknown
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tokenTypeControl
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tokenTypeUserDefined
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tokenTypeUnused
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tokenTypeByte
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)
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type Params struct {
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Architectures []string `json:"architectures"`
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VocabSize int `json:"vocab_size"`
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HiddenSize int `json:"hidden_size"` // n_embd
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HiddenLayers int `json:"num_hidden_layers"` // n_layer
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ContextSize int `json:"max_position_embeddings"`
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IntermediateSize int `json:"intermediate_size"`
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AttentionHeads int `json:"num_attention_heads"` // n_head
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KeyValHeads int `json:"num_key_value_heads"`
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NormEPS float64 `json:"rms_norm_eps"`
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BoSTokenID int `json:"bos_token_id"`
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EoSTokenID int `json:"eos_token_id"`
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HeadDimension int `json:"head_dim"`
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PaddingTokenID int `json:"pad_token_id"`
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RopeFrequencyBase float64 `json:"rope_theta"`
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Experts int `json:"num_local_experts"`
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ExpertsUsed int `json:"num_experts_per_tok"`
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PreTokenizer string
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ByteOrder
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type Parameters struct {
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Architectures []string `json:"architectures"`
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VocabSize uint32 `json:"vocab_size"`
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}
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type ByteOrder interface {
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binary.ByteOrder
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binary.AppendByteOrder
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func (Parameters) KV(t *Tokenizer) llm.KV {
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kv := llm.KV{
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"general.file_type": uint32(1),
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"general.quantization_version": uint32(2),
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"tokenizer.ggml.pre": t.Pre,
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"tokenizer.ggml.model": t.Vocabulary.Model,
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"tokenizer.ggml.tokens": t.Vocabulary.Tokens,
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"tokenizer.ggml.scores": t.Vocabulary.Scores,
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"tokenizer.ggml.token_type": t.Vocabulary.Types,
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}
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if t.Template != "" {
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kv["tokenizer.chat_template"] = t.Template
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}
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for _, sv := range t.SpecialVocabulary {
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kv[fmt.Sprintf("tokenizer.ggml.%s_token_id", sv.Key())] = uint32(sv.ID)
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kv[fmt.Sprintf("tokenizer.ggml.add_%s_token", sv.Key())] = sv.AddToken
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}
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return kv
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}
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type ModelArch interface {
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GetTensors() error
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LoadVocab() error
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WriteGGUF(io.WriteSeeker) error
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func (Parameters) specialTokenTypes() []string {
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return []string{
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"bos", "eos", "unk", "sep", "pad", "cls", "mask",
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}
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}
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type ModelFormat interface {
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GetLayerName(string) (string, error)
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GetTensors(string, *Params) ([]llm.Tensor, error)
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GetParams(string) (*Params, error)
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GetModelArch(string, string, *Params) (ModelArch, error)
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func (Parameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
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return llm.WriteGGUF(ws, kv, ts)
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}
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type ModelData struct {
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Path string
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Name string
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Params *Params
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Vocab *Vocab
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Tensors []llm.Tensor
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Format ModelFormat
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type Converter interface {
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// KV maps parameters to LLM key-values
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KV(*Tokenizer) llm.KV
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// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
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Tensors([]Tensor) []llm.Tensor
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// tensorName returns the LLM tensor name for a specific input name
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tensorName(string) string
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// specialTokenTypes returns any special token types the model uses
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specialTokenTypes() []string
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writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
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}
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func GetModelFormat(dirname string) (ModelFormat, error) {
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files, err := filepath.Glob(filepath.Join(dirname, "*"))
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// Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
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// and files it finds in the input path.
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// Supported input model formats include safetensors.
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// Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
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func Convert(fsys fs.FS, ws io.WriteSeeker) error {
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bts, err := fs.ReadFile(fsys, "config.json")
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if err != nil {
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return nil, err
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return err
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}
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for _, fn := range files {
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if strings.HasSuffix(fn, ".safetensors") {
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return &SafetensorFormat{}, nil
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} else if strings.HasSuffix(fn, ".bin") || strings.HasSuffix(fn, ".pth") {
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slog.Debug("model is torch")
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return &TorchFormat{}, nil
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}
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var p Parameters
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if err := json.Unmarshal(bts, &p); err != nil {
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return err
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}
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return nil, fmt.Errorf("couldn't determine model format")
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}
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if len(p.Architectures) < 1 {
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return errors.New("unknown architecture")
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}
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// Details on gguf's tokenizer can be found at:
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// https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#tokenizer
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type Vocab struct {
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Tokens []string
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Scores []float32
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Types []int32
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Merges []string
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}
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var conv Converter
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switch p.Architectures[0] {
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case "LlamaForCausalLM", "MistralForCausalLM":
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conv = &llama{}
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case "MixtralForCausalLM":
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conv = &mixtral{}
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case "GemmaForCausalLM":
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conv = &gemma{}
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default:
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return errors.New("unsupported architecture")
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}
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func LoadSentencePieceTokens(dirpath string, params *Params) (*Vocab, error) {
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slog.Info(fmt.Sprintf("reading vocab from %s", filepath.Join(dirpath, "tokenizer.model")))
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in, err := os.ReadFile(filepath.Join(dirpath, "tokenizer.model"))
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if err := json.Unmarshal(bts, conv); err != nil {
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return err
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}
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t, err := parseTokenizer(fsys, conv.specialTokenTypes())
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if err != nil {
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return nil, err
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return err
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}
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// To regenerate sentencepiece from the protobufs use:
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// protoc -I=./ --go_out=./ sentencepiece_model.proto
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modelProto := &sentencepiece.ModelProto{}
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if err := proto.Unmarshal(in, modelProto); err != nil {
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return nil, err
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}
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v := &Vocab{
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Tokens: make([]string, 0),
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Scores: make([]float32, 0),
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Types: make([]int32, 0),
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}
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pieces := modelProto.GetPieces()
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for _, p := range pieces {
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v.Tokens = append(v.Tokens, p.GetPiece())
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v.Scores = append(v.Scores, p.GetScore())
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t := p.GetType()
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switch t {
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case sentencepiece.ModelProto_SentencePiece_UNKNOWN:
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case sentencepiece.ModelProto_SentencePiece_CONTROL:
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case sentencepiece.ModelProto_SentencePiece_UNUSED:
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case sentencepiece.ModelProto_SentencePiece_BYTE:
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default:
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t = sentencepiece.ModelProto_SentencePiece_NORMAL
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if vocabSize := int(p.VocabSize); vocabSize > len(t.Vocabulary.Tokens) {
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slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", p.VocabSize, "actual", len(t.Vocabulary.Tokens))
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for i := range vocabSize - len(t.Vocabulary.Tokens) {
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t.Vocabulary.Tokens = append(t.Vocabulary.Tokens, fmt.Sprintf("[PAD%d]", i))
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t.Vocabulary.Scores = append(t.Vocabulary.Scores, -1)
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t.Vocabulary.Types = append(t.Vocabulary.Types, tokenTypeUserDefined)
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}
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v.Types = append(v.Types, int32(t))
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} else {
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slog.Debug("vocabulary", "size", len(t.Vocabulary.Tokens))
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}
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slog.Info(fmt.Sprintf("vocab size: %d", len(v.Tokens)))
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// add any additional tokens
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addIn, err := os.ReadFile(filepath.Join(dirpath, "added_tokens.json"))
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if os.IsNotExist(err) {
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return v, nil
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} else if err != nil {
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return nil, err
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ts, err := parseTensors(fsys)
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if err != nil {
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return err
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}
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slog.Info("reading user defined tokens")
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var extraTokenData map[string]int
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if err := json.Unmarshal(addIn, &extraTokenData); err != nil {
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return nil, err
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}
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type token struct {
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key string
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pos int
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}
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extraTokens := make([]token, 0)
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for k, id := range extraTokenData {
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extraTokens = append(extraTokens, token{k, id})
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}
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slices.SortFunc(extraTokens, func(a, b token) int {
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return cmp.Compare(a.pos, b.pos)
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})
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numToks := len(v.Tokens)
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for cnt, t := range extraTokens {
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// the token id should match the specific index for the total number of tokens
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if t.pos != cnt+numToks {
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return nil, fmt.Errorf("token ID '%d' for '%s' doesn't match total token size", t.pos, t.key)
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}
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v.Tokens = append(v.Tokens, t.key)
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v.Scores = append(v.Scores, -1000.0)
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v.Types = append(v.Types, tokenTypeUserDefined)
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}
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slog.Info(fmt.Sprintf("vocab size w/ extra tokens: %d", len(v.Tokens)))
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if params.VocabSize > len(v.Tokens) {
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missingTokens := params.VocabSize - len(v.Tokens)
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slog.Warn(fmt.Sprintf("vocab is missing %d tokens", missingTokens))
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for cnt := range missingTokens {
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v.Tokens = append(v.Tokens, fmt.Sprintf("<dummy%05d>", cnt+1))
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v.Scores = append(v.Scores, -1)
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v.Types = append(v.Types, tokenTypeUserDefined)
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}
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}
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return v, nil
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return conv.writeFile(ws, conv.KV(t), conv.Tensors(ts))
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}
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103
convert/convert_gemma.go
Normal file
103
convert/convert_gemma.go
Normal file
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@ -0,0 +1,103 @@
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package convert
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import (
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"strings"
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"github.com/pdevine/tensor"
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"github.com/pdevine/tensor/native"
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"github.com/ollama/ollama/llm"
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)
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type gemma struct {
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Parameters
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MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
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HiddenSize uint32 `json:"hidden_size"`
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HiddenLayers uint32 `json:"num_hidden_layers"`
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IntermediateSize uint32 `json:"intermediate_size"`
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NumAttentionHeads uint32 `json:"num_attention_heads"`
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NumKeyValueHeads uint32 `json:"num_key_value_heads"`
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RMSNormEPS float32 `json:"rms_norm_eps"`
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HeadDim uint32 `json:"head_dim"`
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}
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var _ Converter = (*gemma)(nil)
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func (p *gemma) KV(t *Tokenizer) llm.KV {
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kv := p.Parameters.KV(t)
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kv["general.architecture"] = "gemma"
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kv["general.name"] = "gemma"
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kv["gemma.context_length"] = p.MaxPositionEmbeddings
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kv["gemma.embedding_length"] = p.HiddenSize
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kv["gemma.block_count"] = p.HiddenLayers
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kv["gemma.feed_forward_length"] = p.IntermediateSize
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kv["gemma.attention.head_count"] = p.NumAttentionHeads
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kv["gemma.attention.head_count_kv"] = p.NumKeyValueHeads
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kv["gemma.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
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kv["gemma.attention.key_length"] = p.HeadDim
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kv["gemma.attention.value_length"] = p.HeadDim
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kv["tokenizer.ggml.eot_token_id"] = uint32(107)
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kv["tokenizer.ggml.middle_token_id"] = uint32(68)
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kv["tokenizer.ggml.prefix_token_id"] = uint32(67)
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kv["tokenizer.ggml.suffix_token_id"] = uint32(69)
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return kv
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}
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func (p *gemma) Tensors(ts []Tensor) []llm.Tensor {
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var out []llm.Tensor
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for _, t := range ts {
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name := p.tensorName(t.Name())
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if strings.HasSuffix(name, "_norm.weight") {
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t.SetRepacker(p.addOne)
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}
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out = append(out, llm.Tensor{
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Name: name,
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Kind: t.Kind(),
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Shape: t.Shape(),
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WriterTo: t,
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})
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}
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return out
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}
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func (p *gemma) tensorName(n string) string {
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return strings.NewReplacer(
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"model.embed_tokens", "token_embd",
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"model.norm", "output_norm",
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"model.layers", "blk",
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"input_layernorm", "attn_norm",
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"self_attn.q_proj", "attn_q",
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"self_attn.k_proj", "attn_k",
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"self_attn.v_proj", "attn_v",
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"self_attn.o_proj", "attn_output",
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"mlp.gate_proj", "ffn_gate",
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"mlp.down_proj", "ffn_down",
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"mlp.up_proj", "ffn_up",
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"post_attention_layernorm", "ffn_norm",
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"block_sparse_moe.gate", "ffn_inp",
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).Replace(n)
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}
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func (*gemma) addOne(_ string, data []float32, shape []uint64) ([]float32, error) {
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n := tensor.New(tensor.WithShape(int(shape[0])), tensor.WithBacking(data))
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ones := tensor.Ones(tensor.Float32, int(shape[0]))
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n, err := n.Add(ones)
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if err != nil {
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return nil, err
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}
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ts, err := native.SelectF32(n, 0)
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if err != nil {
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return nil, err
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}
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var f32s []float32
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for _, t := range ts {
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f32s = append(f32s, t...)
