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82 lines
3.2 KiB
Go
82 lines
3.2 KiB
Go
package convert
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import "github.com/ollama/ollama/fs/ggml"
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type gemma3Model struct {
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gemmaModel
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TextModel gemma3TextModel `json:"text_config"`
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VisionModel gemma3VisionModel `json:"vision_config"`
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}
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type gemma3TextModel struct {
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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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SlidingWindow uint32 `json:"sliding_window"`
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AttentionLogitSoftcap float32 `json:"attn_logit_softcapping"`
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FinalLogitSoftcap float32 `json:"final_logit_softcapping"`
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RopeLocalTheta float32 `json:"rope_local_base_freq"`
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RopeGlobalTheta float32 `json:"rope_global_base_freq"`
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}
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type gemma3VisionModel struct {
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ImageSize uint32 `json:"image_size"`
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NumChannels uint32 `json:"num_channels"`
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HiddenLayers uint32 `json:"num_hidden_layers"`
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}
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func (p *gemma3Model) KV(t *Tokenizer) ggml.KV {
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kv := p.ModelParameters.KV(t)
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kv["general.architecture"] = "gemma3"
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kv["gemma3.context_length"] = p.TextModel.MaxPositionEmbeddings
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kv["gemma3.embedding_length"] = p.TextModel.HiddenSize
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kv["gemma3.block_count"] = p.TextModel.HiddenLayers
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kv["gemma3.text.feed_forward_length"] = p.TextModel.IntermediateSize
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kv["gemma3.attention.head_count"] = p.TextModel.NumAttentionHeads
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kv["gemma3.attention.head_count_kv"] = p.TextModel.NumKeyValueHeads
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kv["gemma3.text.attention.layer_norm_rms_epsilon"] = p.TextModel.RMSNormEPS
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kv["gemma3.attention.key_length"] = p.TextModel.HeadDim
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kv["gemma3.attention.value_length"] = p.TextModel.HeadDim
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kv["gemma3.text.attention.sliding_window"] = p.TextModel.SlidingWindow
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kv["gemma3.text.final_logit_softcapping"] = p.TextModel.FinalLogitSoftcap
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kv["gemma3.text.rope.local.freq_base"] = p.TextModel.RopeLocalTheta
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kv["gemma3.text.rope.global.freq_base"] = p.TextModel.RopeGlobalTheta
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kv["tokenizer.ggml.bos_token_id"] = uint32(2)
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kv["tokenizer.ggml.eot_token_id"] = uint32(1)
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kv["gemma3.vision.image_size"] = p.VisionModel.ImageSize
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kv["gemma3.vision.num_channels"] = p.VisionModel.NumChannels
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kv["gemma3.vision.block_count"] = p.VisionModel.HiddenLayers
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return kv
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}
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func (p *gemma3Model) 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.norm", "output_norm",
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"vision_model.vision_model", "v",
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"language_model.", "",
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"model.layers", "blk",
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"encoder.layers", "blk",
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"vision_tower.vision_model.embeddings", "v",
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"input_layernorm", "attn_norm",
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"self_attn.q_proj", "attn_q",
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"self_attn.q_norm", "attn_q_norm",
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"self_attn.k_proj", "attn_k",
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"self_attn.k_norm", "attn_k_norm",
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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", "post_attention_norm",
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"pre_feedforward_layernorm", "ffn_norm",
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"post_feedforward_layernorm", "post_ffw_norm",
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}
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}
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