ggml-org
diff --git a/‎convert-hf-to-gguf.py
Copy file name to clipboardExpand all lines: convert-hf-to-gguf.py
+1-1Lines changed: 1 addition & 1 deletion b/‎convert-hf-to-gguf.py
Copy file name to clipboardExpand all lines: convert-hf-to-gguf.py
+1-1Lines changed: 1 addition & 1 deletion
diff --git a/‎llama.cpp
Copy file name to clipboardExpand all lines: llama.cpp
+20-21Lines changed: 20 additions & 21 deletions b/‎llama.cpp
Copy file name to clipboardExpand all lines: llama.cpp
+20-21Lines changed: 20 additions & 21 deletions
@@ -422,7 +422,7 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         #       don't edit the hashes manually!
         if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
             # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
-            res = "llama-bpe"         
+            res = "llama-bpe" 
         if chkhsh == "049ecf7629871e3041641907f3de7c733e4dbfdc736f57d882ba0b0845599754":
             # ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-base
             res = "deepseek-llm"
 
@@ -4653,16 +4653,7 @@ static void llm_load_vocab(
 
         // for now, only BPE models have pre-tokenizers
         if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
-            if (tokenizer_pre.empty()) {
-                LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
-                LLAMA_LOG_WARN("%s:                                             \n", __func__);
-                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
-                LLAMA_LOG_WARN("%s: GENERATION QUALITY WILL BE DEGRADED!        \n", __func__);
-                LLAMA_LOG_WARN("%s: CONSIDER REGENERATING THE MODEL             \n", __func__);
-                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
-                LLAMA_LOG_WARN("%s:                                             \n", __func__);
-                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            } else if (
+            if (
                     tokenizer_pre == "default") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
             } else if (
@@ -4715,7 +4706,8 @@ static void llm_load_vocab(
                 tokenizer_pre == "smaug-bpe") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMAUG;
             } else {
-                throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
+                LLAMA_LOG_WARN("%s: missing or unrecognized pre-tokenizer type, using: 'default'\n", __func__);
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
             }
         } else {
             vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
@@ -5569,7 +5561,7 @@ static bool llm_load_tensors(
                         layer.attn_norm_2   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-			layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff});
+                        layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff});
                         layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE,    "weight", i), {n_embd, n_ff});
 
                         layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN,        "weight", i), {n_ff, n_embd});
@@ -6631,7 +6623,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
         }
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
-        return -1;
+        throw;
     }
 
     return 0;
@@ -16254,16 +16246,23 @@ struct llama_model * llama_load_model_from_file(
         }
         model->rpc_servers.push_back(servers);
     }
-    int status = llama_model_load(path_model, *model, params);
-    GGML_ASSERT(status <= 0);
-    if (status < 0) {
-        if (status == -1) {
-            LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
-        } else if (status == -2) {
-            LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
+
+    try {
+        int status = llama_model_load(path_model, *model, params);
+        GGML_ASSERT(status <= 0);
+        if (status < 0) {
+            if (status == -1) {
+                LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
+            } else if (status == -2) {
+                LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
+            }
+            delete model;
+            return nullptr;
         }
+    } catch (...) {
+        LLAMA_LOG_ERROR("%s: exception loading model\n", __func__);
         delete model;
-        return nullptr;
+        throw;
     }
 
     return model;