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}
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return f32s, nil
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}
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182
convert/convert_llama.go
Normal file
182
convert/convert_llama.go
Normal file
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@ -0,0 +1,182 @@
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package convert
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import (
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"cmp"
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"fmt"
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"strings"
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"github.com/ollama/ollama/llm"
|
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"github.com/pdevine/tensor"
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"github.com/pdevine/tensor/native"
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)
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type llama struct {
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Parameters
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NLayers uint32 `json:"n_layers"`
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NumHiddenLayers uint32 `json:"num_hidden_layers"`
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NLayer uint32 `json:"n_layer"`
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MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
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NCtx uint32 `json:"n_ctx"`
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HiddenSize uint32 `json:"hidden_size"`
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NEmbd uint32 `json:"n_embd"`
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IntermediateSize uint32 `json:"intermediate_size"`
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NInner uint32 `json:"n_inner"`
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NumAttentionHeads uint32 `json:"num_attention_heads"`
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NHead uint32 `json:"n_head"`
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NumKeyValueHeads uint32 `json:"num_key_value_heads"`
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RopeTheta float32 `json:"rope_theta"`
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RopeScaling struct {
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Type string `json:"type"`
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Factor float32 `json:"factor"`
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} `json:"rope_scaling"`
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RMSNormEPS float32 `json:"rms_norm_eps"`
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LayerNormEPS float32 `json:"layer_norm_eps"`
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LayerNormEpsilon float32 `json:"layer_norm_epsilon"`
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NormEpsilon float32 `json:"norm_epsilon"`
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HeadDim uint32 `json:"head_dim"`
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}
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var _ Converter = (*llama)(nil)
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func (p *llama) KV(t *Tokenizer) llm.KV {
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kv := p.Parameters.KV(t)
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kv["general.architecture"] = "llama"
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kv["general.name"] = "llama"
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kv["llama.vocab_size"] = p.VocabSize
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kv["llama.block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
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if contextLength := cmp.Or(p.MaxPositionEmbeddings, p.NCtx); contextLength > 0 {
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kv["llama.context_length"] = contextLength
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}
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if embeddingLength := cmp.Or(p.HiddenSize, p.NEmbd); embeddingLength > 0 {
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kv["llama.embedding_length"] = cmp.Or(p.HiddenSize, p.NEmbd)
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}
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if feedForwardLength := cmp.Or(p.IntermediateSize, p.NInner); feedForwardLength > 0 {
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kv["llama.feed_forward_length"] = cmp.Or(p.IntermediateSize, p.NInner)
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}
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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 p.HeadDim > 0 {
|
||||
kv["llama.attention.key_length"] = p.HeadDim
|
||||
kv["llama.attention.value_length"] = p.HeadDim
|
||||
}
|
||||
|
||||
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
|
||||
}
|
||||
89
convert/convert_mixtral.go
Normal file
89
convert/convert_mixtral.go
Normal file
|
|
@ -0,0 +1,89 @@
|
|||
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
|
||||
}
|
||||
|
|
@ -1,48 +1,33 @@
|
|||
//go:build slow
|
||||
|
||||
package convert
|
||||
|
||||
import (
|
||||
"crypto/sha256"
|
||||
"encoding/json"
|
||||
"flag"
|
||||
"fmt"
|
||||
"io"
|
||||
"io/fs"
|
||||
"log/slog"
|
||||
"math"
|
||||
"os"
|
||||
"path/filepath"
|
||||
"slices"
|
||||
"testing"
|
||||
|
||||
"github.com/ollama/ollama/llm"
|
||||
"golang.org/x/exp/maps"
|
||||
)
|
||||
|
||||
func convertFull(t *testing.T, p string) (llm.KV, llm.Tensors) {
|
||||
func convertFull(t *testing.T, fsys fs.FS) (*os.File, 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 := arch.WriteGGUF(f); err != nil {
|
||||
if err := Convert(fsys, f); err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
|
|
@ -50,53 +35,91 @@ func convertFull(t *testing.T, p string) (llm.KV, llm.Tensors) {
|
|||
if err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
defer r.Close()
|
||||
t.Cleanup(func() { r.Close() })
|
||||
|
||||
m, _, err := llm.DecodeGGML(r)
|
||||
m, _, err := llm.DecodeGGML(r, math.MaxInt)
|
||||
if err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
return m.KV(), m.Tensors()
|
||||
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())
|
||||
}
|
||||
|
||||
func TestConvertFull(t *testing.T) {
|
||||
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},
|
||||
cases := []string{
|
||||
"Meta-Llama-3-8B-Instruct",
|
||||
"Mistral-7B-Instruct-v0.2",
|
||||
"Mixtral-8x7B-Instruct-v0.1",
|
||||
"gemma-2b-it",
|
||||
}
|
||||
|
||||
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 {
|
||||
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 {
|
||||
t.Skipf("%s not found", p)
|
||||
}
|
||||
|
||||
kv, tensors := convertFull(t, p)
|
||||
f, kv, tensors := convertFull(t, os.DirFS(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)
|
||||
}
|
||||
|
||||
if kv.Architecture() != tt.arch {
|
||||
t.Fatalf("expected llama, got %s", kv.Architecture())
|
||||
actual[k] = fmt.Sprintf("%x", sha256.Sum256(bts))
|
||||
}
|
||||
}
|
||||
|
||||
if kv.FileType().String() != "F16" {
|
||||
t.Fatalf("expected F16, got %s", kv.FileType())
|
||||
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 len(tensors) != tt.tensors {
|
||||
t.Fatalf("expected %d tensors, got %d", tt.tensors, len(tensors))
|
||||
expectFile, err := os.Open(filepath.Join("testdata", fmt.Sprintf("%s.json", tt)))
|
||||
if err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
layers := tensors.Layers()
|
||||
if len(layers) != tt.layers {
|
||||
t.Fatalf("expected %d layers, got %d", tt.layers, len(layers))
|
||||
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)
|
||||
}
|
||||
}
|
||||
})
|
||||
}
|
||||
|
|
|
|||
58
convert/fs.go
Normal file
58
convert/fs.go
Normal file
|
|
@ -0,0 +1,58 @@
|
|||
package convert
|
||||
|
||||
import (
|
||||
"archive/zip"
|
||||
"errors"
|
||||
"io"
|
||||
"io/fs"
|
||||
"os"
|
||||
"path/filepath"
|
||||
)
|
||||
|
||||
type ZipReader struct {
|
||||
r *zip.Reader
|
||||
p string
|
||||
|
||||
// limit is the maximum size of a file that can be read directly
|
||||
// from the zip archive. Files larger than this size will be extracted
|
||||
limit int64
|
||||
}
|
||||
|
||||
func NewZipReader(r *zip.Reader, p string, limit int64) fs.FS {
|
||||
return &ZipReader{r, p, limit}
|
||||
}
|
||||
|
||||
func (z *ZipReader) Open(name string) (fs.File, error) {
|
||||
r, err := z.r.Open(name)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
defer r.Close()
|
||||
|
||||
if fi, err := r.Stat(); err != nil {
|
||||
return nil, err
|
||||
} else if fi.Size() < z.limit {
|
||||
return r, nil
|
||||
}
|
||||
|
||||
if !filepath.IsLocal(name) {
|
||||
return nil, zip.ErrInsecurePath
|
||||
}
|
||||
|
||||
n := filepath.Join(z.p, name)
|
||||
if _, err := os.Stat(n); errors.Is(err, os.ErrNotExist) {
|
||||
w, err := os.Create(n)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
defer w.Close()
|
||||
|
||||
if _, err := io.Copy(w, r); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
} else if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
return os.Open(n)
|
||||
}
|
||||
102
convert/gemma.go
102
convert/gemma.go
|
|
@ -1,102 +0,0 @@
|
|||
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)
|
||||
}
|
||||
159
convert/llama.go
159
convert/llama.go
|
|
@ -1,159 +0,0 @@
|
|||
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
|
||||
}
|
||||
|
|
@ -1,84 +0,0 @@
|
|||
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),
|
||||
}
|
||||
|
||||
if m.Params.HeadDimension > 0 {
|
||||
kv["llama.attention.key_length"] = uint32(m.Params.HeadDimension)
|
||||
kv["llama.attention.value_length"] = uint32(m.Params.HeadDimension)
|
||||
}
|
||||
|
||||
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)
|
||||
}
|
||||
|
|
@ -1,87 +0,0 @@
|
|||
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)
|
||||
}
|
||||
82
convert/reader.go
Normal file
82
convert/reader.go
Normal file
|
|
@ -0,0 +1,82 @@
|
|||
package convert
|
||||
|
||||
import (
|
||||
"errors"
|
||||
"io"
|
||||
"io/fs"
|
||||
"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
|
||||
}
|
||||
|
||||
const (
|
||||
tensorKindF32 uint32 = iota
|
||||
tensorKindF16
|
||||
)
|
||||
|
||||
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 tensorKindF32
|
||||
default:
|
||||
return tensorKindF16
|
||||
}
|
||||
}
|
||||
|
||||
func (t *tensorBase) SetRepacker(fn repacker) {
|
||||
t.repacker = fn
|
||||
}
|
||||
|
||||
type repacker func(string, []float32, []uint64) ([]float32, error)
|
||||
|
||||
func parseTensors(fsys fs.FS) ([]Tensor, error) {
|
||||
patterns := []struct {
|
||||
Pattern string
|
||||
Func func(fs.FS, ...string) ([]Tensor, error)
|
||||
}{
|
||||
{"model-*-of-*.safetensors", parseSafetensors},
|
||||
{"model.safetensors", parseSafetensors},
|
||||
{"pytorch_model-*-of-*.bin", parseTorch},
|
||||
{"pytorch_model.bin", parseTorch},
|
||||
{"consolidated.*.pth", parseTorch},
|
||||
}
|
||||
|
||||
for _, pattern := range patterns {
|
||||
