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config.json: 100%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 663/663 [00:00<00:00, 7.86MB/s]
[2025-10-22 12:55:24,766] [DEBUG] [axolotl.utils.config.log_gpu_memory_usage:127] [PID:2418] baseline 0.000GB ()
[2025-10-22 12:55:24,766] [INFO] [axolotl.cli.config.load_cfg:248] [PID:2418] config:
{
"activation_offloading": false,
"adapter": "lora",
"axolotl_config_path": "config.yaml",
"base_model": "Qwen/Qwen2.5-7B-Instruct",
"base_model_config": "Qwen/Qwen2.5-7B-Instruct",
"batch_size": 16,
"bf16": true,
"capabilities": {
"bf16": true,
"compute_capability": "sm_90",
"fp8": false,
"n_gpu": 1,
"n_node": 1
},
"context_parallel_size": 1,
"dataloader_num_workers": 1,
"dataloader_pin_memory": true,
"dataloader_prefetch_factor": 256,
"dataset_processes": 36,
"datasets": [
{
"message_property_mappings": {
"content": "content",
"role": "role"
},
"path": "/workspace/fine-tuning/data/data.json",
"trust_remote_code": false,
"type": "alpaca"
}
],
"ddp": false,
"device": "cuda:0",
"dion_rank_fraction": 1.0,
"dion_rank_multiple_of": 1,
"env_capabilities": {
"torch_version": "2.7.1"
},
"eval_batch_size": 4,
"eval_causal_lm_metrics": [
"sacrebleu",
"comet",
"ter",
"chrf"
],
"eval_max_new_tokens": 128,
"eval_table_size": 0,
"experimental_skip_move_to_device": true,
"flash_attention": true,
"fp16": false,
"gradient_accumulation_steps": 4,
"gradient_checkpointing": true,
"gradient_checkpointing_kwargs": {
"use_reentrant": true
},
"include_tkps": true,
"learning_rate": 0.0002,
"lisa_layers_attribute": "model.layers",
"load_best_model_at_end": false,
"load_in_4bit": false,
"load_in_8bit": true,
"local_rank": 0,
"logging_steps": 10,
"lora_alpha": 16,
"lora_dropout": 0.05,
"lora_r": 8,
"lora_target_modules": [
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"down_proj",
"up_proj"
],
"loraplus_lr_embedding": 1e-06,
"lr_scheduler": "cosine",
"mean_resizing_embeddings": false,
"micro_batch_size": 4,
"model_config_type": "qwen2",
"num_epochs": 3.0,
"optimizer": "adamw_bnb_8bit",
"output_dir": "/workspace/fine-tuning/output",
"pretrain_multipack_attn": true,
"profiler_steps_start": 0,
"qlora_sharded_model_loading": false,
"ray_num_workers": 1,
"resources_per_worker": {
"GPU": 1
},
"sample_packing_bin_size": 200,
"sample_packing_group_size": 100000,
"save_only_model": false,
"save_safetensors": true,
"save_steps": 200,
"save_total_limit": 2,
"sequence_len": 4096,
"shuffle_before_merging_datasets": false,
"shuffle_merged_datasets": true,
"skip_prepare_dataset": false,
"streaming_multipack_buffer_size": 10000,
"strict": false,
"tensor_parallel_size": 1,
"tiled_mlp_use_original_mlp": true,
"tokenizer_config": "Qwen/Qwen2.5-7B-Instruct",
"tokenizer_save_jinja_files": true,
"torch_dtype": "torch.bfloat16",
"train_on_inputs": false,
"trl": {
"log_completions": false,
"mask_truncated_completions": false,
"ref_model_mixup_alpha": 0.9,
"ref_model_sync_steps": 64,
"scale_rewards": true,
"sync_ref_model": false,
"use_vllm": false,
"vllm_server_host": "0.0.0.0",
"vllm_server_port": 8000
},
"use_ray": false,
"val_set_size": 0.0,
"vllm": {
"device": "auto",
"dtype": "auto",
"gpu_memory_utilization": 0.9,
"host": "0.0.0.0",
"port": 8000
},
"weight_decay": 0.0,
"world_size": 1
}
tokenizer_config.json: 0.00B [00:00, ?B/s]
tokenizer_config.json: 7.30kB [00:00, 45.7MB/s]
vocab.json: 0.00B [00:00, ?B/s]
vocab.json: 2.78MB [00:00, 112MB/s]
merges.txt: 0.00B [00:00, ?B/s]
merges.txt: 1.67MB [00:00, 129MB/s]
tokenizer.json: 0.00B [00:00, ?B/s]
