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_(?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.repacker = m.Repack l.WriterTo = wt } m.Tensors = append(m.Tensors, l) } return nil } func (m *MistralModel) LoadVocab() error { v, err := LoadSentencePieceTokens(m.Path, m.Params) if err != nil { return err } m.Vocab = v return nil } func (m *MistralModel) WriteGGUF(ws io.WriteSeeker) error { kv := llm.KV{ "general.architecture": "llama", "general.name": m.Name, "llama.context_length": uint32(m.Params.ContextSize), "llama.embedding_length": uint32(m.Params.HiddenSize), "llama.block_count": uint32(m.Params.HiddenLayers), "llama.feed_forward_length": uint32(m.Params.IntermediateSize), "llama.rope.dimension_count": uint32(m.Params.HiddenSize / m.Params.AttentionHeads), "llama.attention.head_count": uint32(m.Params.AttentionHeads), "llama.attention.head_count_kv": uint32(m.Params.KeyValHeads), "llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS), "general.file_type": uint32(1), "tokenizer.ggml.model": "llama", "tokenizer.ggml.tokens": m.Vocab.Tokens, "tokenizer.ggml.scores": m.Vocab.Scores, "tokenizer.ggml.token_type": m.Vocab.Types, "tokenizer.ggml.bos_token_id": uint32(m.Params.BoSTokenID), "tokenizer.ggml.eos_token_id": uint32(m.Params.EoSTokenID), "tokenizer.ggml.add_bos_token": true, "tokenizer.ggml.add_eos_token": false, "tokenizer.ggml.unknown_token_id": uint32(0), } return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors) } func (m *MistralModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) { return llamaRepack(name, m.Params, data, shape) }