matches, err := fs.Glob(fsys, pattern.Pattern)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if len(matches) > 0 {
|
||||
return pattern.Func(fsys, matches...)
|
||||
}
|
||||
}
|
||||
|
||||
return nil, errors.New("unknown tensor format")
|
||||
}
|
||||
149
convert/reader_safetensors.go
Normal file
149
convert/reader_safetensors.go
Normal file
|
|
@ -0,0 +1,149 @@
|
|||
package convert
|
||||
|
||||
import (
|
||||
"bytes"
|
||||
"encoding/binary"
|
||||
"encoding/json"
|
||||
"fmt"
|
||||
"io"
|
||||
"io/fs"
|
||||
"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(fsys fs.FS, ps ...string) ([]Tensor, error) {
|
||||
var ts []Tensor
|
||||
for _, p := range ps {
|
||||
f, err := fsys.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{
|
||||
fs: fsys,
|
||||
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
|
||||
}
|
||||
|
||||
// safetensorsPad returns the padded size of the safetensors file given a length n and offset s
|
||||
func safetensorsPad(n, offset int64) int64 {
|
||||
return 8 + n + offset
|
||||
}
|
||||
|
||||
type safetensor struct {
|
||||
fs fs.FS
|
||||
path string
|
||||
dtype string
|
||||
offset int64
|
||||
size int64
|
||||
*tensorBase
|
||||
}
|
||||
|
||||
func (st safetensor) WriteTo(w io.Writer) (int64, error) {
|
||||
f, err := st.fs.Open(st.path)
|
||||
if err != nil {
|
||||
return 0, err
|
||||
}
|
||||
defer f.Close()
|
||||
|
||||
if seeker, ok := f.(io.Seeker); ok {
|
||||
if _, err := seeker.Seek(st.offset, io.SeekStart); err != nil {
|
||||
return 0, err
|
||||
}
|
||||
} else {
|
||||
if _, err := io.CopyN(io.Discard, f, st.offset); 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 tensorKindF32:
|
||||
return 0, binary.Write(w, binary.LittleEndian, f32s)
|
||||
case tensorKindF16:
|
||||
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())
|
||||
}
|
||||
}
|
||||
47
convert/reader_torch.go
Normal file
47
convert/reader_torch.go
Normal file
|
|
@ -0,0 +1,47 @@
|
|||
package convert
|
||||
|
||||
import (
|
||||
"io"
|
||||
"io/fs"
|
||||
|
||||
"github.com/nlpodyssey/gopickle/pytorch"
|
||||
"github.com/nlpodyssey/gopickle/types"
|
||||
)
|
||||
|
||||
func parseTorch(fsys fs.FS, 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
|
||||
}
|
||||
|
|
@ -1,309 +0,0 @@
|
|||
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(¶ms); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
params.ByteOrder = binary.LittleEndian
|
||||
return ¶ms, 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")
|
||||
}
|
||||
313
convert/testdata/Meta-Llama-3-8B-Instruct.json
vendored
Normal file
313
convert/testdata/Meta-Llama-3-8B-Instruct.json
vendored
Normal file
|
|
@ -0,0 +1,313 @@
|
|||
{
|
||||
"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",
|
||||
"blk.9.ffn_down.weight": "acb898a6490adff592e10b4c62d70edc5941661ee6da44658500e9205357c8e9",
|
||||
"blk.9.ffn_gate.weight": "4cff63013593aadc3ffbaaa6ed70ffdba1224cd43c3644bf6f4162b5ac1ab542",
|
||||
"blk.9.ffn_up.weight": "f985b5a2d6cf4fe32c7256301c3c89b8ad22b59e516342c52da42d8110766a4e",
|
||||
"blk.9.ffn_norm.weight": "0d659c538bc6b21ed0018f107ab674a7424a00a42946c80e07208b479b21918f",
|
||||
"blk.9.attn_k.weight": "f67611d888780d1b38c1c146b361c65310c8183bdf64fd73e2259985c6e8517f",
|
||||
"blk.9.attn_output.weight": "f12ca1fa62a02ddc3f77f798bfb5707e0c50bf18ee0eaa67025521a98355f26b",
|
||||
"blk.9.attn_q.weight": "3865185f4361a645b086ad47b72904c095313fb1c624e511647bf1a7dfc1c476",
|
||||
"blk.9.attn_v.weight": "92125bbfed63544ab56052bd1e4aa453bbf34c795249ee54cde54907c8c6d1d3",
|
||||
"blk.10.attn_norm.weight": "5d6bfbe545bcc2fcb2fc75c68f64b1f4c918badaf53e0156fe2d88aa977b2f94",
|
||||
"blk.10.ffn_down.weight": "1dd9da8b0d2696ab5531fbca8a29c7d67567620a9d3e5fc2a19ec5d7e4c6cc8a",
|
||||
"blk.10.ffn_gate.weight": "6e55e7f014edaebda0ac6819a426221d3b025c27312a2e18cc5806f31e3db226",
|
||||
"blk.10.ffn_up.weight": "d80dde54af5db51241345ee8d64c1972608644f4deeac1e8195dc423bf27474a",
|
||||
"blk.10.ffn_norm.weight": "f6ca65951d58ae3379eee8247bec34ebd0db05674cc9295593573841b8a55df3",
|
||||
"blk.10.attn_k.weight": "b58e350bd6b49aba0fba4e4dd6865de3a2a0651ab865dbf2419b627b53ffc187",
|
||||
"blk.10.attn_output.weight": "6b26a986e12fe66ec286a21d7d5af5eaa1bfe6f2bf502165d270e4497235a54a",
|
||||
"blk.10.attn_q.weight": "3440e0e5b7e0d1e426424ae5a33f4e057be623249e9035ea12e57dbe5d3893c4",
|
||||
"blk.10.attn_v.weight": "ebfadcfe14bcd6dee933053df0a67e12e7a196d5cc45728c1ffb2a2daedd5ca2",
|
||||
"blk.11.attn_norm.weight": "3ed057b9576cd2de84507ef64c7646dc478c651efca4c2024cbe91a4f3fbf0bc",
|
||||
"blk.11.ffn_down.weight": "8ff1c2487d22f5c499761e4eb721418f141f960160d0bab779595a34e4d68898",
|
||||
"blk.11.ffn_gate.weight": "9c74e4507c7e45bf39b7cc7402198cd1dd77e3fff8c625b0413acaeb16efeb9f",
|
||||
"blk.11.ffn_up.weight": "4367158007161d29939e00a322bb6776016e43f648a94f9b08a96a477aae75be",
|
||||
"blk.11.ffn_norm.weight": "1cc0288c1491072121f4c9a0af20be0e13af49895696a3320e4fcac608768de3",
|
||||
"blk.11.attn_k.weight": "066f5b3c144fce1366835e1ebf376f768b333b8ae29f5b478c42d1d0c809c855",
|
||||
"blk.11.attn_output.weight": "e0d9f3d3f2c54aed59c02713ea4fb562799ddbacbe67ca3998dfc887bc44e47b",
|
||||
"blk.11.attn_q.weight": "28d3ecc8a88cb3815e89a7f7a7d043da7a71f702b337a126e4d3a2ac1cd6370f",
|
||||
"blk.11.attn_v.weight": "7c5cdef10ee73bca0a3b9f6ece5f0a0155664e0ce3d8de90ccdccfab5545e5e7",
|
||||
"blk.12.attn_norm.weight": "973b133301a1af760cd7b3a7955371ea0a750808b442deb6adaf7b98482bd0c6",
|
||||
"blk.12.ffn_down.weight": "d6c87b4b4ca03f75546ddd6a9e7fca720585a309188723c1ace8122438d4b200",
|
||||
"blk.12.ffn_gate.weight": "2189a6e0cab1540bd05d6089b922aa8fd694be51255654933c165f302a0c955f",
|
||||
"blk.12.ffn_up.weight": "5affbec19b58d092b9305721e3552481fe2eff51269ea3ed91cda3b9ef84d4df",
|
||||
"blk.12.ffn_norm.weight": "f650fd42a34e950f758b4a130e7b8b1a712b1dcbede0291bb8edde47aaed0ef6",
|
||||
"blk.12.attn_k.weight": "59b1e86f10450a7cc188beefc0856d2dcf44e8d7fdd9cd8859c30ec1ebaf24b6",
|
||||
"blk.12.attn_output.weight": "446b0d36b2f66bd72a2323f4f4e9d85a0f621e9a58872e89a27248d6b1123238",
|
||||
"blk.12.attn_q.weight": "3ed6bfd39f040301ed99fad882d3e569769d594259f9948445bef0e44ec881fb",
|
||||
"blk.12.attn_v.weight": "e73652cd5d0029b1931be3ba9d82508f6696dce5a29d085476a54fb7a2ddbabc",
|
||||
"blk.13.attn_norm.weight": "491b85278c0bd67bd31b9b8a9720902c244bd067e53a4a03641b7c0994782e82",
|
||||
"blk.13.ffn_down.weight": "ad71cc248a85e9ced49307a24a9bfae01d387e979a7689c82ff59998e09741f3",
|
||||
"blk.13.ffn_gate.weight": "0a55984d53971fab97575ee0ef5882013be7fdecfa76e3fbebb5dc85a07a14d4",
|
||||
"blk.13.ffn_up.weight": "378b697b35e2e53c0de98e8e29b73d42ae3ec112ec16129aa5997a9e2f3b5943",
|
||||
"blk.13.ffn_norm.weight": "f8aff2f69ab286210fad45a62b03f8d10b38f96a420d7baadf6b95d7b0b0bcd2",
|
||||
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|
||||
"blk.28.ffn_norm.weight": "b8f9e54c322079ff20a82b88948cdc2916c22c7db40b9a9ed6d3cbe89efb727e",
|
||||
"blk.28.attn_k.weight": "55d055ba653b728d6e784f9e013786fed07115c9fdf23367e3941386d5e77db8",
|
||||
"blk.28.attn_output.weight": "155101c03ddbf18f4fd0694bfc982f33c7bae25c9b087d6f5273c2bfbffcf2c9",
|
||||
"blk.28.attn_q.weight": "1ed19bfdd22e9c14eca014739982492e9516d411515a8585f65cf754d849e53f",
|
||||
"blk.28.attn_v.weight": "11ba854dd575c025d37256eee9041f6d1bd2b549a083d6409a09bfc1542913f3",
|
||||
"blk.29.attn_norm.weight": "02b0bf5e2fcefd11a153cc988c81ba672682e4844fcf6442423e21a0e10d566d",
|
||||
"blk.29.ffn_down.weight": "594bb692ec2779938721ff4748666ca8370e0e4fe85229503f616438b8884f5f",
|
||||
"blk.29.ffn_gate.weight": "8bedcf47e91dcb2cf4093de56b048ee411faab6ff472f89ab2c9c113a08e6967",
|
||||
"blk.29.ffn_up.weight": "e241a547b5fd6dfca8200b8141e21c1c487a96cbc4e5855f181a7ed1be91b642",
|
||||
"blk.29.ffn_norm.weight": "e63eba5e4c6b288bfd9f15e46e236086456c8b7f1f9c732c0b5de84962a2e7cc",
|
||||
"blk.29.attn_k.weight": "afe5979d5bcf211aebb526620f5974bcb0a2c39c8be71e815575c55d6385e3aa",
|
||||
"blk.29.attn_output.weight": "9c944ed44b124b014906fc240afd3b90aed56bbd9567f2eddfd5b7a685b3cb48",
|
||||
"blk.29.attn_q.weight": "e234e08e5c1bd9245a2edc8d63e9933b6b879f97c01392209cad4f55f05f3ada",
|
||||
"blk.29.attn_v.weight": "5cb8e3e5f954e775c5a5e4de7a9a62b17e9c6931bb0ff0e2f82c4126fd3e1a1c",
|
||||
"blk.30.attn_norm.weight": "a65483ee51a0b214144ec8a14f28ea5437586e9e12ebe342a57d1f8627ee12af",
|
||||
"blk.30.ffn_down.weight": "417959da77ceb33ead4271cbb9428b195196173a893c44e52880a7ec61b4856b",
|
||||
"blk.30.ffn_gate.weight": "a0d503ffcbe45dc927600bb98c9f6082487e65cb577ab545add400d666a87638",
|
||||
"blk.30.ffn_up.weight": "f8ab957b82ffcd10b21303cb5e866209b6fe95f827b1b94e9a949207952d12c0",
|
||||
"blk.30.ffn_norm.weight": "210c7ceb0514a9ef27b5d4d1b3aff6dde43f1af0345a050d71097940e0e73e03",
|
||||
"blk.30.attn_k.weight": "16861b9abcf5a3fe73c93d977ca45a1e6daa65be0fd85c2cff53486ce2033afa",
|
||||
"blk.30.attn_output.weight": "ca541fb2e57e2257118c35784845b0c731278af8db3036ac53d71aa1681fdbdc",
|
||||
"blk.30.attn_q.weight": "f7834917748e26bb456b945e230bc926c228e93696bc01fbc2b134bdeeac71a1",
|
||||
"blk.30.attn_v.weight": "9292783171dbe5eb689d17c9bda11e537f0e9b328fced6986c938d61ed590e81",
|
||||
"blk.31.ffn_gate.weight": "e4766a04bcd8f937ba883c6a144101e546747804ca66c35c97281d6ccb47b566",
|
||||
"blk.31.ffn_up.weight": "cc1e666116f7e6b06736db4aa4b81003c583f54f4d9200bfa48842249940e16a",
|
||||
"blk.31.attn_k.weight": "fc80b57557687504efae7d24265cb7dc39b8f826bb3d897a11783012dbedc44f",
|
||||
"blk.31.attn_output.weight": "215617f50a1f5d9b2250b82f3652b35a9e9aa0ad9ef2b485d73965a14b2b872a",
|
||||
"blk.31.attn_q.weight": "274b4f1dfb0bdec28632705677049fb3e327ce6d9e1f3baaad1560439039982f",
|
||||
"blk.31.attn_v.weight": "e641b8b926f9dfcbbf6b6da1c02555525ac4b1c306d96f20cfbba7d6662c4e56",
|
||||
"blk.31.attn_norm.weight": "b3243c361d4041ddb892ce6862dd5091f57d87357e3c67e177451b85d8baf34d",
|
||||
"blk.31.ffn_down.weight": "0a00cd3ecd5e91624a27f9e239b1de425d5ba3cfff82c256a11a4ad434abf3c2",
|
||||
"blk.31.ffn_norm.weight": "2a0d67ea2bb1303975712243f07273c92fce83baa11b1cd6d8e42e74ea3c810b",
|
||||
"output.weight": "768615f077fb797967844571c58b94d7c399d884d115be3ab4b0154504cae892",
|
||||
"output_norm.weight": "7cc5b7ce10e5082000fa00bfa68af8c7c5da218e59e2c41cf2f1499d40ca229e"
|
||||
}
|
||||
313
convert/testdata/Mistral-7B-Instruct-v0.2.json
vendored
Normal file
313
convert/testdata/Mistral-7B-Instruct-v0.2.json
vendored
Normal file
|
|
@ -0,0 +1,313 @@
|
|||
{
|
||||
"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.attention.head_count": "32",
|
||||
"llama.attention.head_count_kv": "8",
|
||||
"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",
|
||||
"tokenizer.ggml.unknown_token_id": "0",
|
||||
"tokenizer.ggml.scores": "e3d3eea80bb41a1213f2d0aa3e8a38581d1f19323be77dbd779c9c7e3b72e676",
|
||||
"tokenizer.ggml.token_type": "6040635e6bd38d98af06698feb75c1802bad35180ee6ae0a503e38c0f60fd71e",
|
||||
"tokenizer.ggml.tokens": "604ac4bfbd019e430d7b6cdf18c6c0cd5b967900601f0307f714ec7773aa5ca6",
|
||||
"token_embd.weight": "cde834ccac5e94324b25cb81b02d27312cac0c551b55a7e1d555d90bf6cb6e81",
|
||||
"blk.0.attn_k.weight": "458bfdd9715c66e017c2447b1ed3c582963a3111479314e664faad8c914f42be",
|
||||
"blk.0.attn_norm.weight": "e1fd60b95f713bae7b7e3ca933c64ae6c9cd1e8d808000204bbfdc19f0ba635b",
|
||||
