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@@ -4,7 +4,13 @@ import "github.com/ollama/ollama/fs/ggml"
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type gemma3Model struct {
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type gemma3Model struct {
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gemmaModel
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gemmaModel
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- TextModel gemma3TextModel `json:"text_config"`
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+ Architecture string
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+ TextModel struct {
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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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+ SlidingWindow uint32 `json:"sliding_window"`
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+ } `json:"text_config"`
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VisionModel struct {
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VisionModel struct {
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NumAttentionHeads uint32 `json:"num_attention_heads"` // attention.head_count 16
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NumAttentionHeads uint32 `json:"num_attention_heads"` // attention.head_count 16
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LayerNormEpsilon float32 `json:"layer_norm_eps"` // attention.layer_norm_epsilon 1e-05
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LayerNormEpsilon float32 `json:"layer_norm_eps"` // attention.layer_norm_epsilon 1e-05
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@@ -15,49 +21,54 @@ type gemma3Model struct {
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NumChannels uint32 `json:"num_channels"` // num_channels 3
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NumChannels uint32 `json:"num_channels"` // num_channels 3
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PatchSize uint32 `json:"patch_size"` // patch_size 14
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PatchSize uint32 `json:"patch_size"` // patch_size 14
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} `json:"vision_config"`
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} `json:"vision_config"`
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-}
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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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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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NumAttentionHeads uint32 `json:"num_attention_heads"`
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NumKeyValueHeads uint32 `json:"num_key_value_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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RMSNormEPS float32 `json:"rms_norm_eps"`
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HeadDim uint32 `json:"head_dim"`
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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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FinalLogitSoftcap float32 `json:"final_logit_softcapping"`
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RopeLocalTheta float32 `json:"rope_local_base_freq"`
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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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RopeGlobalTheta float32 `json:"rope_global_base_freq"`
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+ SlidingWindow uint32 `json:"sliding_window"`
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}
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}
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func (p *gemma3Model) KV(t *Tokenizer) ggml.KV {
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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 := p.ModelParameters.KV(t)
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kv["general.architecture"] = "gemma3"
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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["gemma3.vision.block_count"] = p.VisionModel.NumHiddenLayers
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- kv["gemma3.vision.embedding_length"] = p.VisionModel.HiddenSize
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- kv["gemma3.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
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- kv["gemma3.vision.image_size"] = p.VisionModel.ImageSize
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- kv["gemma3.vision.patch_size"] = p.VisionModel.PatchSize
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- kv["gemma3.vision.num_channels"] = p.VisionModel.NumChannels
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- kv["gemma3.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
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- kv["gemma3.vision.attention.layer_norm_epsilon"] = p.VisionModel.LayerNormEpsilon
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+ switch p.Architecture {
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+ case "Gemma3ForCausalLM":
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+ kv["gemma3.context_length"] = p.MaxPositionEmbeddings
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+ kv["gemma3.attention.head_count"] = p.NumAttentionHeads
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+ kv["gemma3.attention.head_count_kv"] = p.NumKeyValueHeads
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+ kv["gemma3.text.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
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+ kv["gemma3.attention.key_length"] = p.HeadDim
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+ kv["gemma3.attention.value_length"] = p.HeadDim
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+ kv["gemma3.text.attention.sliding_window"] = p.SlidingWindow
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+ kv["gemma3.text.final_logit_softcapping"] = p.FinalLogitSoftcap
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+ kv["gemma3.text.rope.local.freq_base"] = p.RopeLocalTheta
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+ kv["gemma3.text.rope.global.freq_base"] = p.RopeGlobalTheta
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+ kv["gemma3.embedding_length"] = p.HiddenSize
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+ kv["gemma3.block_count"] = p.HiddenLayers
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+ kv["gemma3.text.feed_forward_length"] = p.IntermediateSize
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+ default:
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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.text.attention.sliding_window"] = p.TextModel.SlidingWindow
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+ kv["gemma3.vision.block_count"] = p.VisionModel.NumHiddenLayers
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+ kv["gemma3.vision.embedding_length"] = p.VisionModel.HiddenSize
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+ kv["gemma3.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
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+ kv["gemma3.vision.image_size"] = p.VisionModel.ImageSize
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+ kv["gemma3.vision.patch_size"] = p.VisionModel.PatchSize
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+ kv["gemma3.vision.num_channels"] = p.VisionModel.NumChannels
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+ kv["gemma3.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
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+ kv["gemma3.vision.attention.layer_norm_epsilon"] = p.VisionModel.LayerNormEpsilon
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+ }
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+
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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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+
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return kv
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return kv
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}
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}
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