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- package convert
- import "github.com/ollama/ollama/fs/ggml"
- type gemma2Model struct {
- gemmaModel
- SlidingWindow uint32 `json:"sliding_window"`
- AttentionLogitSoftcap float32 `json:"attn_logit_softcapping"`
- FinalLogitSoftcap float32 `json:"final_logit_softcapping"`
- }
- func (p *gemma2Model) KV(t *Tokenizer) ggml.KV {
- kv := p.ModelParameters.KV(t)
- kv["general.architecture"] = "gemma2"
- kv["gemma2.context_length"] = p.MaxPositionEmbeddings
- kv["gemma2.embedding_length"] = p.HiddenSize
- kv["gemma2.block_count"] = p.HiddenLayers
- kv["gemma2.feed_forward_length"] = p.IntermediateSize
- kv["gemma2.attention.head_count"] = p.NumAttentionHeads
- kv["gemma2.attention.head_count_kv"] = p.NumKeyValueHeads
- kv["gemma2.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
- kv["gemma2.attention.key_length"] = p.HeadDim
- kv["gemma2.attention.value_length"] = p.HeadDim
- kv["gemma2.attention.sliding_window"] = p.SlidingWindow
- kv["gemma2.attn_logit_softcapping"] = p.AttentionLogitSoftcap
- kv["gemma2.final_logit_softcapping"] = p.FinalLogitSoftcap
- kv["tokenizer.ggml.eot_token_id"] = uint32(107)
- kv["tokenizer.ggml.middle_token_id"] = uint32(68)
- kv["tokenizer.ggml.prefix_token_id"] = uint32(67)
- kv["tokenizer.ggml.suffix_token_id"] = uint32(69)
- return kv
- }
- func (p *gemma2Model) Replacements() []string {
- return []string{
- "model.embed_tokens", "token_embd",
- "model.norm", "output_norm",
- "model.layers", "blk",
- "input_layernorm", "attn_norm",
- "self_attn.q_proj", "attn_q",
- "self_attn.k_proj", "attn_k",
- "self_attn.v_proj", "attn_v",
- "self_attn.o_proj", "attn_output",
- "mlp.gate_proj", "ffn_gate",
- "mlp.down_proj", "ffn_down",
- "mlp.up_proj", "ffn_up",
- "post_attention_layernorm", "post_attention_norm",
- "pre_feedforward_layernorm", "ffn_norm",
- "post_feedforward_layernorm", "post_ffw_norm",
- }
- }
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