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llama.cpp
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removing whitespace

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ryan-mangeno 2025-10-30 13:09:24 -04:00
parent 7e8e1a07d5
commit b66c2fd1cd
2 changed files with 9 additions and 10 deletions
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3
convert_hf_to_gguf.py
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@ -9329,7 +9329,7 @@ class SmallThinkerModel(TextModel):
experts = [k for d in self._experts for k in d.keys()]
if len(experts) > 0:
raise ValueError(f"Unprocessed experts: {experts}")
@ModelBase.register("ModernBertModel", "ModernBertForMaskedLM", "ModernBertForSequenceClassification")
class ModernBertModel(BertModel):
@ -9368,7 +9368,6 @@ class ModernBertModel(BertModel):
name = name[6:]
return super().modify_tensors(data_torch, name, bid)
@ModelBase.register("ApertusForCausalLM")

16
src/llama-model.cpp
View File

@ -855,7 +855,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
type = LLM_TYPE_149M; break; // modern-bert-base
case 28:
type = LLM_TYPE_395M; break; // modern-bert-large
default: type = LLM_TYPE_UNKNOWN;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_JINA_BERT_V2:
@ -2993,11 +2993,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}, 0);
}
} break;
case LLM_ARCH_MODERN_BERT:
case LLM_ARCH_MODERN_BERT:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
for(int i = 0; i < n_layer; ++i) {
@ -3006,7 +3006,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
if ( i != 0 ) {
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
} else{
// layer 0 uses identity
// layer 0 uses identity
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
}
@ -3014,7 +3014,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3 * n_embd }, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, 2 * n_ff}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, 2 * n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
}
@ -8209,7 +8209,7 @@ struct llm_build_modern_bert : public llm_graph_context {
ggml_tensor * cur = nullptr;
ggml_tensor * inpL = nullptr;
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_pos = build_inp_pos();
// construct input embeddings (token, type, position)
inpL = build_inp_embd(model.tok_embd);
@ -8221,7 +8221,7 @@ struct llm_build_modern_bert : public llm_graph_context {
ggml_tensor * inp_out_ids = build_inp_out_ids();
auto * inp_attn = build_attn_inp_no_cache();
auto * inp_attn = build_attn_inp_no_cache();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * cur = inpL;
@ -19831,7 +19831,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
case LLM_ARCH_NOMIC_BERT_MOE:
case LLM_ARCH_NEO_BERT:
case LLM_ARCH_WAVTOKENIZER_DEC:
case LLM_ARCH_MODERN_BERT:
case LLM_ARCH_MODERN_BERT:
case LLM_ARCH_GEMMA_EMBEDDING:
case LLM_ARCH_DREAM:
case LLM_ARCH_LLADA: