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[INFO|2025-03-07 10:08:21] configuration_utils.py:771 >> Model config LlamaConfig {
"_name_or_path": "meta-llama/Llama-3.3-70B-Instruct",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"eos_token_id": [
128001,
128008,
128009
],
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 8192,
"initializer_range": 0.02,
"intermediate_size": 28672,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 64,
"num_hidden_layers": 80,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": {
"factor": 8.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_type": "llama3"
},
"rope_theta": 500000.0,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.49.0",
"use_cache": true,
"vocab_size": 128256
}
[INFO|2025-03-07 10:08:21] tokenization_utils_base.py:2050 >> loading file tokenizer.json from cache at /workspace/hf_home/hub/models--meta-llama--Llama-3.3-70B-Instruct/snapshots/6f6073b423013f6a7d4d9f39144961bfbfbc386b/tokenizer.json
[INFO|2025-03-07 10:08:21] tokenization_utils_base.py:2050 >> loading file tokenizer.model from cache at None
[INFO|2025-03-07 10:08:21] tokenization_utils_base.py:2050 >> loading file added_tokens.json from cache at None
[INFO|2025-03-07 10:08:21] tokenization_utils_base.py:2050 >> loading file special_tokens_map.json from cache at /workspace/hf_home/hub/models--meta-llama--Llama-3.3-70B-Instruct/snapshots/6f6073b423013f6a7d4d9f39144961bfbfbc386b/special_tokens_map.json
[INFO|2025-03-07 10:08:21] tokenization_utils_base.py:2050 >> loading file tokenizer_config.json from cache at /workspace/hf_home/hub/models--meta-llama--Llama-3.3-70B-Instruct/snapshots/6f6073b423013f6a7d4d9f39144961bfbfbc386b/tokenizer_config.json
[INFO|2025-03-07 10:08:21] tokenization_utils_base.py:2050 >> loading file chat_template.jinja from cache at None
[INFO|2025-03-07 10:08:21] tokenization_utils_base.py:2313 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[INFO|2025-03-07 10:08:22] configuration_utils.py:699 >> loading configuration file config.json from cache at /workspace/hf_home/hub/models--meta-llama--Llama-3.3-70B-Instruct/snapshots/6f6073b423013f6a7d4d9f39144961bfbfbc386b/config.json
[INFO|2025-03-07 10:08:22] configuration_utils.py:771 >> Model config LlamaConfig {
"_name_or_path": "meta-llama/Llama-3.3-70B-Instruct",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"eos_token_id": [
128001,
128008,
128009
],
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 8192,
"initializer_range": 0.02,
"intermediate_size": 28672,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 64,
"num_hidden_layers": 80,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": {
"factor": 8.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_type": "llama3"
},
"rope_theta": 500000.0,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.49.0",
"use_cache": true,
"vocab_size": 128256
}
[INFO|2025-03-07 10:08:22] tokenization_utils_base.py:2050 >> loading file tokenizer.json from cache at /workspace/hf_home/hub/models--meta-llama--Llama-3.3-70B-Instruct/snapshots/6f6073b423013f6a7d4d9f39144961bfbfbc386b/tokenizer.json
[INFO|2025-03-07 10:08:22] tokenization_utils_base.py:2050 >> loading file tokenizer.model from cache at None
[INFO|2025-03-07 10:08:22] tokenization_utils_base.py:2050 >> loading file added_tokens.json from cache at None
[INFO|2025-03-07 10:08:22] tokenization_utils_base.py:2050 >> loading file special_tokens_map.json from cache at /workspace/hf_home/hub/models--meta-llama--Llama-3.3-70B-Instruct/snapshots/6f6073b423013f6a7d4d9f39144961bfbfbc386b/special_tokens_map.json
[INFO|2025-03-07 10:08:22] tokenization_utils_base.py:2050 >> loading file tokenizer_config.json from cache at /workspace/hf_home/hub/models--meta-llama--Llama-3.3-70B-Instruct/snapshots/6f6073b423013f6a7d4d9f39144961bfbfbc386b/tokenizer_config.json
[INFO|2025-03-07 10:08:22] tokenization_utils_base.py:2050 >> loading file chat_template.jinja from cache at None
[INFO|2025-03-07 10:08:22] tokenization_utils_base.py:2313 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[INFO|2025-03-07 10:08:22] logging.py:157 >> Add <|eot_id|>,<|eom_id|> to stop words.
