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