"blk.0.attn_output.weight": "df13b6a157d9d4f96c53b012b3b9bcd207d0c94144cbd22ae3ec13bb07d6c373",
|
||||
"blk.0.attn_q.weight": "13b4126b4245bf06c915a93317c42b8174e05053535ec99dc576541e4cec7c25",
|
||||
"blk.0.attn_v.weight": "5b1781d3a341214511b27eb4e268674ea3ea829dbdf8ae5a6bb89b3c0b33fafd",
|
||||
"blk.0.ffn_down.weight": "49186f5d8148d316b07458841d13a2e66587f4af69b776188a809591ed9c070d",
|
||||
"blk.0.ffn_gate.weight": "4397e30ece09136f00f4ff84ff49e5241b765a374deb8c5a12e897e2bf73473e",
|
||||
"blk.0.ffn_norm.weight": "43260589aac3850a779bca3f9649f793bbfbe5db538361cb743b3830217f8287",
|
||||
"blk.0.ffn_up.weight": "fd7ac918240a07566f6967527ffca58fcf433a30b78fdd6d84b2136d4ebd9987",
|
||||
"blk.1.attn_k.weight": "209839566c7d235bdc20565a4766378b6ee8553133a5a3315abe8a85baa80712",
|
||||
"blk.1.attn_norm.weight": "58c52986f7c69784ba327cb7f350923420782bee17fa39b1fbd13839d4005357",
|
||||
"blk.1.attn_output.weight": "5067cc628449682665dfcf59b16e58fe2a9d2a81cb099f0fcd42f4f8670c6740",
|
||||
"blk.1.attn_q.weight": "f410f9f0dd5edc09401af597d02e2a4c727f1502ec3ec3898321617b36c6df6b",
|
||||
"blk.1.attn_v.weight": "d40fa49e07c102c0644e130e7909eaa93ed0d54e2edddc0759e721d58a4e4f5e",
|
||||
"blk.1.ffn_down.weight": "594b1eff6ed4defbdd819fabbe2d48764984f08878a860bdb808511d5a25b8db",
|
||||
"blk.1.ffn_gate.weight": "4cda97541e388a5bb607ce4cc8b3db1da7045830a630e7ba4d17807befcff346",
|
||||
"blk.1.ffn_norm.weight": "66c13d7481be65b97aa474735ddc9674f33d512ddda76fa6fb45c7464b09f1ed",
|
||||
"blk.1.ffn_up.weight": "1adc6de288ba4cc1237833ca8b4eb81107149842e38bc452e18e5cfe284338a2",
|
||||
"blk.2.attn_k.weight": "5420423559f236ab22d85a00849f31e0cc6e9c7dd879de724393d8cd2b379153",
|
||||
"blk.2.attn_norm.weight": "495fe1ab40cc52aa054ddd4f0c2d2790f4326c8d103296b1b38f3b1060db2a24",
|
||||
"blk.2.attn_output.weight": "ccb83e7085381f558bfd65588c525ad2671feddcbc3887afb4038ad9c7aac348",
|
||||
"blk.2.attn_q.weight": "2e8f77478392bc93c2a391f2e0f4a173a952bbab88a7aca099c6ee909726409a",
|
||||
"blk.2.attn_v.weight": "d64512590f3b7ebbb9e77c2eb97fbda90b00d45c944f2b174f03a2cb11007567",
|
||||
"blk.2.ffn_down.weight": "1de5084a05dcaa6b1bd926e83517dbe9ebe7fde79235fe56018b3028b1aa6397",
|
||||
"blk.2.ffn_gate.weight": "cbea526b557f49aad8c976973cf367fcd12175b900f551984f498b9e07e4b7fd",
|
||||
"blk.2.ffn_norm.weight": "530aa49b10c7eae08899d143409240deb95dae4e1d5bf78cea3b26393cff3ba1",
|
||||
"blk.2.ffn_up.weight": "13a5fc19b96b4dcc1e9bd01998c8272ebe52034c1933ed123a506b711fae9a5c",
|
||||
"blk.3.attn_k.weight": "1913b63a73305941d8cdc472e7f101c633d3357a78602eac0a4b49a744261075",
|
||||
"blk.3.attn_norm.weight": "9c11bed5ab41f4adbfdae4ead65b525c8f19443e656a8c61ba412a4e1ad1193b",
|
||||
"blk.3.attn_output.weight": "bb0b42c1d34779c5943272ed71f1dbb31ad8edd75f8bcd5c868f88505ac3a610",
|
||||
"blk.3.attn_q.weight": "3461a1fe4e49f5319ea047cae98ccdb46528a3ec23831183fe87610b48c94948",
|
||||
"blk.3.attn_v.weight": "82aa30be6a61526a41fb79bb28a2617416f5909f0477aa9e95e16be9370fcb38",
|
||||
"blk.3.ffn_down.weight": "68521011ae03f5e3b0966127111afa8ee9f2eaeeef8d3a0b86b633e0332e9fbf",
|
||||
"blk.3.ffn_gate.weight": "1e89e26338fd364bb679695968c65106382f15ad55c95cbb5ec9bdfeb766f432",
|
||||
"blk.3.ffn_norm.weight": "c81932529a5a8c417c27b888dbe95fff8b447c2ea5f6f560444ec5d50b93832c",
|
||||
"blk.3.ffn_up.weight": "305021735afd8669afefd713f56137248d5e817e60471a112ad06b7fa07ffe88",
|
||||
"blk.4.attn_k.weight": "cc26ba5c5c28082a79e6abfe61186029e80b145252ca6a7924c437f0bcf2d51b",
|
||||
"blk.4.attn_norm.weight": "302d251fdcc91f7468cf33f80b49484251d8917d7018ad264ab3a85c8ecf9ddd",
|
||||
"blk.4.attn_output.weight": "a012f5bee3520cd4ce51f0076c132ebc3653309f304032ad051aa308f55f36de",
|
||||
"blk.4.attn_q.weight": "3c8d607e447f5ef21e73af71e3c0d32fae16f91f31faae34ff06912cf9cb68fa",
|
||||
"blk.4.attn_v.weight": "49f6c81a634ce46d71c2350206ecbd231b1732af96e4e4e67693c41a07e007d8",
|
||||
"blk.4.ffn_down.weight": "e89504f311a4a34dc819a67b761022f14d71c43df3ead4f892c87aaa8e9f0adf",
|
||||
"blk.4.ffn_gate.weight": "18b22f079a2fbaefe3572eec61fdcd996fd747724e2f0ff4f08cfcb43eb7bfb6",
|
||||
"blk.4.ffn_norm.weight": "22415a492c168a0878912b05c854a631228b01c3ea8842e1d75989ec46c18a65",
|
||||
"blk.4.ffn_up.weight": "f57379eae2874d8853f14ddf0f0fcc4ff1338574d5ed5d7e88331d5fb84f5642",
|
||||
"blk.5.attn_k.weight": "d627af853c40bddf9762ce3988008c1ff17f2686fa8f73a0b5da38010147c316",
|
||||
"blk.5.attn_norm.weight": "9ce01092c7f7f1c3ef72d6b794da12d77aa1f6a24fb96ba1b9bd5a0bcc3e2443",
|
||||
"blk.5.attn_output.weight": "0388da8064c4b6b795ce2d8079e8a36535e82b2c9cf794e38ce8ae460aae726d",
|
||||
"blk.5.attn_q.weight": "039b7ce1c909761fdf475c06cf14cabe5a90199282c89e4dcf460e95a4b6275d",
|
||||
"blk.5.attn_v.weight": "c47bfd8d2496bdb6e00e03b903e15fd0ee806a515094ec257e43cc433147ab7e",
|
||||
"blk.5.ffn_down.weight": "1d62e6708974bae318cbf00a8bf621d9ba0537e549ce4710a536520a8d14168e",
|
||||
"blk.5.ffn_gate.weight": "8b42b1b11c92db19985094cbb50434e3a7c9cfea71ee6f21ea79eae7c49284a5",
|
||||
"blk.5.ffn_norm.weight": "e0bc520f1505e687ec391d632a381d38d8ebcdec19f614a11a2000ab573e8b7b",
|
||||
"blk.5.ffn_up.weight": "8cdcd17d2ea89bb9ab902dbc6bf3f827fa4ee029c6bf19eecbdefd146d8b6f2f",
|
||||
"blk.6.attn_k.weight": "5dc6bcff89794d1756bf57ec665b58622d9352130d31082a6c66e1a079f99932",
|
||||
"blk.6.attn_norm.weight": "13b26008abe0f119b5104b9d78ebd5e797d3cdd68122b93d73a3b4831a54d085",
|
||||
"blk.6.attn_output.weight": "f5a49917ea70c3fb311ccfffbfafa63ab18416a5d55e5429b70ce8bfba57c075",
|
||||
"blk.6.attn_q.weight": "d9c2f652c87dbd09ec3822e12876648fa32e86553ac25afab723b1cd9f8cef90",
|
||||
"blk.6.attn_v.weight": "5ecc5fe67609a35151011cb526f45c56fc0a999079ae0ff37c755ca03c68c555",
|
||||
"blk.6.ffn_down.weight": "0ec125ae0ecb2d9277fdb1b04f17efee94e37d0ae37311057c212ca2db3fe6d1",
|
||||
"blk.6.ffn_gate.weight": "fa4d6d38355ee8aa3b80b476d65ae7e343c9b7770d7b097fc848ee8a6e091d1f",
|
||||
"blk.6.ffn_norm.weight": "30e8f7defc627532e1739dc76d31223d45767391a431f925b63dabe334b0f392",
|
||||
"blk.6.ffn_up.weight": "6b97cc32b290fa9087806b5d65aa6dc1760737730c8c71394cc4f30c2157f9ab",
|
||||
"blk.7.attn_k.weight": "0231cb127cb7c3714cd72b8f39343891d7715a9bab2237ade9e7bc5f4ed2e68a",
|
||||
"blk.7.attn_norm.weight": "7c3187f07eead7d219d98ab2daf87905e88d5f1ace109b6f5fa55dce3914981f",
|
||||
"blk.7.attn_output.weight": "2f30ad972c284ae7c8eb0482053433495ebe8fe9c5ee2c28b4bc4ed1f33050fe",
|
||||
"blk.7.attn_q.weight": "3a2b4b8d61cc9956d304fa9f82a9e65b4bb9fda2196670b16df7e0d8c43eff2c",
|
||||
"blk.7.attn_v.weight": "d2aab97d0dcf0f61dd2f32848f7a8a99c423a4948a660a660a03a546972b8db8",
|
||||
"blk.7.ffn_down.weight": "2270d520468c5549cd30023ff9c452a277058310104c4239a616373fc5a94387",
|
||||
"blk.7.ffn_gate.weight": "4134a3ef71b3eac8f76b6f1a2e58625b3bae48081f175994bc3ed7d8b0d4f2d0",
|
||||
"blk.7.ffn_norm.weight": "42df4abd4b8769b16f3930068f96960af1b061f1aeb7505384f272233b2badff",
|
||||
"blk.7.ffn_up.weight": "c920549054ec16ff8c73a72f5d837cf4e11885e44db57c1c1c584c18fbd7a9a5",
|
||||
"blk.8.attn_k.weight": "01c609bd3bf31ce65688f1f640ee413740e821330134d4ed1877a3065d1527d5",
|
||||
"blk.8.attn_norm.weight": "48857411f769b00290f4e4f2e593e092781fdc2503f80c1e3eeda1b85a20f74d",
|
||||
"blk.8.attn_output.weight": "90fb273f8df83744554bd59236515c16c5a5a698ca3fbedc17cc89ddcee354ff",
|
||||
"blk.8.attn_q.weight": "ade617ac4653c7f00593dbb51837a468afef20a14eaab3780fb96ac3d6714369",
|
||||
"blk.8.attn_v.weight": "c2c37496494864fee5c527d1fe1f88529d31c73f9cbd02ef9b2e9b23611ea50f",
|
||||
"blk.8.ffn_down.weight": "2da58572e9ad79087c03cbb0c23c9ef69f93ec221fd5fe4ed92fb93871d23ffa",
|
||||
"blk.8.ffn_gate.weight": "4483294e628edaa4901708e73e92c917bdd93b780fa01aa74aed57166f2bbf0a",
|
||||
"blk.8.ffn_norm.weight": "c0cbb7a4f8123b62f0c4652a687f3b394802bc32870dc446eefb709e42043a7f",
|
||||
"blk.8.ffn_up.weight": "9eaf8a2060cb9224cd585997cd671866c4051ad885c2c6d9fdc7056c2a5c0d89",
|
||||
"blk.9.attn_k.weight": "5dd36c45fbc9c50fd35c36cd75576288506971eac5c5311d4f5c16ef60099645",
|
||||
"blk.9.attn_norm.weight": "3c8ca64f2f75ed7c8fc1da010c23be787648139a96ca0ef3ad10be7b14942b8d",
|
||||
"blk.9.attn_output.weight": "6277e1f833024f53c409be919ec76d34464a78b278c8f9dbf79e777746e3b995",
|
||||
"blk.9.attn_q.weight": "87352b70d9e328c2d51d59090cf5ea5a046529864a890d0bc8986447a0a5c006",
|
||||
"blk.9.attn_v.weight": "2efdf01161d7a82a9117cc2d87d37dba5ffefcf730781cb94fcc95130e48ff9e",
|
||||
"blk.9.ffn_down.weight": "e7658a2ca984961c7ace16acb679387bedb1fef656b5330bbbf588db19673a75",
|
||||
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|
||||
"blk.24.ffn_down.weight": "cdb8540c32b1ab988f984484928d39f6841f2131c1cebe90ad9456737fccbcaf",
|
||||
"blk.24.ffn_gate.weight": "da2e0e913648b5526bd2bbb344038dd067639343aed3b413662b064b0db7556e",
|
||||
"blk.24.ffn_norm.weight": "8940bd781c610d75eb2be63cfc8d869a3af05e53c963dc7fd4c6f653df5a80ab",
|
||||
"blk.24.ffn_up.weight": "90cbac2a58801abe11ed6c24560aa4acb949f79429f2aa8ff129ac05868bb87d",
|
||||
"blk.25.attn_k.weight": "90607131e36998e990ce718ad05cbecd1bcaed010931401ce6baa3b0d93ebce6",
|
||||
"blk.25.attn_norm.weight": "fbf679c85656c04a6cf8fedd5412c1ace22960e6c2d47f2d43997827811fbb97",
|
||||
"blk.25.attn_output.weight": "08412724ee7a2086514406e6f68fb9f622e10bac25b0c373b294709f4b09bd2b",
|
||||
"blk.25.attn_q.weight": "9c1238e98a2747654a0d4371d3e7ea8b979867f609dc42482544f25591e85c7f",
|
||||
"blk.25.attn_v.weight": "a57796a535c6cb09581cbafd6a91dc14adc8cca2a2465a7ffd0aec546cd84074",
|
||||
"blk.25.ffn_down.weight": "f7e34e8a6391b480da08b52640613ccadce268373934b409759743a1735b74d6",
|
||||
"blk.25.ffn_gate.weight": "b8d0b2f4612678b5ce42bd4a683f8024514b75fb5ebf6b22c600811e95582ee4",
|
||||
"blk.25.ffn_norm.weight": "cde1fdba2369d315f3c6940a997c471ec891924e642505db580d732763bd7b75",
|
||||
"blk.25.ffn_up.weight": "72e700c32ac8b9c47559c2222e45888a480b527ea512075423c5dc01678e2bb3",
|
||||
"blk.26.attn_k.weight": "6ac83b3414ae75bf3a9055c32e49d2c40fe611ab21f8444f03d2f465d18122c9",
|
||||
"blk.26.attn_norm.weight": "55f9d6dc9d75973dc75136ecb9d991b4398097ac133070873fb96ec76a6f60bc",
|
||||
"blk.26.attn_output.weight": "ebc4fcbd15b33263e50ed2ad45740867cce15bc90e1216623babcb1820734509",
|
||||
"blk.26.attn_q.weight": "080f057521073e412936fe3fee64fd574c8128fa4a148b879d3e598fe4954581",
|
||||
"blk.26.attn_v.weight": "0fa2830d6746487ac91b243716e4302361f891e4e008eddd14abec47c7809d5e",
|
||||
"blk.26.ffn_down.weight": "cb2ab8af1653adc57111ada49d2825c6995e338c8208455b92de10e580f60f31",
|
||||
"blk.26.ffn_gate.weight": "231ce30966086bce2dc0e0afd34a22a1958cfda7a57c41b3b8e9444c5dfde8a6",
|
||||
"blk.26.ffn_norm.weight": "35d959d25d17b00617590f5d5831bf705c385c51e46297a14375a700effca6af",
|
||||
"blk.26.ffn_up.weight": "367680c8d332538b467d1ef87cfeb36cc5c6af564c5023c5fb50e728e3438287",
|
||||
"blk.27.attn_k.weight": "0bfcb351c6d17aeac5b55a915074fbdf00f11c4bda98babb196ac8804805746b",
|
||||
"blk.27.attn_norm.weight": "5d598a88c2e75ba59dd7ba4fee940bdec92d72038f1286536d2dfb71d008a09c",
|
||||
"blk.27.attn_output.weight": "23a9da7347336479f6a10ded14cb3f46e06b5bd56dc4b0fbc526c688552ec840",
|
||||
"blk.27.attn_q.weight": "b83319dba9055f069208e9c9d66da08bc6874f23e575288fcd81697d1777aa54",
|
||||
"blk.27.attn_v.weight": "36ed34ccb2f36fdf16b2c2dd225a98ea6b7b0e376e7791191136ccd7bd7a4add",
|
||||
"blk.27.ffn_down.weight": "5488e1d3a58c71b5e9ddda430540b4776b268cfe1457cbc1c2622dedd9e4526e",