tokenizer.json: 7.03MB [00:00, 109MB/s]
[2025-10-22 12:55:25,791] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:278] [PID:2418] EOS: 151645 / <|im_end|>
[2025-10-22 12:55:25,792] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:279] [PID:2418] BOS: None / None
[2025-10-22 12:55:25,792] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:280] [PID:2418] PAD: 151643 / <|endoftext|>
[2025-10-22 12:55:25,792] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:281] [PID:2418] UNK: None / None
[2025-10-22 12:55:25,792] [INFO] [axolotl.utils.data.shared.load_preprocessed_dataset:476] [PID:2418] Unable to find prepared dataset in last_run_prepared/a99a12059c50ab085817560a37dbde6c
[2025-10-22 12:55:25,792] [INFO] [axolotl.utils.data.sft._load_raw_datasets:320] [PID:2418] Loading raw datasets...
[2025-10-22 12:55:25,792] [WARNING] [axolotl.utils.data.sft._load_raw_datasets:322] [PID:2418] Processing datasets during training can lead to VRAM instability. Please pre-process your dataset using `axolotl preprocess path/to/config.yml`.
Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 333 examples [00:00, 21071.50 examples/s]
[2025-10-22 12:55:25,923] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:2418] Loading dataset: /workspace/fine-tuning/data/data.json with base_type: alpaca and prompt_style: None
Tokenizing Prompts (num_proc=36): 0%| | 0/333 [00:00<?, ? examples/s]
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Tokenizing Prompts (num_proc=36): 21%|ââââââââââââââââ | 70/333 [00:01<00:03, 77.51 examples/s]
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Tokenizing Prompts (num_proc=36): 32%|ââââââââââââââââââââââââ | 108/333 [00:01<00:02, 86.61 examples/s]
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Tokenizing Prompts (num_proc=36): 43%|ââââââââââââââââââââââââââââââââ | 144/333 [00:02<00:01, 102.80 examples/s]
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Tokenizing Prompts (num_proc=36): 65%|âââââââââââââââââââââââââââââââââââââââââââââââ | 216/333 [00:02<00:00, 117.53 examples/s]
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Tokenizing Prompts (num_proc=36): 76%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 252/333 [00:03<00:00, 113.43 examples/s]
Tokenizing Prompts (num_proc=36): 84%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 279/333 [00:03<00:00, 134.23 examples/s]
Tokenizing Prompts (num_proc=36): 89%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 297/333 [00:03<00:00, 114.85 examples/s]
Tokenizing Prompts (num_proc=36): 97%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 324/333 [00:03<00:00, 132.86 examples/s]
Tokenizing Prompts (num_proc=36): 100%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 333/333 [00:04<00:00, 80.47 examples/s]
[2025-10-22 12:55:30,239] [INFO] [axolotl.utils.data.utils.handle_long_seq_in_dataset:218] [PID:2418] min_input_len: 36
[2025-10-22 12:55:30,239] [INFO] [axolotl.utils.data.utils.handle_long_seq_in_dataset:220] [PID:2418] max_input_len: 1350
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Dropping Long Sequences (>4096) (num_proc=36): 75%|âââââââââââââââââââââââââââââââââââââââââââââ | 251/333 [00:00<00:00, 345.84 examples/s]
Dropping Long Sequences (>4096) (num_proc=36): 100%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 333/333 [00:01<00:00, 273.41 examples/s]
Saving the dataset (0/1 shards): 0%| | 0/333 [00:00<?, ? examples/s]
Saving the dataset (1/1 shards): 100%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 333/333 [00:00<00:00, 29276.08 examples/s]
