mirror of https://github.com/ollama/ollama
118 lines
3.5 KiB
Go
118 lines
3.5 KiB
Go
package convert
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import (
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"cmp"
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"github.com/ollama/ollama/fs/ggml"
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)
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type ropeScaling struct {
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Factor float32 `json:"factor"`
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OriginalMaxPositionEmbeds uint32 `json:"original_max_position_embeddings"`
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AttentionFactor float32 `json:"attention_factor"`
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BetaFast float32 `json:"beta_fast"`
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BetaSlow float32 `json:"beta_slow"`
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RopeType string `json:"rope_type"`
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ExtrapolationFactor float32 `json:"extrapolation_factor"`
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}
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type olmoModel struct {
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ModelParameters
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HiddenSize uint32 `json:"hidden_size"`
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NumHiddenLayers 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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MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
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RMSNormEPS float32 `json:"rms_norm_eps"`
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RopeTheta float32 `json:"rope_theta"`
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RopeScaling *ropeScaling `json:"rope_scaling"`
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SlidingWindow uint32 `json:"sliding_window"`
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LayerTypes []string `json:"layer_types"`
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}
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var _ ModelConverter = (*olmoModel)(nil)
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func (p *olmoModel) KV(t *Tokenizer) ggml.KV {
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kv := p.ModelParameters.KV(t)
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kv["general.architecture"] = "olmo3"
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kv["olmo3.block_count"] = p.NumHiddenLayers
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kv["olmo3.context_length"] = p.MaxPositionEmbeddings
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kv["olmo3.embedding_length"] = p.HiddenSize
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kv["olmo3.feed_forward_length"] = p.IntermediateSize
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kv["olmo3.attention.head_count"] = p.NumAttentionHeads
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kv["olmo3.attention.head_count_kv"] = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
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if p.RopeTheta > 0 {
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kv["olmo3.rope.freq_base"] = p.RopeTheta
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}
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if p.RopeScaling != nil {
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if p.RopeScaling.Factor > 0 {
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kv["olmo3.rope.scaling.factor"] = p.RopeScaling.Factor
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}
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if p.RopeScaling.OriginalMaxPositionEmbeds > 0 {
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kv["olmo3.rope.scaling.original_context_length"] = p.RopeScaling.OriginalMaxPositionEmbeds
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}
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if p.RopeScaling.AttentionFactor > 0 {
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kv["olmo3.rope.scaling.attn_factor"] = p.RopeScaling.AttentionFactor
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}
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if p.RopeScaling.RopeType != "" {
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kv["olmo3.rope.scaling.type"] = p.RopeScaling.RopeType
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}
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}
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if p.RMSNormEPS > 0 {
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kv["olmo3.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
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}
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if p.SlidingWindow > 0 {
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kv["olmo3.attention.sliding_window"] = p.SlidingWindow
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}
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if len(p.LayerTypes) > 0 {
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slidingPattern := make([]bool, len(p.LayerTypes))
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for i, layerType := range p.LayerTypes {
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slidingPattern[i] = (layerType == "sliding_attention")
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}
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kv["olmo3.attention.sliding_window_pattern"] = slidingPattern
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}
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return kv
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}
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func (p *olmoModel) Tensors(ts []Tensor) []*ggml.Tensor {
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out := make([]*ggml.Tensor, 0, len(ts))
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for _, t := range ts {
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out = append(out, &ggml.Tensor{
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Name: t.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 *olmoModel) Replacements() []string {
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return []string{
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"lm_head", "output",
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"model.embed_tokens", "token_embd",
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"model.layers", "blk",
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"model.norm", "output_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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"self_attn.q_norm", "attn_q_norm",
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"self_attn.k_norm", "attn_k_norm",
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"post_attention_layernorm", "post_attention_norm",
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"post_feedforward_layernorm", "post_ffw_norm",
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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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}
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}
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