[INFO|2025-03-07 10:08:22] logging.py:157 >> Loading dataset jgayed/ets480...
[INFO|2025-03-07 10:08:27] configuration_utils.py:699 >> loading configuration file config.json from cache at /workspace/hf_home/hub/models--meta-llama--Llama-3.3-70B-Instruct/snapshots/6f6073b423013f6a7d4d9f39144961bfbfbc386b/config.json
[INFO|2025-03-07 10:08:27] configuration_utils.py:771 >> Model config LlamaConfig {
"_name_or_path": "meta-llama/Llama-3.3-70B-Instruct",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"eos_token_id": [
128001,
128008,
128009
],
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 8192,
"initializer_range": 0.02,
"intermediate_size": 28672,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 64,
"num_hidden_layers": 80,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": {
"factor": 8.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_type": "llama3"
},
"rope_theta": 500000.0,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.49.0",
"use_cache": true,
"vocab_size": 128256
}
[INFO|2025-03-07 10:08:27] logging.py:157 >> Quantizing model to 4 bit with bitsandbytes.
[INFO|2025-03-07 10:08:27] quantizer_bnb_4bit.py:276 >> The device_map was not initialized. Setting device_map to {'': 0}. If you want to use the model for inference, please set device_map ='auto'
[INFO|2025-03-07 10:08:27] modeling_utils.py:3982 >> loading weights file model.safetensors from cache at /workspace/hf_home/hub/models--meta-llama--Llama-3.3-70B-Instruct/snapshots/6f6073b423013f6a7d4d9f39144961bfbfbc386b/model.safetensors.index.json
[INFO|2025-03-07 10:08:27] modeling_utils.py:1633 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.
[INFO|2025-03-07 10:08:27] configuration_utils.py:1140 >> Generate config GenerationConfig {
"bos_token_id": 128000,
"eos_token_id": [
128001,
128008,
128009
]
}
[INFO|2025-03-07 10:09:51] modeling_utils.py:4970 >> All model checkpoint weights were used when initializing LlamaForCausalLM.
[INFO|2025-03-07 10:09:51] modeling_utils.py:4978 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at meta-llama/Llama-3.3-70B-Instruct.
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|2025-03-07 10:09:51] configuration_utils.py:1095 >> loading configuration file generation_config.json from cache at /workspace/hf_home/hub/models--meta-llama--Llama-3.3-70B-Instruct/snapshots/6f6073b423013f6a7d4d9f39144961bfbfbc386b/generation_config.json
[INFO|2025-03-07 10:09:51] configuration_utils.py:1140 >> Generate config GenerationConfig {
"bos_token_id": 128000,
"do_sample": true,
"eos_token_id": [
128001,
128008,
128009
],
"temperature": 0.6,
"top_p": 0.9
}
[INFO|2025-03-07 10:09:51] logging.py:157 >> Gradient checkpointing enabled.
[INFO|2025-03-07 10:09:51] logging.py:157 >> Using torch SDPA for faster training and inference.
[INFO|2025-03-07 10:09:51] logging.py:157 >> Upcasting trainable params to float32.
[INFO|2025-03-07 10:09:51] logging.py:157 >> Fine-tuning method: LoRA
[INFO|2025-03-07 10:09:51] logging.py:157 >> Found linear modules: up_proj,o_proj,v_proj,q_proj,k_proj,gate_proj,down_proj
[WARNING|2025-03-07 10:09:54] trainer.py:781 >> No label_names provided for model class `PeftModelForCausalLM`. Since `PeftModel` hides base models input arguments, if label_names is not given, label_names can't be set automatically within `Trainer`. Note that empty label_names list will be used instead.
[INFO|2025-03-07 10:09:56] logging.py:157 >> trainable params: 828,375,040 || all params: 276,735,205,376 || trainable%: 0.2993
[INFO|2025-03-07 10:09:56] trainer.py:746 >> Using auto half precision backend
[WARNING|2025-03-07 10:09:56] trainer.py:781 >> No label_names provided for model class `PeftModelForCausalLM`. Since `PeftModel` hides base models input arguments, if label_names is not given, label_names can't be set automatically within `Trainer`. Note that empty label_names list will be used instead.
[INFO|2025-03-07 10:09:56] deepspeed.py:334 >> Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the custom optimizer has both CPU and GPU implementation (except LAMB)
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