Saving the dataset (1/1 shards): 100%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 333/333 [00:00<00:00, 28696.24 examples/s]
[2025-10-22 12:55:31,719] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:404] [PID:2418] total_num_tokens: 94_264
[2025-10-22 12:55:31,721] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:422] [PID:2418] `total_supervised_tokens: 82_964`
[2025-10-22 12:55:31,721] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:520] [PID:2418] total_num_steps: 63
[2025-10-22 12:55:31,721] [INFO] [axolotl.utils.data.sft._prepare_standard_dataset:121] [PID:2418] Maximum number of steps set at 63
[2025-10-22 12:55:31,750] [DEBUG] [axolotl.train.setup_model_and_tokenizer:65] [PID:2418] Loading tokenizer... Qwen/Qwen2.5-7B-Instruct
[2025-10-22 12:55:32,146] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:278] [PID:2418] EOS: 151645 / <|im_end|>
[2025-10-22 12:55:32,147] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:279] [PID:2418] BOS: None / None
[2025-10-22 12:55:32,147] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:280] [PID:2418] PAD: 151643 / <|endoftext|>
[2025-10-22 12:55:32,147] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:281] [PID:2418] UNK: None / None
[2025-10-22 12:55:32,147] [DEBUG] [axolotl.train.setup_model_and_tokenizer:74] [PID:2418] Loading model
[2025-10-22 12:55:32,205] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_evaluation_loop:87] [PID:2418] Patched Trainer.evaluation_loop with nanmean loss calculation
[2025-10-22 12:55:32,206] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_maybe_log_save_evaluate:138] [PID:2418] Patched Trainer._maybe_log_save_evaluate with nanmean loss calculation
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model-00001-of-00004.safetensors: 51%|ââââââââââââââââââââââââââââââââââââââââ | 2.02G/3.95G [00:11<00:07, 252MB/s]
model-00001-of-00004.safetensors: 53%|ââââââââââââââââââââââââââââââââââââââââââ | 2.08G/3.95G [00:11<00:06, 300MB/s]
model-00001-of-00004.safetensors: 54%|âââââââââââââââââââââââââââââââââââââââââââ | 2.15G/3.95G [00:11<00:05, 337MB/s]
model-00001-of-00004.safetensors: 56%|ââââââââââââââââââââââââââââââââââââââââââââ | 2.22G/3.95G [00:12<00:05, 304MB/s]
model-00001-of-00004.safetensors: 58%|ââââââââââââââââââââââââââââââââââââââââââââââ | 2.28G/3.95G [00:12<00:05, 295MB/s]
model-00001-of-00004.safetensors: 59%|ââââââââââââââââââââââââââââââââââââââââââââââ | 2.33G/3.95G [00:12<00:06, 241MB/s]
model-00001-of-00004.safetensors: 61%|ââââââââââââââââââââââââââââââââââââââââââââââââ | 2.42G/3.95G [00:12<00:05, 299MB/s]
model-00001-of-00004.safetensors: 62%|âââââââââââââââââââââââââââââââââââââââââââââââââ | 2.46G/3.95G [00:13<00:05, 252MB/s]
model-00001-of-00004.safetensors: 64%|âââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.54G/3.95G [00:13<00:04, 307MB/s]
model-00001-of-00004.safetensors: 66%|ââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.60G/3.95G [00:13<00:04, 292MB/s]
model-00001-of-00004.safetensors: 68%|âââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.67G/3.95G [00:13<00:03, 322MB/s]
model-00001-of-00004.safetensors: 69%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.74G/3.95G [00:13<00:03, 353MB/s]
model-00001-of-00004.safetensors: 71%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.79G/3.95G [00:13<00:03, 372MB/s]
model-00001-of-00004.safetensors: 72%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.86G/3.95G [00:14<00:02, 410MB/s]