|
||||
"blk.27.ffn_gate.weight": "4ff48011ee0bac39af704849d9132a2410392c87a509c684f2062f6b76b498fb",
|
||||
"blk.27.ffn_norm.weight": "32afe99675983da3de2961d1b5ca41c98970a356823597fe29e91f6e86abf0e8",
|
||||
"blk.27.ffn_up.weight": "1eae3088a75629571fdbf6a20f141bc2bb2ed3f5ba2b9fd1d949f80695e442a1",
|
||||
"blk.28.attn_k.weight": "c4e80af714962d6f9040d2c09f316f4a1cbc3a2e994e19902d7c653cf3c73dba",
|
||||
"blk.28.attn_norm.weight": "c1ecf85dedc1c83d5d402bb7c94fb8b9c11f1a3e5f64e7680f80912d4a560794",
|
||||
"blk.28.attn_output.weight": "72ba47c061b21f5ebc5213a455eaf6fc49c8f8e04ff9ce37e6ed4921b629161d",
|
||||
"blk.28.attn_q.weight": "c4abc47234307f44b8ca789aa6668e298158fa4b459b2c1e84bd581806591cc1",
|
||||
"blk.28.attn_v.weight": "aeba950799d4950e491ad0fcbe30334e39b8975177990a2cb339031c45ac153c",
|
||||
"blk.28.ffn_down.weight": "4e84ce382a37b994fb8608df451a60040559e3f4f3241c3b3cb8989a3ed50d83",
|
||||
"blk.28.ffn_gate.weight": "04df157acdc8e8534ad60acc2d2a4dd3a7a6610f6382535ec728994fa6f83f83",
|
||||
"blk.28.ffn_norm.weight": "4d0386dae2bd1c1a9d0f9730718333e3a486c3bc6a5c5d482193c75d39832c80",
|
||||
"blk.28.ffn_up.weight": "fec60bb0a3daf182a14bd8311fe6dd1e3fd020c5fc273e2549cdb1a2d6b79b05",
|
||||
"blk.29.attn_k.weight": "b0532a263aa5a4e2a7a80adc83fc5dec974493bd18da7f953e7ebfc3f3a19aae",
|
||||
"blk.29.attn_norm.weight": "593fc3b4000c35b7a59dace09ca1756c08be0105b2edd354a0e1c16c82898859",
|
||||
"blk.29.attn_output.weight": "315b896f9f0cbacd0ca8937384c3a3a227efa908cb8c3a9125ec00c480e32b9b",
|
||||
"blk.29.attn_q.weight": "d482d45386d4ad3394f08e9dff233ee3a70d0427d65c0b8fa05905da7e25ca53",
|
||||
"blk.29.attn_v.weight": "cd3b5a6e2852da796902930a6a84bc87fc6a7c7bf51f8fc23758d12a39013b36",
|
||||
"blk.29.ffn_down.weight": "5b3dba6f9753bd1b1ebcba65ef5373dd62c38e755c44b7231b95d93d45761f89",
|
||||
"blk.29.ffn_gate.weight": "8610d9d2db15c256243ffcca3ffd31786d0ada0af0e7c7aa3fd20524370ab036",
|
||||
"blk.29.ffn_norm.weight": "1a2ef2d38b7ac3e51190b9ccb8b6552ba83ab290e523356a7f851ddb35dedca2",
|
||||
"blk.29.ffn_up.weight": "a5fdd15811bde16dc27677cf1a4c97daab4c28cb12a9530f1a0e573134fdb69c",
|
||||
"blk.30.attn_k.weight": "1efeb0b5f4b45a85cdf47300f892ac77ac1f38000ec3653565d1303d1fb8c743",
|
||||
"blk.30.attn_norm.weight": "c73934c182c7fe80838ec1d0b92f50a583f75f7a3d78d822f009b58ad2c80e65",
|
||||
"blk.30.attn_output.weight": "3a0fd89de2d274614750345d827a9c886a4f97b343a13cdf680390505df596a3",
|
||||
"blk.30.attn_q.weight": "711e113362bdb067db843c66236704eb1cd3fc5f40e3767143e96d510686ef4e",
|
||||
"blk.30.attn_v.weight": "82b12a9a74fd3d91b73cc2e841e2b3f0a5197ccd2998afa17020995f880d2267",
|
||||
"blk.30.ffn_down.weight": "af9f4b1287c0d824ae22d6e335d19e04a70135b835be7caa2435f1d85e931993",
|
||||
"blk.30.ffn_gate.weight": "e2ab3e6f15f5c50fca66c084cb6a57a2b6b82406d65150e82ea0437b93dd9a46",
|
||||
"blk.30.ffn_norm.weight": "c1b9c325c83f00e177386a4d7e769945f2995e60950c4a576c0a2c4ab9703d04",
|
||||
"blk.30.ffn_up.weight": "9b94a21efd419715d82071b490d3b635cf1e8da080620dcc39e5bde976d7e9a6",
|
||||
"blk.31.attn_k.weight": "0db0d82e3ddcc2c06209f5f013e1d72a84a996c40bf00186be485b909cc268e8",
|
||||
"blk.31.attn_norm.weight": "2b8b7239471f57140c5cdfe06bd224a4f6326282f99736e44fba4c7b120ac101",
|
||||
"blk.31.attn_output.weight": "a310b048840cc3ff2be4b84796340e8e2cdf05ec89d14bd3655c109b2bfa9fcd",
|
||||
"blk.31.attn_q.weight": "f45e0cd95645175ea82813455356d171838539bc3f7676d877c698f2af0a0eda",
|
||||
"blk.31.attn_v.weight": "8bde008e809112aa7e7c23e9c3099087bcc557313b01306c87efa0a4a30805ba",
|
||||
"blk.31.ffn_down.weight": "8266fec7e203fbfad7033120861e44984581ff8b6851d01dfb7b81c5d8fa90ec",
|
||||
"blk.31.ffn_gate.weight": "b73bc0aa5baf006d9ef6403104891b8133671b0992398fe038380b67e0d7e2cf",
|
||||
"blk.31.ffn_norm.weight": "9c62cc27a7b6017c1df8ad49bff249a8245e8895c6754f402cd44623fda83268",
|
||||
"blk.31.ffn_up.weight": "5b970a4694ea3171a0167f6e1636d9f00268bc1c9640430ffc35218494884adb",
|
||||
"output.weight": "74fa0ef08c57a30e633e7117b1e9c805f833e2e5e21434bc79ddf9c92c6d7330",
|
||||
"output_norm.weight": "59b8a59fd3fbf39353506116e43e5e76edd0cbf2a2873d869da4cf27a04997c3"
|
||||
}
|
||||
348
convert/testdata/Mixtral-8x7B-Instruct-v0.1.json
vendored
Normal file
348
convert/testdata/Mixtral-8x7B-Instruct-v0.1.json
vendored
Normal file
|
|
@ -0,0 +1,348 @@
|
|||
{
|
||||
"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",
|
||||
"tokenizer.ggml.scores": "e3d3eea80bb41a1213f2d0aa3e8a38581d1f19323be77dbd779c9c7e3b72e676",
|
||||
"tokenizer.ggml.token_type": "6040635e6bd38d98af06698feb75c1802bad35180ee6ae0a503e38c0f60fd71e",
|
||||
"tokenizer.ggml.tokens": "604ac4bfbd019e430d7b6cdf18c6c0cd5b967900601f0307f714ec7773aa5ca6",
|
||||
"token_embd.weight": "1d1d1d39a867d5a4bfb32792a47247d2638c10c95a6259391d02843583505cc4",
|
||||
"blk.0.ffn_gate_exps.weight": "2e5cd43ac3f26c44f071926ff6c3f239ecc52a34bc9a5b5906d3d4c1bf2fbbfa",
|
||||
"blk.0.ffn_down_exps.weight": "a4dfc7e7c96e7402eb70279601675b956bb7331da8101e63fe5c0a611b6972e5",
|
||||
"blk.0.ffn_up_exps.weight": "2d5d87b378b2319c344ed2c642598b6f7cb6beeb582a8ea51abc9ae690d473c3",
|
||||
"blk.0.ffn_gate_inp.weight": "a46aaf5aba7401ce6e41f158242b4879d34901661f3ede85496cbd0ce79d6314",
|
||||
"blk.0.attn_norm.weight": "3fe37d913bdd2b65076bcdd6efe64a37b0b03cacbb1b80b9f7089068aa35f38c",
|
||||
"blk.0.ffn_norm.weight": "5e14308a3c894734eb204c8f558bdc817e94bbd5b4e9cb4094e91ba388c8f7f2",
|
||||
"blk.0.attn_k.weight": "73d943dcac0911e87bd771f4aa1c901e1bfe1aed293af06e1a67812159859f67",
|
||||
"blk.0.attn_output.weight": "4c5f754c855e262e8d4c94c6fbbb57af06399dc0e170d7d99a1a17fc9aab9227",
|
||||
"blk.0.attn_q.weight": "d6fd7403c873d49c05f6f03208f30d99ad34cb3b71c9990c47334d502a8e4c7b",
|
||||
"blk.0.attn_v.weight": "cf17cf64b2d683bd9de6cebaf60e5c264df6fdc38fe719dde9d54c80334f6366",
|
||||
"blk.1.ffn_gate_inp.weight": "0d524de81cd915816b4e714bf595ad6946a9130b3de731cd89428b2781230809",
|
||||
"blk.1.attn_k.weight": "2ea47f412992b374c70674730fe84700e0c8cce177086ce9b6635e42408964bd",
|
||||
"blk.1.attn_output.weight": "b4b2520794d54113e86c8ff678eacfc62e35be4395a594a6c8c22b4383ebcc0c",
|
||||
"blk.1.attn_q.weight": "5db930c98c4f91f6eab57eb974c72210b158e366d23d6d2890b2759c053bee33",
|
||||
"blk.1.attn_v.weight": "079bdde09668394bf7af9f8bc175017b4f48f0ab64e6dd855a4d7561d1693c0f",
|
||||
"blk.1.ffn_gate_exps.weight": "146a62de19f9ab093deb101f9640534ffc3dc40d69f508be12fc0475d01b0c7a",
|
||||
"blk.1.ffn_down_exps.weight": "949da94a3c0f375160672a979e85f7def284264b10d48d038238aad5f5ece793",
|
||||
"blk.1.ffn_up_exps.weight": "7016a3f467d9e3f2f4b4019579ed86b757469cd367f2b225483305376b4bb3c1",
|
||||
"blk.1.attn_norm.weight": "1614d1e6ed537737275eb888666c7bac533f4eefbe73dec92b591045ca9e1afd",
|
||||
"blk.1.ffn_norm.weight": "405a455fa7d1ec36894652ceb554bbcb09a07fd6405f42741e66dc4a4665c19c",
|
||||
"blk.2.ffn_gate_exps.weight": "90d5003fc7421f44220c0842d43128955e91488f6f785fe570b62d81b719e964",
|
||||
"blk.2.ffn_down_exps.weight": "ecdc2b5a8b504ef0a7833acff47d69b0c1fa9c22126de1bb120ff5e48c3d6e2c",
|
||||
"blk.2.ffn_up_exps.weight": "2cbd9485a32460d315eb50a2f3b00863fd77245bfe885b7565efac1cdb1f191e",
|
||||
"blk.2.ffn_gate_inp.weight": "0d0a17a1a2c7a61f2cca49ecbb479154dc93a870873257bc4f225e7607f2e2c2",
|
||||
"blk.2.attn_norm.weight": "b2e4c5a977f87a6f880896bd73596234c9b83622fa0d7add5892501e3155913c",
|
||||
"blk.2.ffn_norm.weight": "0ab875b4280afa922376cfc7b9aa3f7071c9432ea1254091ce7de3749df0e8e6",
|
||||
"blk.2.attn_k.weight": "bb884af51fb51550acfef54ccf1b58ce8284e587806e6a2f88c8265e1ad05a5e",
|
||||
"blk.2.attn_output.weight": "0f03099ba1ef342ea61af9cd71d028123bbd8b1dd7d7fd9b509aef77815427d9",
|
||||
"blk.2.attn_q.weight": "8fad0d29eb4c9d24e564774ee3316b9eb7a4c4985e4567111d2c836c830f6cf3",
|
||||
"blk.2.attn_v.weight": "fe04c847ff677632401a94e7b6b6fdca60391ab21cb23bd791533115de6303a1",
|
||||
"blk.3.ffn_gate_inp.weight": "29e3aaa724590c070e614af8288939603d2641b0ef11e8c0f476bebb2776673c",
|
||||
"blk.3.attn_k.weight": "231cc5631def10f7f292d8862d6125ff555164cd70480ac76362149fad204497",
|
||||
"blk.3.attn_output.weight": "86467a605c62852e05fda1a7ef43150df2cf715fe59785dbcba09f1c27cfa086",
|
||||
"blk.3.attn_q.weight": "901822402453922225c2d6ac79616691d48217635d5ff7338daa971d5ddee210",
|
||||
"blk.3.attn_v.weight": "27030784f44375720df2f090933645a31a022d3fb3b14573e5ca0b78f44070c1",
|
||||
"blk.3.ffn_gate_exps.weight": "231ba59cc0b988d125d77bf627aa3f04636684870af88f081f3944b48a160d86",
|
||||
"blk.3.ffn_down_exps.weight": "530c3ab44ae4d66e8afa4d10c153ba5dfcdfb7321989a988e62e9d12e7234625",
|
||||
"blk.3.ffn_up_exps.weight": "b85c2d4d9d11332e702b3c0a6610d4f525f9a93e5d12f5c7c55c592c40755e75",
|
||||
"blk.3.attn_norm.weight": "05dbb6d88cfa6b199f9d705ccbda97c0ef13f9ec875c595398a1a42d009a4555",
|
||||
"blk.3.ffn_norm.weight": "6880b1c27d46969ce36fac049c05dc8b89e4bb47dc89df357e32df7e18fc512e",
|
||||
"blk.4.ffn_gate_exps.weight": "a883b4f225b760c5a2f6605dc5e2167ab85bb398c70bf64ceb539fcbd6128dcd",
|
||||
"blk.4.ffn_down_exps.weight": "d291bb656aae77947d4b525e2819bf4112afece53ff31de9dab999af1f65f9c4",
|
||||
"blk.4.ffn_up_exps.weight": "38592afb8ba3dcfb26970f906174f7d3fa62da44fa4be4fc6912a19030ea9164",
|
||||
"blk.4.ffn_gate_inp.weight": "1596cb74e8fd6c3080b937b06468bb397b0dbb661e6d180a6bcbdc43e8bfd0c6",
|
||||
"blk.4.attn_norm.weight": "f90c83c5ff4366281d283384efc941620542b9cfdea160d678dc54a75e33f758",
|
||||
"blk.4.ffn_norm.weight": "d28d8c49d1746b7cc085562d1074905fd14023844de823dc4fb22202bb280790",
|
||||
"blk.4.attn_k.weight": "792bbf412cc357140fdaba543e547a9b2f7582919e307bbd9a80c7d6d8f5f1f9",
|
||||
"blk.4.attn_output.weight": "d98e4a062d2631d9c315f1990d5f6ca9a88e7e0e46387f611ccb0353f876aa12",
|
||||
"blk.4.attn_q.weight": "1a11a55a91d9f748a72176ff6b1c174844df406e00d1b66b9aa64dc6ee4bcd1d",
|
||||
"blk.4.attn_v.weight": "04cb3c02b12a6313c7ac7044513441083d534fb4c5a3f63bbaa58f7edbd2fadb",
|
||||
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|
||||
"blk.31.ffn_down_exps.weight": "5579cb7758c1600b19d1f540deffe081b575962e37437b3b2efb2fb0a2924e40",
|
||||
"blk.31.ffn_up_exps.weight": "f2e7c005276b3a001fb40753f027fa10b4d5a346f43cf4b4bbdeec6e74e1cf6a",
|
||||
"blk.31.ffn_gate_inp.weight": "89885dc0e30b6b16a90c0331d7fa3174671e941364e8102d934f02132237e61b",
|
||||
"blk.31.attn_norm.weight": "99e4e9bf86a9edf8c404153a7e8a82324ba79da462622196e2faba161bd95172",
|
||||
"blk.31.ffn_norm.weight": "55335997cf6de781bf332b943de96ff4646966b05d9fee86b76ea897e27b6ca7",
|
||||
"blk.31.attn_k.weight": "cee570762b78da6316b637892cc4b080e40f57af5551ffb1866b9a8e80e96628",
|
||||
"blk.31.attn_output.weight": "fa321ff55ec7819ead7b819fd45215262f39744569765ba2113c989c03588802",
|
||||
"blk.31.attn_q.weight": "9e2c409b878f8a2a1436874abf428fceb1c534b21f9ad4dd6f532b8a469007f0",
|
||||
"blk.31.attn_v.weight": "a845d0be68ba537b4a775bfba4d897faf7c82a811a2612b0b7420cc4f3574cb8",
|
||||
"output.weight": "16101cbb74b54cda9ebc07ca3c762e3263a56efb3cc011156184b95807d7cf13",
|
||||
"output_norm.weight": "d7aa61585baedd60157aafe157930785742c55989c288573566a971b02423564"
|
||||
}
|
||||
188
convert/testdata/gemma-2b-it.json
vendored
Normal file
188
convert/testdata/gemma-2b-it.json
vendored
Normal file
|
|
@ -0,0 +1,188 @@
|
|||
{
|
||||
"general.architecture": "gemma",
|
||||
