convert_gemma3.go 4.0 KB

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  1. package convert
  2. import "github.com/ollama/ollama/fs/ggml"
  3. type gemma3Model struct {
  4. gemmaModel
  5. TextModel gemma3TextModel `json:"text_config"`
  6. VisionModel struct {
  7. NumAttentionHeads uint32 `json:"num_attention_heads"` // attention.head_count 16
  8. LayerNormEpsilon float32 `json:"layer_norm_eps"` // attention.layer_norm_epsilon 1e-05
  9. NumHiddenLayers uint32 `json:"num_hidden_layers"` // block_count 32
  10. HiddenSize uint32 `json:"hidden_size"` // embedding_length 1280
  11. IntermediateSize uint32 `json:"intermediate_size"` // feed_forward_length 5120
  12. ImageSize uint32 `json:"image_size"` // image_size 560
  13. NumChannels uint32 `json:"num_channels"` // num_channels 3
  14. PatchSize uint32 `json:"patch_size"` // patch_size 14
  15. } `json:"vision_config"`
  16. }
  17. type gemma3TextModel struct {
  18. MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
  19. HiddenSize uint32 `json:"hidden_size"`
  20. HiddenLayers uint32 `json:"num_hidden_layers"`
  21. IntermediateSize uint32 `json:"intermediate_size"`
  22. NumAttentionHeads uint32 `json:"num_attention_heads"`
  23. NumKeyValueHeads uint32 `json:"num_key_value_heads"`
  24. RMSNormEPS float32 `json:"rms_norm_eps"`
  25. HeadDim uint32 `json:"head_dim"`
  26. SlidingWindow uint32 `json:"sliding_window"`
  27. AttentionLogitSoftcap float32 `json:"attn_logit_softcapping"`
  28. FinalLogitSoftcap float32 `json:"final_logit_softcapping"`
  29. RopeLocalTheta float32 `json:"rope_local_base_freq"`
  30. RopeGlobalTheta float32 `json:"rope_global_base_freq"`
  31. }
  32. func (p *gemma3Model) KV(t *Tokenizer) ggml.KV {
  33. kv := p.ModelParameters.KV(t)
  34. kv["general.architecture"] = "gemma3"
  35. kv["gemma3.context_length"] = p.TextModel.MaxPositionEmbeddings
  36. kv["gemma3.embedding_length"] = p.TextModel.HiddenSize
  37. kv["gemma3.block_count"] = p.TextModel.HiddenLayers
  38. kv["gemma3.text.feed_forward_length"] = p.TextModel.IntermediateSize
  39. kv["gemma3.attention.head_count"] = p.TextModel.NumAttentionHeads
  40. kv["gemma3.attention.head_count_kv"] = p.TextModel.NumKeyValueHeads
  41. kv["gemma3.text.attention.layer_norm_rms_epsilon"] = p.TextModel.RMSNormEPS
  42. kv["gemma3.attention.key_length"] = p.TextModel.HeadDim
  43. kv["gemma3.attention.value_length"] = p.TextModel.HeadDim
  44. kv["gemma3.text.attention.sliding_window"] = p.TextModel.SlidingWindow
  45. kv["gemma3.text.final_logit_softcapping"] = p.TextModel.FinalLogitSoftcap
  46. kv["gemma3.text.rope.local.freq_base"] = p.TextModel.RopeLocalTheta
  47. kv["gemma3.text.rope.global.freq_base"] = p.TextModel.RopeGlobalTheta
  48. kv["gemma3.vision.block_count"] = p.VisionModel.NumHiddenLayers
  49. kv["gemma3.vision.embedding_length"] = p.VisionModel.HiddenSize
  50. kv["gemma3.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
  51. kv["gemma3.vision.image_size"] = p.VisionModel.ImageSize
  52. kv["gemma3.vision.patch_size"] = p.VisionModel.PatchSize
  53. kv["gemma3.vision.num_channels"] = p.VisionModel.NumChannels
  54. kv["gemma3.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
  55. kv["gemma3.vision.attention.layer_norm_epsilon"] = p.VisionModel.LayerNormEpsilon
  56. return kv
  57. }
  58. func (p *gemma3Model) Replacements() []string {
  59. return []string{
  60. "lm_head", "output",
  61. "model.embed_tokens", "token_embd",
  62. "model.norm", "output_norm",
  63. "vision_tower.vision_model.embeddings", "v",
  64. "vision_tower.vision_model", "v",
  65. "language_model.", "",
  66. "model.layers", "blk",
  67. "encoder.layers", "blk",
  68. "input_layernorm", "attn_norm",
  69. "self_attn.q_proj", "attn_q",
  70. "self_attn.q_norm", "attn_q_norm",
  71. "self_attn.k_proj", "attn_k",
  72. "self_attn.k_norm", "attn_k_norm",
  73. "self_attn.v_proj", "attn_v",
  74. "self_attn.o_proj", "attn_output",
  75. "self_attn.out_proj", "attn_output",
  76. "mlp.gate_proj", "ffn_gate",
  77. "mlp.down_proj", "ffn_down",
  78. "mlp.up_proj", "ffn_up",
  79. "post_attention_layernorm", "post_attention_norm",
  80. "pre_feedforward_layernorm", "ffn_norm",
  81. "post_feedforward_layernorm", "post_ffw_norm",
  82. "input_projection_weight", "input_projection.weight",
  83. "multi_modal_projector", "mm",
  84. }
  85. }