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[INFO|tokenization_utils_base.py:2024] 2024-01-18 19:25:40,385 >> loading file tokenizer.model [INFO|tokenization_utils_base.py:2024] 2024-01-18 19:25:40,385 >> loading file added_tokens.json [INFO|tokenization_utils_base.py:2024] 2024-01-18 19:25:40,385 >> loading file special_tokens_map.json [INFO|tokenization_utils_base.py:2024] 2024-01-18 19:25:40,385 >> loading file tokenizer_config.json [INFO|tokenization_utils_base.py:2024] 2024-01-18 19:25:40,385 >> loading file tokenizer.json [INFO|configuration_utils.py:737] 2024-01-18 19:25:40,429 >> loading configuration file ./models/LMCocktail-10.7B-v1/config.json [INFO|configuration_utils.py:802] 2024-01-18 19:25:40,430 >> Model config LlamaConfig { "_name_or_path": "./models/LMCocktail-10.7B-v1", "architectures": [ "LlamaForCausalLM" ], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 1, "eos_token_id": 2, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 4096, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 48, "num_key_value_heads": 8, "pad_token_id": 2, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 10000.0, "tie_word_embeddings": false, "torch_dtype": "float16", "transformers_version": "4.36.2", "use_cache": true, "vocab_size": 32000 } [INFO|modeling_utils.py:3341] 2024-01-18 19:25:40,446 >> loading weights file ./models/LMCocktail-10.7B-v1/model.safetensors.index.json [INFO|modeling_utils.py:1341] 2024-01-18 19:25:40,447 >> Instantiating LlamaForCausalLM model under default dtype torch.float16. [INFO|configuration_utils.py:826] 2024-01-18 19:25:40,447 >> Generate config GenerationConfig { "bos_token_id": 1, "eos_token_id": 2, "pad_token_id": 2 } Loading checkpoint shards: 0%| | 0/5 [00:00<?, ?it/s] Loading checkpoint shards: 20%|ββ | 1/5 [00:00<00:00, 6.36it/s] Loading checkpoint shards: 40%|ββββ | 2/5 [00:00<00:00, 6.36it/s] Loading checkpoint shards: 60%|ββββββ | 3/5 [00:00<00:00, 6.37it/s] Loading checkpoint shards: 80%|ββββββββ | 4/5 [00:00<00:00, 6.28it/s] Loading checkpoint shards: 100%|ββββββββββ| 5/5 [00:00<00:00, 6.33it/s] Loading checkpoint shards: 100%|ββββββββββ| 5/5 [00:00<00:00, 6.33it/s] [INFO|modeling_utils.py:4185] 2024-01-18 19:25:41,404 >> All model checkpoint weights were used when initializing LlamaForCausalLM. [INFO|modeling_utils.py:4193] 2024-01-18 19:25:41,404 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at ./models/LMCocktail-10.7B-v1. If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training. [INFO|configuration_utils.py:779] 2024-01-18 19:25:41,407 >> loading configuration file ./models/LMCocktail-10.7B-v1/generation_config.json [INFO|configuration_utils.py:826] 2024-01-18 19:25:41,408 >> Generate config GenerationConfig { "bos_token_id": 1, "eos_token_id": 2, "pad_token_id": 2, "use_cache": false } 01/18/2024 19:25:41 - INFO - llmtuner.model.adapter - Fine-tuning method: LoRA 01/18/2024 19:25:43 - INFO - llmtuner.model.adapter - Merged 1 adapter(s). 01/18/2024 19:25:43 - INFO - llmtuner.model.adapter - Loaded adapter(s): ./models/sft/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1-lora 01/18/2024 19:25:43 - INFO - llmtuner.model.loader - trainable params: 0 || all params: 10731524096 || trainable%: 0.0000 01/18/2024 19:25:43 - INFO - llmtuner.model.loader - This IS expected that the trainable params is 0 if you are using model for inference only. [INFO|configuration_utils.py:483] 2024-01-18 19:25:43,941 >> Configuration saved in ./models/export/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1/config.json [INFO|configuration_utils.py:594] 2024-01-18 19:25:43,941 >> Configuration saved in ./models/export/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1/generation_config.json [INFO|modeling_utils.py:2390] 2024-01-18 19:26:02,405 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 5 checkpoint shards. You can find where each parameters has been saved in the index located at ./models/export/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1/model.safetensors.index.json. [INFO|tokenization_utils_base.py:2432] 2024-01-18 19:26:02,406 >> tokenizer config file saved in ./models/export/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1/tokenizer_config.json [INFO|tokenization_utils_base.py:2441] 2024-01-18 19:26:02,406 >> Special tokens file saved in ./models/export/LMCocktail-10.7B-v1-sft-glaive-function-calling-v2-ep1/special_tokens_map.json |