"general.file_type": "1",
|
||||
"general.quantization_version": "2",
|
||||
"gemma.block_count": "18",
|
||||
"gemma.context_length": "8192",
|
||||
"gemma.embedding_length": "2048",
|
||||
"gemma.feed_forward_length": "16384",
|
||||
"gemma.attention.head_count": "8",
|
||||
"gemma.attention.head_count_kv": "1",
|
||||
"gemma.attention.key_length": "256",
|
||||
"gemma.attention.value_length": "256",
|
||||
"gemma.attention.layer_norm_rms_epsilon": "1e-06",
|
||||
"tokenizer.ggml.model": "llama",
|
||||
"tokenizer.ggml.add_bos_token": "true",
|
||||
"tokenizer.ggml.add_eos_token": "false",
|
||||
"tokenizer.ggml.bos_token_id": "2",
|
||||
"tokenizer.ggml.eos_token_id": "1",
|
||||
"tokenizer.ggml.padding_token_id": "0",
|
||||
"tokenizer.ggml.unknown_token_id": "3",
|
||||
"tokenizer.ggml.scores": "0872465d173867d755d3ee728f882b9dc2057a0bfd596fe1e3d131522f1250d8",
|
||||
"tokenizer.ggml.token_type": "485e40bf3d715a4764818fc097d6a2a41db872d82ee714bc500872a3437ff48d",
|
||||
"tokenizer.ggml.tokens": "c6e66de1841f04de8b8d236d461ab720a4c9b9b5414dc293a09c6e10eab45fda",
|
||||
"token_embd.weight": "17b87ab2c01c80657855a5413d0457b4a041afaeda0cc785080e44e2f04acf07",
|
||||
"blk.0.attn_k.weight": "28ac0da05754ad2714ae95da28a5ad191192140b30b8fd22d108d4700c9d989f",
|
||||
"blk.0.attn_norm.weight": "3f9d5675d1ab0eb8a816719dac9fab81f2e95c52be02c34263339acbc087febb",
|
||||
"blk.0.attn_output.weight": "703295c2c63990ff896778685c678f145298886f680f3ed5dc2a7ad54c293265",
|
||||
"blk.0.attn_q.weight": "69c2d0e4870e9d722a190d356203c9605575a16863466c3d1747966ef1cf5791",
|
||||
"blk.0.attn_v.weight": "95219c9c07b5ffe9a9a01e456d845eef2b11f4fc12c93dbbba479db395444c13",
|
||||
"blk.0.ffn_down.weight": "a2feb5eb3d572c57c5bafbf0ab506862df1160fe40965dcfe4b9fd855c08bed7",
|
||||
"blk.0.ffn_gate.weight": "fcca072c445c31f4dc4d5dfaa785b1bdf7271342442099b74fd17268b5829fbf",
|
||||
"blk.0.ffn_norm.weight": "7621f95dbd245cade6fffd6b08797d69d8e3954e960f0b5551b90d967ab95448",
|
||||
"blk.0.ffn_up.weight": "14a9bcdd451403c67136391e1b6e53b3b1830f00199bd911dbcc56d8749c14f4",
|
||||
"blk.1.attn_k.weight": "c70f73c5df20579cb44d971164b48b5f0d8d5abdb38b381e7a8b880ba12aa406",
|
||||
"blk.1.attn_norm.weight": "88b6b91f93a1ef83425a7c7dc2a2fbd3b22704a04c64a80061df376ac8c33626",
|
||||
"blk.1.attn_output.weight": "f031a537490c452be3b3bb51e6b7949a636405756e160976a1c070a792ea00ee",
|
||||
"blk.1.attn_q.weight": "bdb23214b1cf9cfd30f863a0a5868e52c6809d93b7e8f44df096a94204d9896a",
|
||||
"blk.1.attn_v.weight": "e9bbc0b05f2c872fb1403f8f938cd1612b502229ee401f12593b1164c61acc00",
|
||||
"blk.1.ffn_down.weight": "5ff53811038b661a7b8f2bfdf213bebfb185ec1a6060b662f063714f33584d79",
|
||||
"blk.1.ffn_gate.weight": "205085c8c951a5c7543b1495183cd96028fb49f67464b3e9862a2693a6077a33",
|
||||
"blk.1.ffn_norm.weight": "798f354fc85afce9625f5d10093a585a966831698a0560e6c9b97ce659eb4b22",
|
||||
"blk.1.ffn_up.weight": "db92dc5684cb6e90940e13f4d1da555ed20ba4f8cab1e990ddfd7553e2e91315",
|
||||
"blk.2.attn_k.weight": "ef5ce360c4eed6d00d03ca4761e0f8e4b0af4509978468314be14f3d46621044",
|
||||
"blk.2.attn_norm.weight": "6dadbc05dbd0d3fabb4216affa60a3de1378a82d2859dc90b338cbe70f50d455",
|
||||
"blk.2.attn_output.weight": "6bbf87a966f691bbfd7c8d25629aa4e6710107bd431a667434861febb391edc5",
|
||||
"blk.2.attn_q.weight": "4e575c09ae2de417ce9057ce8b073680e860a24aae13a472b68f101b760752e5",
|
||||
"blk.2.attn_v.weight": "cd33f7f01141e9439afdaf2ea1aaced9feaa335e32a58daa136ebd555d4d96f4",
|
||||
"blk.2.ffn_down.weight": "b970ff1b0b6494165defe2fbfa1d31425766ed71e64de9ec4e66ac3955c8bc5f",
|
||||
"blk.2.ffn_gate.weight": "dbb3e1360402e0e369b101995bb686b73f95d4a7673f061be85d64d15dfb0061",
|
||||
"blk.2.ffn_norm.weight": "bfb7980105d8ac9647710454f57a5cdac50598a0f6f4884e16f1d94b00844687",
|
||||
"blk.2.ffn_up.weight": "50ef89339b275a438b664686f6227dd9b6e43853ed6856ec9e33ef4bbd90bda1",
|
||||
"blk.3.attn_k.weight": "be942ea98151434eebcd2c1da4b00e0146152fe524a530689b1fd491cb833d21",
|
||||
"blk.3.attn_norm.weight": "0df2f218daf609c289fb7c60c5f375fa99c0d4e04381ad5a494a19144edd8e20",
|
||||
"blk.3.attn_output.weight": "c2184aaf86aa2cb8f47be49f60b165834e97205f39c6ee1dfd19fd4411a156ce",
|
||||
"blk.3.attn_q.weight": "4f86e2a0a4221c1c84ff9c409ac89893cb95d7208cf65bf1e98e24e01125f991",
|
||||
"blk.3.attn_v.weight": "abfdb8a60c349dadde641d1afc9542025e24fbf41a3238bfa9675e0b1f1e4b68",
|
||||
"blk.3.ffn_down.weight": "58821a8d87008d47d122427911c6fad5272aca70c448bbae223256a74bacd07e",
|
||||
"blk.3.ffn_gate.weight": "776e051f1a0ddd5c4934e69186683a75ca9a3c8c0f61911bba321fed1dd287d2",
|
||||
"blk.3.ffn_norm.weight": "7f380f29335e28be90bfcfae6f6d69fdf5751211b36d2dd62aa5541ed113e4f2",
|
||||
"blk.3.ffn_up.weight": "fc5ae8d488894cbd4951059675468d227da27871d26e925c9941863841c097ee",
|
||||
"blk.4.attn_k.weight": "14833b078cc4c5137bdd5fdc0538047974ca147a99b0282e1b144440c78bc1db",
|
||||
"blk.4.attn_norm.weight": "0a69957d4a15599fb80ad4753558020804925221457d9a5052926754d3768065",
|
||||
"blk.4.attn_output.weight": "887a49b6130fb6297cf10767207c3dd97191b2cf63723449af9c27bca8dbeda0",
|
||||
"blk.4.attn_q.weight": "51fd577b76764824dd6f0d4891c137ebe4736f591b5ca2793c5fff2be49abbde",
|
||||
"blk.4.attn_v.weight": "1a623c43cf9c509d1b7ea0d1a5c04d0af4809665f9f9e93b7d6dba8c5df178fa",
|
||||
"blk.4.ffn_down.weight": "5d61e8856d8941d2b1fd138116d015f63840d0fa1e31e20e20a5ceca1536ceec",
|
||||
"blk.4.ffn_gate.weight": "06640f7273764f8ca5df7e386547417916b6cd7d565a8343153113239a94b0a1",
|
||||
"blk.4.ffn_norm.weight": "91a6c6c41b894228e361435ecbc5058dca34d4911a23da5b56de219299c964d3",
|
||||
"blk.4.ffn_up.weight": "d016dac1055e36d6a10b6317e57f98a904709ea892ef3194342f4d2f6326561e",
|
||||
"blk.5.attn_k.weight": "987146afe124131500808cc0da33c06d207433656d41df6e6d8c99118a83bac5",
|
||||
"blk.5.attn_norm.weight": "6b354938966f2608a2fb8d0f5b363ed0d8b0967c2ec8d0abd5c625b413042ded",
|
||||
"blk.5.attn_output.weight": "cdcbfe02c6ff79d5326882b017a02099f5af71beedf6b1b3eb4de01e3a844536",
|
||||
"blk.5.attn_q.weight": "b910d0cff781d3efb42eab0a302f46f286b2de717079175680d5b42bf8c309c8",
|
||||
"blk.5.attn_v.weight": "66d3a279f747412f9f4b0e8abad44540c122ab2e811a7ee74c1f33bc36caade9",
|
||||
"blk.5.ffn_down.weight": "c9b0efd2212981f16d956d8571f054b68780ad01f4917033647e359b557a4653",
|
||||
"blk.5.ffn_gate.weight": "fe96b94109ca141c01f6a04788e20783019ca6ec334aa1f3134810bdb499e557",
|
||||
"blk.5.ffn_norm.weight": "aa7b016e832e7055a36c6e20de58ea1936f995f390401fff1c5fc65906064e49",
|
||||
"blk.5.ffn_up.weight": "555ce27c4873d3375394f38ad3b45e3d8848f9d5642dc1602383d0f0a33c2a14",
|
||||
"blk.6.attn_k.weight": "88280d461db324c4f36475ce396793063e61a27283ec64511b0480890fb5b3b4",
|
||||
"blk.6.attn_norm.weight": "af8f460c411f660d33196286d208f1845fd5a2b45f7b56549a4df31e7515447a",
|
||||
"blk.6.attn_output.weight": "dd9996fb0a256e8375ad3917705258a33fce006bcea0f536caae420a77974d8b",
|
||||
"blk.6.attn_q.weight": "7a4841541191e037cfb9b07930c4d8cab451809658b182f0ada6ccde9615c003",
|
||||
"blk.6.attn_v.weight": "ae81e6a592b64d701a9d40233e986039a56cba8d8d24f61aea93c6393cf3078a",
|
||||
"blk.6.ffn_down.weight": "622dd1ce1706355cbc659a8ab2c4509678ffe0f3ad34258e5e25ed2a5d951bcd",
|
||||
"blk.6.ffn_gate.weight": "8389a735c0bd5591010f8ced9805a2a12c749f6df0d3c18ad4d05c2a302e7168",
|
||||
"blk.6.ffn_norm.weight": "621f5346400382474d61358397bd58fb1459b07c53e376e4bca15e08b3f9b3fb",
|
||||
"blk.6.ffn_up.weight": "8d834e4c42f13c251dfee36cf89e12f1bd400680d00d5c2e6cac0459e9ce2f7f",
|
||||
"blk.7.attn_k.weight": "8bd0412de65a3e64901ef8fe6a28c95e116bf39dc9aa22f0126b9d36688e5ea7",
|
||||
"blk.7.attn_norm.weight": "056d8e56be4e87d6dc6f900762f0dc6fde07bfdc50dd85bfc510415e2bba3f3d",
|
||||
"blk.7.attn_output.weight": "27972eda51da53d416ff95aed78149a2c5a287b47d2cd46f2f544ca692ecb3bb",
|
||||
"blk.7.attn_q.weight": "41eca977b9371f7932800c11a9c45b931310196919e2a0651b847703b180fc7f",
|
||||
"blk.7.attn_v.weight": "13c74fd7e07f08883a09fb070a1fe5bbdd2341b4cb8d1cac07c4b637049b5774",
|
||||
"blk.7.ffn_down.weight": "9e75db42468800849a9a7da603d0072c5e86c8ed2b4d8b20a312a51fb86a7a10",
|
||||
"blk.7.ffn_gate.weight": "db6bdc3117f910088aaf7db51f2da63ea5bd933de36af5599c215bfb26f7db2b",
|
||||
"blk.7.ffn_norm.weight": "48bb82b49bfc8679a1e77f282ee182d952db7a3c11be7ef9a102ee2ddd8011e2",
|
||||
"blk.7.ffn_up.weight": "feebea87175817a0f3585ec0af09dc873d94c203581ae97a712eb356d3b49efe",
|
||||
"blk.8.attn_k.weight": "d5640ad71b6af68d88e17bf8e7fc26c907d2262605457a84247dd9afc2884d69",
|
||||
"blk.8.attn_norm.weight": "75b850c481a69083ae09d0207ba7317b37c735a39fcf5fef5400e6c84fb1257f",
|
||||
"blk.8.attn_output.weight": "cbd669dbdea2bdd90f9f0cc97566b3dffff3c56cecb4f47290ceef30da83b2d6",
|
||||
"blk.8.attn_q.weight": "9edcb63087a431bac361822497e6ecdaa06d9ea4a1a754e36da7ba9f8db81c7c",
|
||||
"blk.8.attn_v.weight": "3fb72c2c4f95a83626aa3e30062f9450b09ab37c7871e229f18bbc5cf744633c",
|
||||
"blk.8.ffn_down.weight": "bd69d2c9172974fff154441b237b4787fb53b2d185325442d5048130ef5bc4ef",
|
||||
"blk.8.ffn_gate.weight": "d04689c80553edd011d1cbaa5d570fffa7fa91e88b66cf1352d89ab60b72f908",
|
||||
"blk.8.ffn_norm.weight": "e49984183b735b7f2c4e4730c289eed9394056d2e283a00fd83ea0915df31a73",
|
||||
"blk.8.ffn_up.weight": "8fe62a1ce8e847e567add6c6f6bf2922bc467495b5eb4c116b3cb85b85b3b211",
|
||||
"blk.9.attn_k.weight": "d90904959e5004cf0d6e729c6bff18cc33c094798b802473c1ec55ab8d276183",
|
||||
"blk.9.attn_norm.weight": "79277f290cc07411115d8fa138045edf4a17b3416ab2145409cbe8ab829fd4ee",
|
||||
"blk.9.attn_output.weight": "5a21bf2e1f09a81405025f96d4153ffb630158e17269cff8ffff935c38ceb1a7",
|
||||
"blk.9.attn_q.weight": "51b1d0febc3b350945be4504f55afa4347517bde0f710e1a4b88e6b17e71e7c7",
|
||||
"blk.9.attn_v.weight": "aab7e1db0a8b50a03036356791ffce736ab010d15674c96eaef8049d80076054",
|
||||
"blk.9.ffn_down.weight": "cbf43ec84becb40c9359a181ab0e641fd7faae7d34b549501f7cfb7afdc3d764",
|
||||
"blk.9.ffn_gate.weight": "dce0e8661c778327bed7f03b6790d26710764188aed9dc746e6e05863891fa57",
|
||||
"blk.9.ffn_norm.weight": "6d41642104f995c77bf31122b13237caebda3e7fcccb1367ce91db36b015e923",
|
||||
"blk.9.ffn_up.weight": "82fe4c67bf24e7b2d6f6e05f7b1234c2bf90c3932951091a9066211b8e15ecbb",
|
||||
"blk.10.attn_k.weight": "f6a9ed8fd8d3229b5d03175c413ffc56a07f2ce7236271986361dd3d8993f9aa",
|
||||
"blk.10.attn_norm.weight": "cebbef89f0326ca8e02df3867a571e4d61c20c2a12f295f98ae590d62bc86010",
|
||||
"blk.10.attn_output.weight": "34f5efb86accb4f06347d83a32558ea8eab3039d128969161a741ebacbb656ff",
|
||||
"blk.10.attn_q.weight": "1e0efe27df2d5d50f7157253ba2cfd436d6781c3dc78ca176d0c16a210b5b763",
|
||||
"blk.10.attn_v.weight": "8f085bf50a2b0f83cd6cdda3c8ef5a9e204a36348ed95871aac725d1f68640cf",
|
||||
"blk.10.ffn_down.weight": "bf3b3cb4cace435809ac7b4cc933f20853af12f1f272d3dcefe7f19c0f203b8b",
|
||||
"blk.10.ffn_gate.weight": "d3df7a1413b1c5adf1a1dcda9e5225a15c89874bae53bb6137ad1ea42fca2d34",
|
||||
"blk.10.ffn_norm.weight": "a1da603b0480471b5ed8e862148cecd5fed918f8304d6933ab0bdb25b8d2fb8f",
|
||||
"blk.10.ffn_up.weight": "bffbba605922e972dc47dda88a0b4659aa52236c76e5fe861a949e6d9a367492",
|
||||
"blk.11.attn_k.weight": "9f31c63d66cd32c29b1eb8bb829d0c8525ce2ae936e0eefdaab6335a2d12a3df",
|
||||
"blk.11.attn_norm.weight": "0bde1a266d8b2e8f202bb7e2e88b19147ca83021901f6d3cae77a4df5548c754",
|
||||
