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https://github.com/ggerganov/llama.cpp.git
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add metadata check
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@ -371,6 +371,8 @@ enum llm_kv {
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LLM_KV_TOKENIZER_SUFFIX_ID,
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LLM_KV_TOKENIZER_MIDDLE_ID,
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LLM_KV_TOKENIZER_EOT_ID,
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LLM_KV_TRAINING_TYPE,
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};
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static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
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@ -464,6 +466,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
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{ LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" },
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{ LLM_KV_TOKENIZER_MIDDLE_ID, "tokenizer.ggml.middle_token_id" },
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{ LLM_KV_TOKENIZER_EOT_ID, "tokenizer.ggml.eot_token_id" },
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{ LLM_KV_TRAINING_TYPE, "training.type" },
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};
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struct LLM_KV {
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@ -18519,8 +18523,6 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c
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static const int n_out_tensors = 5; // see llama_model
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LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
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// TODO: check lora base model arch
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ggml_context * ctx = nullptr;
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struct gguf_init_params meta_gguf_params = {
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/* .no_alloc = */ false,
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@ -18532,6 +18534,25 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c
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throw std::exception();
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}
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// check metadata
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{
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auto get_kv_str = [&](std::string key) -> std::string {
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std::vector<char> str_buf(32, 0); // we only get the arch, so no need big buffer here
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int id = gguf_find_key(ctx_gguf, key.c_str());
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return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf, id));
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};
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LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
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auto lora_arch_name = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE));
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auto lora_arch = llm_arch_from_string(lora_arch_name);
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if (lora_arch != model->arch) {
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throw std::runtime_error("model arch and LoRA arch mismatch");
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}
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auto train_type = get_kv_str(llm_kv(LLM_KV_TRAINING_TYPE));
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if (train_type != "finetune_lora") {
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throw std::runtime_error("expect training.type to be finetune_lora, but got: " + train_type);
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}
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}
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// calculate n_tensors_per_layer
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int n_tensors_per_layer = 0;
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{
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@ -18542,7 +18563,6 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c
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if (il == 0) n_tensors_per_layer++;
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}
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}
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// printf("n_tensors_per_layer %d\n", n_tensors_per_layer);
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// count layer buffer types
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std::map<ggml_backend_buffer_type_t, int> buft_tensor_count;
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