package convert import ( "encoding/binary" "fmt" "io" "os" "regexp" "strings" "github.com/d4l3k/go-bfloat16" "github.com/pdevine/tensor" "github.com/pdevine/tensor/native" "github.com/x448/float16" "github.com/ollama/ollama/llm" ) type MistralModel struct { ModelData } func mistralLayerHandler(w io.Writer, r safetensorWriterTo, f *os.File) error { layerSize := r.end - r.start var err error tData := make([]uint16, layerSize/2) if err = binary.Read(f, r.bo, tData); err != nil { return err } var heads uint32 if strings.Contains(r.t.Name, "attn_q") { heads = uint32(r.params.AttentionHeads) } else if strings.Contains(r.t.Name, "attn_k") { heads = uint32(r.params.KeyValHeads) if heads == 0 { heads = uint32(r.params.AttentionHeads) } } else { return fmt.Errorf("unknown layer type") } tData, err = repack(tData, int(heads), r.t.Shape) if err != nil { return err } var buf []byte for _, n := range tData { buf = r.bo.AppendUint16(buf, n) } tempBuf := make([]uint16, len(tData)) tDataF32 := bfloat16.DecodeFloat32(buf) for cnt, v := range tDataF32 { tDataF16 := float16.Fromfloat32(v) tempBuf[cnt] = uint16(tDataF16) } if err = binary.Write(w, r.bo, tempBuf); err != nil { return err } return nil } func repack(data []uint16, heads int, shape []uint64) ([]uint16, error) { n := tensor.New(tensor.WithShape(int(shape[0]), int(shape[1])), tensor.WithBacking(data)) origShape := n.Shape().Clone() // reshape the tensor and swap axes 1 and 2 to unpack the layer for gguf if err := n.Reshape(heads, 2, origShape[0]/heads/2, origShape[1]); err != nil { return nil, err } if err := n.T(0, 2, 1, 3); err != nil { return nil, err } if err := n.Reshape(origShape...); err != nil { return nil, err } if err := n.Transpose(); err != nil { return nil, err } newN, err := native.SelectU16(n, 1) if err != nil { return nil, err } var fullTensor []uint16 for _, v := range newN { fullTensor = append(fullTensor, v...) } return fullTensor, nil } func (m *MistralModel) GetTensors() error { t, err := GetSafeTensors(m.Path, m.Params) if err != nil { return err } m.Tensors = []llm.Tensor{} pattern := `^blk\.[0-9]+\.attn_(?Pq|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.handler = mistralLayerHandler l.WriterTo = wt } m.Tensors = append(m.Tensors, l) } return nil } func (m *MistralModel) LoadVocab() error { v, err := LoadSentencePieceTokens(m.Path, m.Params.VocabSize) if err != nil { return err } m.Vocab = v return nil } func (m *MistralModel) WriteGGUF() (string, 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), } f, err := os.CreateTemp("", "ollama-gguf") if err != nil { return "", err } defer f.Close() mod := llm.NewGGUFV3(m.Params.ByteOrder) if err := mod.Encode(f, kv, m.Tensors); err != nil { return "", err } return f.Name(), nil }