"blk.11.attn_output.weight": "e10725c7cf746ed4a7e472cf7aea6cb564e5db6a1d5197adc980d650a387ccea",
|
||||
"blk.11.attn_q.weight": "05ee758a7d065802630f8c65dca424364c1c8825e389aa33f9405c45e8a50cce",
|
||||
"blk.11.attn_v.weight": "0c3ae7090f11775d24c51120db6e305db6aff706493e7ee123dcab74485ba789",
|
||||
"blk.11.ffn_down.weight": "7ba40b8e12c09c5fb2006b77a771cb01ce894e88a3b3e1877f927a5b89c91709",
|
||||
"blk.11.ffn_gate.weight": "db76388a023b98097972d354ba1c6a5e26efdeb1c596b9c28bf2cd8f6596975e",
|
||||
"blk.11.ffn_norm.weight": "a38c3ae1b89a68ddc7b72c99c5b28be7fe3787c4fad9904d0c43d64eaf00c474",
|
||||
"blk.11.ffn_up.weight": "13c8142f9cf1eddc658babf978daf3515c4ccc45f849f3e7e3930aa18a8480a0",
|
||||
"blk.12.attn_k.weight": "f03241c36ac87cb57429a2ef22186b8d7d0b590a8b173beb01fa13d93772f3b1",
|
||||
"blk.12.attn_norm.weight": "4568f654e6d65104d586e7c16ba960c83428698ce103022b7e0be15e2884e13b",
|
||||
"blk.12.attn_output.weight": "04867603f82f91e41306e09b33ecda0104b3ee4834061f2c0bbdc8da33c72509",
|
||||
"blk.12.attn_q.weight": "70fe04b9a8e08b6100cc8d6b58bf4cbbad15ca1de82d63baca5d352ba6c4cbae",
|
||||
"blk.12.attn_v.weight": "15cb28db61a86c98687991d7e611bc92a1fcc6007f3432149cfb5fe518a4f65e",
|
||||
"blk.12.ffn_down.weight": "6d10c790a4e3dc44c2dc36d96251ae97cdf30a4fa04d4c43e31bfbd038e6a7b7",
|
||||
"blk.12.ffn_gate.weight": "3462a2d8f6b4743b25e24da51b90018ac2858d05ac7e582bcb69063cfdac1104",
|
||||
"blk.12.ffn_norm.weight": "1f96392c1faa34e34ae5dea55a6a86c5aa4c79758952075d53d28de89dd88456",
|
||||
"blk.12.ffn_up.weight": "d22eacc612a7411953d948483c5fb201e11722955ee0754da866e7bec578ac6d",
|
||||
"blk.13.attn_k.weight": "5864977e6b733ea942647d6feed5c76156c48c200649c22e4e11b9e5860e57f3",
|
||||
"blk.13.attn_norm.weight": "87e053535144723db4145aa5402acc54331b7696752d852bb9fc542ff33f0fb5",
|
||||
"blk.13.attn_output.weight": "078145f5ad83f8b14f97a869346f7fd1583b24d1e3edadaa95d3da4242973f8f",
|
||||
"blk.13.attn_q.weight": "3b8caf35504cbc4d1a7dd6e011a95760703b7f71e2218b030b1254f811362dd7",
|
||||
"blk.13.attn_v.weight": "4fdf8365a603e043e5b40c4a21c84ac167f9be62794178f9d8a608dfe5653bf9",
|
||||
"blk.13.ffn_down.weight": "a07d3abbfcacf48ba028df2cab895be32cc15022d23389a745286e79c1b1d1fd",
|
||||
"blk.13.ffn_gate.weight": "1d2ab39666aa2909acc96787432a3ed13b19d25170f74665fadff9b17bbaffb1",
|
||||
"blk.13.ffn_norm.weight": "4f2e809fda5f3eadf52578ee50e0ba36e53be91e55dce418c12dfe595f5f18e7",
|
||||
"blk.13.ffn_up.weight": "8783d2720c2c37ca176a5801e0b3ef1f9cc9cf3ef1cd37af423aaf6b2a27e2bd",
|
||||
"blk.14.attn_k.weight": "ce9428e2b55d43ae0c6690dbd56182f99adc427694ba8236b405cc8ea5035e86",
|
||||
"blk.14.attn_norm.weight": "6abb35f9db8251d6ae954bda147c6ada2371b0574d11702e828f3c6ac99b7cc0",
|
||||
"blk.14.attn_output.weight": "fe3880916d0ceb5bff672c88bbefb7060a545be609bf049beb2024b38221836d",
|
||||
"blk.14.attn_q.weight": "7c8ad81be6f4a350931fd108b5f7c9e366e8c26ef62d1d85ffef5dca8fd893f8",
|
||||
"blk.14.attn_v.weight": "e4bdedffacbebe38567a0734dfd67db90e911d9a9669fcde9a7c4ad8a0066c52",
|
||||
"blk.14.ffn_down.weight": "ef6694dff1e05820aac0cd2b22f39ac7788b4967afc9250775575554c66aab2c",
|
||||
"blk.14.ffn_gate.weight": "db63c4179e2db704bc505e2b4696e055b593e295a1b7c4c586fc793bdd5aab19",
|
||||
"blk.14.ffn_norm.weight": "2796a62d832a9710148f95d533320492a33e712b2e5218659c548705bd11684d",
|
||||
"blk.14.ffn_up.weight": "3f78c78d8c2d54df45f799d4ff902316628af296834afe4ceed63d4a324ff03e",
|
||||
"blk.15.attn_k.weight": "6e810ee3859e07695645ee0c9a5efc7962668984a5f0a9325f47e462743b447c",
|
||||
"blk.15.attn_norm.weight": "0956b576ae96db0b28cb09f761f801cfd9281432284664f0fe181c8d9c55d1ec",
|
||||
"blk.15.attn_output.weight": "03a17f7e94208177aace5cc41b7f54670ba57873b7274ff6e23caf58cce110ca",
|
||||
"blk.15.attn_q.weight": "b8edafe7d2216a6f8b4ae4905a906475490e6ea418f6e1d3cec563dbdc6fab91",
|
||||
"blk.15.attn_v.weight": "f8ae8cae0f4cfa34a459824eba57350c3c248104ba5607e7d9dc7d7c39aaf4a6",
|
||||
"blk.15.ffn_down.weight": "8d02eb439da852246d2ca67e9b7b6de0b090b80744355e64728a23e41926505b",
|
||||
"blk.15.ffn_gate.weight": "ed5bf361c67db8731f186b775826f21c33bdb521111fd2d922539719a770239f",
|
||||
"blk.15.ffn_norm.weight": "5942ca3c73209ac9a0c8bfd9b4aab7f7be7aee9aa12d9c35833493b44af76767",
|
||||
"blk.15.ffn_up.weight": "f4bebf4ad99ec5f911327dec347be6c595814885309c7bc5647ce28c7f4d1cf5",
|
||||
"blk.16.attn_k.weight": "756a534c19364448e0958b8948fe33891c6ccda0fbb4dfa2024e1f532a87804b",
|
||||
"blk.16.attn_norm.weight": "386b7b9e4e6509f6af9c022d942b6c6c6cc136aeed8751ecb037c74d7c4bfb93",
|
||||
"blk.16.attn_output.weight": "3ba1a766a25830b84d7c22178203635f9c5624caad290bc5e5d73da5d5e7a2ec",
|
||||
"blk.16.attn_q.weight": "d39b0c91e1fda7685d50a0f7cc8d18c44b5bdc90a142c7fda0bc329cca1afa74",
|
||||
"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"
|
||||
}
|
||||
|
|
@ -3,19 +3,150 @@ package convert
|
|||
import (
|
||||
"cmp"
|
||||
"crypto/sha256"
|
||||
"encoding/hex"
|
||||
"encoding/json"
|
||||
"errors"
|
||||
"fmt"
|
||||
"io/fs"
|
||||
"log/slog"
|
||||
"os"
|
||||
"slices"
|
||||
)
|
||||
|
||||
"golang.org/x/exp/maps"
|
||||
const (
|
||||
_ int32 = iota
|
||||
tokenTypeNormal
|
||||
tokenTypeUnknown
|
||||
tokenTypeControl
|
||||
tokenTypeUserDefined
|
||||
tokenTypeUnused
|
||||
tokenTypeByte
|
||||
)
|
||||
|
||||
type Tokenizer struct {
|
||||
Version string `json:"version"`
|
||||
AddedTokens []Token `json:"added_tokens"`
|
||||
Model TokenizerModel `json:"model"`
|
||||
*Vocabulary
|
||||
SpecialVocabulary []*SpecialVocabulary
|
||||
Merges []string
|
||||
|
||||
Pre string
|
||||
Template string
|
||||
}
|
||||
|
||||
func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error) {
|
||||
v, err := parseVocabulary(fsys)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
t := &Tokenizer{
|
||||
Vocabulary: v,
|
||||
Pre: "default",
|
||||
}
|
||||
|
||||
addedTokens := make(map[string]token)
|
||||
if f, err := fsys.Open("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 != "" {
|
||||
// create a checksum of all Split pretokenizers which should be sufficient
|
||||
// to identify the pretokenizer
|
||||
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 := fsys.Open("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 specialTokenTypes {
|
||||
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"`
|
||||
|
||||
PreTokenizer struct {
|
||||
PreTokenizers []struct {
|
||||
|
|
@ -27,80 +158,108 @@ type Tokenizer struct {
|
|||
} `json:"pre_tokenizer"`
|
||||
}
|
||||
|
||||
type TokenizerModel struct {
|
||||
Type string `json:"type"`
|
||||
Vocab map[string]int `json:"vocab"`
|
||||
Merges []string `json:"merges"`
|
||||
Tokens []Token
|
||||
}
|
||||
|
||||
type Token struct {
|
||||
type token struct {
|
||||
ID int `json:"id"`
|
||||
Content string `json:"content"`
|
||||
Special bool `json:"special"`
|
||||
UserDefined bool
|
||||
}
|
||||
|
||||
func (t *Token) Type() int32 {
|
||||
switch {
|
||||
case t.Special:
|
||||
return tokenTypeControl
|
||||
case t.UserDefined:
|
||||
return tokenTypeUserDefined
|
||||
default:
|
||||
return tokenTypeNormal
|
||||
}
|
||||
type Vocabulary struct {
|
||||
Model string
|
||||
Tokens []string
|
||||
Scores []float32
|
||||
Types []int32
|
||||
}
|
||||
|
||||
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)
|
||||
func parseVocabularyFromTokenizer(fsys fs.FS) (*Vocabulary, error) {
|
||||
f, err := fsys.Open("tokenizer.json")
|
||||
if err != nil {
|
||||
panic(err)
|
||||
return nil, err
|
||||
}
|
||||
defer f.Close()
|
||||
|
||||
var t Tokenizer
|
||||
var t tokenizer
|
||||
if err := json.NewDecoder(f).Decode(&t); err != nil {
|
||||
return "", nil, nil, err
|
||||
return nil, err
|
||||
}
|
||||
|
||||
tokens = make([]Token, t.maxID()+1)
|
||||
var tokens []token
|
||||
for k, v := range t.Model.Vocab {
|
||||
tokens[v] = Token{ID: v, Content: k, Special: false, UserDefined: false}
|
||||
tokens = append(tokens, token{
|
||||
ID: v,
|
||||
Content: k,
|
||||
})
|
||||
}
|
||||
|
||||
for _, v := range t.AddedTokens {
|
||||
v.UserDefined = true
|
||||
tokens[v.ID] = v
|
||||
for _, t := range t.AddedTokens {
|
||||
t.UserDefined = true
|
||||
tokens = append(tokens, t)
|
||||
}
|
||||
|
||||
sha256sum := sha256.New()
|
||||
for _, pt := range t.PreTokenizer.PreTokenizers {
|
||||
if pt.Type == "Split" && pt.Pattern.Regex != "" {
|
||||
sha256sum.Write([]byte(pt.Pattern.Regex))
|
||||
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)
|
||||
}
|
||||
}
|
||||
|
||||
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"
|
||||
return &v, nil
|
||||
}
|
||||
|
||||
func parseVocabulary(fsys fs.FS) (*Vocabulary, error) {
|
||||
patterns := []struct {
|
||||
Pattern string
|
||||
Func func(fs.FS) (*Vocabulary, error)
|
||||
}{
|
||||
{"tokenizer.model", parseSentencePiece},
|
||||
{"tokenizer.json", parseVocabularyFromTokenizer},
|
||||
}
|
||||
|
||||
return pre, tokens, t.Model.Merges, nil
|
||||
for _, pattern := range patterns {
|
||||
if _, err := fs.Stat(fsys, pattern.Pattern); errors.Is(err, os.ErrNotExist) {
|
||||
continue
|
||||
} else if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
return pattern.Func(fsys)
|
||||
}
|
||||
|
||||
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")
|
||||
}
|
||||
|
|
|
|||
83
convert/tokenizer_spm.go
Normal file
83
convert/tokenizer_spm.go
Normal file
|
|
@ -0,0 +1,83 @@
|
|||
package convert
|
||||
|
||||
import (
|
||||
"cmp"
|
||||
"encoding/json"
|
||||
"errors"
|
||||
"fmt"
|
||||
"io/fs"
|
||||
"os"
|
||||
"slices"
|
||||
|
||||
"google.golang.org/protobuf/proto"
|
||||
|
||||
"github.com/ollama/ollama/convert/sentencepiece"
|
||||
)
|
||||
|
||||
func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
|
||||
bts, err := fs.ReadFile(fsys, "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 := fsys.Open("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
|
||||
}
|
||||
287
convert/torch.go
287
convert/torch.go
|
|
@ -1,287 +0,0 @@
|
|||
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(¶ms)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
params.ByteOrder = binary.LittleEndian
|
||||
return ¶ms, 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")
|
||||
}
|
||||
14
llm/ggla.go
14
llm/ggla.go
|
|
@ -36,6 +36,8 @@ type ggla struct {
|
|||
|
||||
kv KV
|
||||
tensors []*Tensor
|
||||
|
||||
tensorOffset uint64
|
||||
}
|
||||
|
||||
func newGGLA(container *containerGGLA) *ggla {
|
||||
|
|
@ -50,7 +52,10 @@ func (llm *ggla) KV() KV {
|
|||
}
|
||||
|
||||
func (llm *ggla) Tensors() Tensors {
|
||||
return llm.tensors
|
||||
return Tensors{
|
||||
Items: llm.tensors,
|
||||
Offset: llm.tensorOffset,
|
||||
}
|
||||
}
|
||||
|
||||
func (llm *ggla) decode(rs io.ReadSeeker) (retErr error) {
|
||||
|
|
@ -66,6 +71,13 @@ func (llm *ggla) decode(rs io.ReadSeeker) (retErr 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 {
|
||||
|
|
|
|||
|
|
@ -112,11 +112,14 @@ func (kv KV) ChatTemplate() string {
|
|||
return s
|
||||
}
|
||||
|
||||
type Tensors []*Tensor
|
||||
type Tensors struct {
|
||||
Items []*Tensor
|
||||
Offset uint64
|
||||
}
|
||||
|
||||
func (ts Tensors) Layers() map[string]Layer {
|
||||
layers := make(map[string]Layer)
|
||||
for _, t := range ts {
|
||||
for _, t := range ts.Items {
|
||||
parts := strings.Split(t.Name, ".")
