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llama.cpp
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model : make Gemma 4 shared-KV tail attn_k tensors optional on load (#21739)

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MoonRide303 2026-04-10 21:45:50 +02:00 committed by GitHub
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commit e62fa13c24
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1 changed files with 3 additions and 2 deletions
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src/llama-model.cpp
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@ -4623,17 +4623,18 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
const int64_t n_embd_head = hparams.n_embd_head_k(i);
const int64_t n_embd_k = hparams.n_embd_k_gqa(i);
const int64_t n_embd_v = hparams.n_embd_v_gqa(i);
const int kv_flags = hparams.has_kv(i) ? 0 : TENSOR_NOT_REQUIRED;
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
// note: use_alternative_attention (v_proj is optional, if it's not present, use k_proj)
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k}, kv_flags);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v}, TENSOR_NOT_REQUIRED);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head * n_head, n_embd}, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head}, kv_flags);
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
layer.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), {1u}, TENSOR_NOT_REQUIRED);