|
||||
if parts[0] == "blk" {
|
||||
// join first and second part, e.g. blk.%d
|
||||
|
|
|
|||
345
llm/gguf.go
345
llm/gguf.go
|
|
@ -2,11 +2,16 @@ package llm
|
|||
|
||||
import (
|
||||
"bytes"
|
||||
"cmp"
|
||||
"encoding/binary"
|
||||
"encoding/json"
|
||||
"fmt"
|
||||
"io"
|
||||
"log/slog"
|
||||
"slices"
|
||||
"strings"
|
||||
|
||||
"golang.org/x/exp/maps"
|
||||
)
|
||||
|
||||
type containerGGUF struct {
|
||||
|
|
@ -88,7 +93,8 @@ type gguf struct {
|
|||
kv KV
|
||||
tensors []*Tensor
|
||||
|
||||
parameters uint64
|
||||
parameters uint64
|
||||
tensorOffset uint64
|
||||
|
||||
scratch [16 << 10]byte
|
||||
}
|
||||
|
|
@ -100,16 +106,15 @@ 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 llm.tensors
|
||||
return Tensors{
|
||||
Items: llm.tensors,
|
||||
Offset: llm.tensorOffset,
|
||||
}
|
||||
}
|
||||
|
||||
func (llm *gguf) numTensor() uint64 {
|
||||
|
|
@ -199,7 +204,7 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
|
|||
return fmt.Errorf("failed to read tensor dimensions: %w", err)
|
||||
}
|
||||
|
||||
shape := [4]uint64{1, 1, 1, 1}
|
||||
shape := make([]uint64, dims)
|
||||
for i := 0; uint32(i) < dims; i++ {
|
||||
shape[i], err = readGGUF[uint64](llm, rs)
|
||||
if err != nil {
|
||||
|
|
@ -236,13 +241,21 @@ 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 := llm.padding(offset, int64(alignment))
|
||||
padding := ggufPadding(offset, int64(alignment))
|
||||
if _, err := rs.Seek(padding, io.SeekCurrent); err != nil {
|
||||
return fmt.Errorf("failed to seek to init padding: %w", err)
|
||||
}
|
||||
|
|
@ -261,12 +274,12 @@ func readGGUF[T any](llm *gguf, r io.Reader) (T, error) {
|
|||
return t, err
|
||||
}
|
||||
|
||||
func writeGGUF[V any](llm *gguf, w io.Writer, t uint32, v V) error {
|
||||
if err := binary.Write(w, llm.ByteOrder, t); err != nil {
|
||||
func writeGGUF[V any](w io.Writer, t uint32, v V) error {
|
||||
if err := binary.Write(w, binary.LittleEndian, t); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
return binary.Write(w, llm.ByteOrder, v)
|
||||
return binary.Write(w, binary.LittleEndian, v)
|
||||
}
|
||||
|
||||
func readGGUFV1String(llm *gguf, r io.Reader) (string, error) {
|
||||
|
|
@ -330,12 +343,12 @@ func readGGUFString(llm *gguf, r io.Reader) (string, error) {
|
|||
return string(buf), nil
|
||||
}
|
||||
|
||||
func writeGGUFString(llm *gguf, w io.Writer, s string) error {
|
||||
if err := binary.Write(w, llm.ByteOrder, ggufTypeString); err != nil {
|
||||
func writeGGUFString(w io.Writer, s string) error {
|
||||
if err := binary.Write(w, binary.LittleEndian, ggufTypeString); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
if err := binary.Write(w, llm.ByteOrder, uint64(len(s))); err != nil {
|
||||
if err := binary.Write(w, binary.LittleEndian, uint64(len(s))); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
|
|
@ -476,216 +489,72 @@ func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
|
|||
return a, 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 {
|
||||
// writeGGUFArray writes a slice s of type E to the write with a gguf type of t
|
||||
func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
|
||||
if err := binary.Write(w, binary.LittleEndian, ggufTypeArray); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
if err := binary.Write(w, llm.ByteOrder, t); err != nil {
|
||||
if err := binary.Write(w, binary.LittleEndian, t); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
if err := binary.Write(w, llm.ByteOrder, uint64(len(s))); err != nil {
|
||||
if err := binary.Write(w, binary.LittleEndian, uint64(len(s))); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
for _, e := range s {
|
||||
if err := binary.Write(w, llm.ByteOrder, e); err != nil {
|
||||
return err
|
||||
}
|
||||
}
|
||||
|
||||
return nil
|
||||
return binary.Write(w, binary.LittleEndian, s)
|
||||
}
|
||||
|
||||
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",
|
||||
"bert.pooling_type",
|
||||
},
|
||||
}
|
||||
|
||||
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 {
|
||||
func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
|
||||
if err := binary.Write(ws, binary.LittleEndian, []byte("GGUF")); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
if err := binary.Write(ws, llm.ByteOrder, llm.Version); err != nil {
|
||||
if err := binary.Write(ws, binary.LittleEndian, uint32(3)); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
if err := binary.Write(ws, llm.ByteOrder, llm.numTensor()); err != nil {
|
||||
if err := binary.Write(ws, binary.LittleEndian, uint64(len(ts))); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
if err := binary.Write(ws, llm.ByteOrder, llm.numKV()); err != nil {
|
||||
if err := binary.Write(ws, binary.LittleEndian, uint64(len(kv))); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
kvCheck := make(map[string]bool)
|
||||
for k := range kv {
|
||||
kvCheck[k] = false
|
||||
}
|
||||
keys := maps.Keys(kv)
|
||||
slices.Sort(keys)
|
||||
|
||||
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 {
|
||||
for _, key := range keys {
|
||||
if err := ggufWriteKV(ws, key, kv[key]); err != nil {
|
||||
return err
|
||||
}
|
||||
}
|
||||
|
||||
for k, v := range kvCheck {
|
||||
if !v {
|
||||
return fmt.Errorf("Didn't know how to write kv %s", k)
|
||||
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 _, 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 cmp.Compare(i, j)
|
||||
})
|
||||
|
||||
var s uint64
|
||||
for _, t := range ts {
|
||||
t.Offset = s
|
||||
if err := ggufWriteTensorInfo(ws, t); err != nil {
|
||||
return err
|
||||
}
|
||||
s += t.Size()
|
||||
}
|
||||
|
||||
var alignment int64 = 32
|
||||
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 {
|
||||
for _, t := range ts {
|
||||
if err := ggufWriteTensor(ws, t, alignment); err != nil {
|
||||
return err
|
||||
}
|
||||
}
|
||||
|
|
@ -693,6 +562,102 @@ func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
|
|||
return nil
|
||||
}
|
||||
|
||||
func (gguf) padding(offset, align int64) int64 {
|
||||
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 {
|
||||
slog.Debug(t.Name, "kind", t.Kind, "shape", t.Shape, "offset", t.Offset)
|
||||
if err := binary.Write(ws, binary.LittleEndian, uint64(len(t.Name))); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
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 {
|
||||
offset, err := ws.Seek(0, io.SeekCurrent)
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
if err := binary.Write(ws, binary.LittleEndian, bytes.Repeat([]byte{0}, int(ggufPadding(offset, alignment)))); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
_, err = t.WriteTo(ws)
|
||||
return err
|
||||
}
|
||||
|
||||
func ggufPadding(offset, align int64) int64 {
|
||||
return (align - offset%align) % align
|
||||
}
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@ package llm
|
|||
|
||||
import (
|
||||
"bytes"
|
||||
"encoding/binary"
|
||||
"fmt"
|
||||
"os"
|
||||
"testing"
|
||||
|
|
@ -20,7 +19,6 @@ 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{
|
||||
|
|
@ -32,7 +30,7 @@ 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 = gguf.Encode(f, KV{
|
||||
err = WriteGGUF(f, KV{
|
||||
"general.architecture": "llama",
|
||||
"general.name": "name",
|
||||
"llama.context_length": uint32(32),
|
||||
|
|
|
|||
|
|
@ -81,112 +81,43 @@ func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressRe
|
|||
return layers, nil
|
||||
}
|
||||
|
||||
func extractFromZipFile(p string, file *os.File, fn func(api.ProgressResponse)) error {
|
||||
stat, err := file.Stat()
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
r, err := zip.NewReader(file, stat.Size())
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
fn(api.ProgressResponse{Status: "unpacking model metadata"})
|
||||
for _, f := range r.File {
|
||||
if !filepath.IsLocal(f.Name) {
|
||||
return fmt.Errorf("%w: %s", zip.ErrInsecurePath, f.Name)
|
||||
}
|
||||
|
||||
n := filepath.Join(p, f.Name)
|
||||
if err := os.MkdirAll(filepath.Dir(n), 0o750); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
// TODO(mxyng): this should not write out all files to disk
|
||||
outfile, err := os.Create(n)
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
defer outfile.Close()
|
||||
|
||||
infile, err := f.Open()
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
defer infile.Close()
|
||||
|
||||
if _, err = io.Copy(outfile, infile); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
if err := outfile.Close(); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
if err := infile.Close(); err != nil {
|
||||
return err
|
||||
}
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {
|
||||
tempDir, err := os.MkdirTemp(filepath.Dir(file.Name()), "")
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
defer os.RemoveAll(tempDir)
|
||||
|
||||
if err := extractFromZipFile(tempDir, file, fn); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
mf, err := convert.GetModelFormat(tempDir)
|
||||
func parseFromZipFile(_ context.Context, f *os.File, digest string, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {
|
||||
fi, err := f.Stat()
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
params, err := mf.GetParams(tempDir)
|
||||
r, err := zip.NewReader(f, fi.Size())
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
mArch, err := mf.GetModelArch("", tempDir, params)
|
||||
p, err := os.MkdirTemp(filepath.Dir(f.Name()), "")
|
||||
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
|
||||
}
|
||||
defer os.RemoveAll(p)
|
||||
|
||||
fn(api.ProgressResponse{Status: "converting model"})
|
||||
|
||||
// TODO(mxyng): this should write directly into a layer
|
||||
// e.g. NewLayer(arch.Reader(), "application/vnd.ollama.image.model")
|
||||
temp, err := os.CreateTemp(tempDir, "fp16")
|
||||
t, err := os.CreateTemp(p, "fp16")
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
defer temp.Close()
|
||||
defer os.Remove(temp.Name())
|
||||
defer t.Close()
|
||||
defer os.Remove(t.Name())
|
||||
|
||||
if err = mArch.WriteGGUF(temp); err != nil {
|
||||
fn(api.ProgressResponse{Status: "converting model"})
|
||||
if err := convert.Convert(convert.NewZipReader(r, p, 32<<20), t); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if _, err := temp.Seek(0, io.SeekStart); err != nil {
|
||||
if _, err := t.Seek(0, io.SeekStart); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
layer, err := NewLayer(temp, "application/vnd.ollama.image.model")
|
||||
layer, err := NewLayer(t, "application/vnd.ollama.image.model")
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,16 +1,11 @@
|
|||
package server
|
||||
|
||||
import (
|
||||
"archive/zip"
|
||||
"bytes"
|
||||
"encoding/json"
|
||||
"errors"
|
||||
"fmt"
|
||||
"io"
|
||||
"os"
|
||||
"path/filepath"
|
||||
"slices"
|
||||
"strings"
|
||||
"testing"
|
||||
|
||||
"github.com/google/go-cmp/cmp"
|
||||
|
|
@ -18,103 +13,6 @@ import (
|
|||
"github.com/ollama/ollama/template"
|
||||
)
|
||||
|
||||
func createZipFile(t *testing.T, name string) *os.File {
|
||||
t.Helper()
|
||||
|
||||
f, err := os.CreateTemp(t.TempDir(), "")
|
||||
if err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
zf := zip.NewWriter(f)
|
||||
defer zf.Close()
|
||||
|
||||
zh, err := zf.CreateHeader(&zip.FileHeader{Name: name})
|
||||
if err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
if _, err := io.Copy(zh, bytes.NewReader([]byte(""))); err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
return f
|
||||
}
|
||||
|
||||
func TestExtractFromZipFile(t *testing.T) {
|
||||
cases := []struct {
|
||||
name string
|
||||
expect []string
|
||||
err error
|
||||
}{
|
||||
{
|
||||
name: "good",
|
||||
expect: []string{"good"},
|
||||
},
|
||||
{
|
||||
name: strings.Join([]string{"path", "..", "to", "good"}, string(os.PathSeparator)),
|
||||
expect: []string{filepath.Join("to", "good")},
|
||||
},
|
||||
{
|
||||
name: strings.Join([]string{"path", "..", "to", "..", "good"}, string(os.PathSeparator)),
|
||||
expect: []string{"good"},
|
||||
},
|
||||
{
|
||||
name: strings.Join([]string{"path", "to", "..", "..", "good"}, string(os.PathSeparator)),
|
||||
expect: []string{"good"},
|
||||
},
|
||||
{
|
||||
name: strings.Join([]string{"..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "bad"}, string(os.PathSeparator)),
|
||||
err: zip.ErrInsecurePath,
|
||||
},
|
||||
{
|
||||
name: strings.Join([]string{"path", "..", "..", "to", "bad"}, string(os.PathSeparator)),
|
||||
err: zip.ErrInsecurePath,
|
||||
},
|
||||
}
|
||||
|
||||
for _, tt := range cases {
|
||||
t.Run(tt.name, func(t *testing.T) {
|
||||
f := createZipFile(t, tt.name)
|
||||
defer f.Close()
|
||||
|
||||
tempDir := t.TempDir()
|
||||
if err := extractFromZipFile(tempDir, f, func(api.ProgressResponse) {}); !errors.Is(err, tt.err) {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
var matches []string
|
||||
if err := filepath.Walk(tempDir, func(p string, fi os.FileInfo, err error) error {
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
if !fi.IsDir() {
|
||||
matches = append(matches, p)
|
||||
}
|
||||
|
||||
return nil
|
||||
}); err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
var actual []string
|
||||
for _, match := range matches {
|
||||
rel, err := filepath.Rel(tempDir, match)
|
||||
if err != nil {
|
||||
t.Error(err)
|
||||
}
|
||||
|
||||
actual = append(actual, rel)
|
||||
}
|
||||
|
||||
if !slices.Equal(actual, tt.expect) {
|
||||
t.Fatalf("expected %d files, got %d", len(tt.expect), len(matches))
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
func readFile(t *testing.T, base, name string) *bytes.Buffer {
|
||||
t.Helper()
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@ package server
|
|||
|
||||
import (
|
||||
"bytes"
|
||||
"encoding/binary"
|
||||
"encoding/json"
|
||||
"fmt"
|
||||
"io"
|
||||
|
|
@ -29,7 +28,7 @@ func createBinFile(t *testing.T, kv map[string]any, ti []llm.Tensor) string {
|
|||
}
|
||||
defer f.Close()
|
||||
|
||||
if err := llm.NewGGUFV3(binary.LittleEndian).Encode(f, kv, ti); err != nil {
|
||||
if err := llm.WriteGGUF(f, kv, ti); err != nil {
|
||||
t.Fatal(err)
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -3,7 +3,6 @@ package server
|
|||
import (
|
||||
"bytes"
|
||||
"context"
|
||||
"encoding/binary"
|
||||
"fmt"
|
||||
"log/slog"
|
||||
"os"
|
||||
|
|
@ -114,8 +113,7 @@ func newScenarioRequest(t *testing.T, ctx context.Context, modelName string, est
|
|||
require.NoError(t, err)
|
||||
defer f.Close()
|
||||
|
||||
gguf := llm.NewGGUFV3(binary.LittleEndian)
|
||||
err = gguf.Encode(f, llm.KV{
|
||||
require.NoError(t, llm.WriteGGUF(f, llm.KV{
|
||||
"general.architecture": "llama",
|
||||
"general.name": "name",
|
||||
"llama.context_length": uint32(32),
|
||||
|
|
@ -129,7 +127,7 @@ func newScenarioRequest(t *testing.T, ctx context.Context, modelName string, est
|
|||
}, []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()
|
||||
|
|
|
|||
Loading…
Reference in a new issue