model-00001-of-00004.safetensors: 74%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.90G/3.95G [00:14<00:02, 419MB/s]
model-00001-of-00004.safetensors: 75%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.97G/3.95G [00:14<00:02, 397MB/s]
model-00001-of-00004.safetensors: 77%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.04G/3.95G [00:14<00:02, 436MB/s]
model-00001-of-00004.safetensors: 78%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.10G/3.95G [00:14<00:01, 432MB/s]
model-00001-of-00004.safetensors: 80%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.16G/3.95G [00:14<00:01, 459MB/s]
model-00001-of-00004.safetensors: 82%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.23G/3.95G [00:14<00:01, 479MB/s]
model-00001-of-00004.safetensors: 84%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.30G/3.95G [00:15<00:01, 496MB/s]
model-00001-of-00004.safetensors: 85%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.36G/3.95G [00:15<00:01, 503MB/s]
model-00001-of-00004.safetensors: 87%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.43G/3.95G [00:15<00:01, 507MB/s]
model-00001-of-00004.safetensors: 89%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.50G/3.95G [00:15<00:00, 506MB/s]
model-00001-of-00004.safetensors: 90%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.57G/3.95G [00:15<00:00, 514MB/s]
model-00001-of-00004.safetensors: 93%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.68G/3.95G [00:15<00:00, 524MB/s]
model-00001-of-00004.safetensors: 95%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.74G/3.95G [00:15<00:00, 538MB/s]
model-00001-of-00004.safetensors: 97%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.81G/3.95G [00:15<00:00, 531MB/s]
model-00001-of-00004.safetensors: 98%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.88G/3.95G [00:16<00:00, 537MB/s]
model-00001-of-00004.safetensors: 100%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 3.95G/3.95G [00:16<00:00, 529MB/s]
model-00001-of-00004.safetensors: 100%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 3.95G/3.95G [00:16<00:00, 243MB/s]
model-00002-of-00004.safetensors: 0%| | 0.00/3.86G [00:00<?, ?B/s]
model-00002-of-00004.safetensors: 0%| | 3.45M/3.86G [00:01<19:55, 3.23MB/s]
model-00002-of-00004.safetensors: 3%|âââ | 107M/3.86G [00:01<00:39, 94.4MB/s]
model-00002-of-00004.safetensors: 5%|ââââ | 185M/3.86G [00:01<00:23, 154MB/s]
model-00002-of-00004.safetensors: 7%|ââââââ | 280M/3.86G [00:01<00:15, 224MB/s]
model-00002-of-00004.safetensors: 9%|âââââââ | 336M/3.86G [00:02<00:14, 250MB/s]
model-00002-of-00004.safetensors: 11%|âââââââââ | 425M/3.86G [00:02<00:14, 243MB/s]
model-00002-of-00004.safetensors: 13%|ââââââââââ | 492M/3.86G [00:02<00:12, 268MB/s]
model-00002-of-00004.safetensors: 14%|ââââââââââââ | 559M/3.86G [00:02<00:10, 302MB/s]
model-00002-of-00004.safetensors: 16%|âââââââââââââ | 626M/3.86G [00:02<00:09, 344MB/s]
model-00002-of-00004.safetensors: 18%|âââââââââââââââ | 697M/3.86G [00:03<00:08, 364MB/s]
model-00002-of-00004.safetensors: 20%|ââââââââââââââââ | 764M/3.86G [00:03<00:08, 354MB/s]
model-00002-of-00004.safetensors: 21%|âââââââââââââââââ | 831M/3.86G [00:03<00:07, 387MB/s]
model-00002-of-00004.safetensors: 23%|âââââââââââââââââââ | 898M/3.86G [00:03<00:07, 396MB/s]
model-00002-of-00004.safetensors: 25%|ââââââââââââââââââââ | 965M/3.86G [00:03<00:06, 423MB/s]
model-00002-of-00004.safetensors: 27%|âââââââââââââââââââââ | 1.03G/3.86G [00:04<00:08, 317MB/s]
model-00002-of-00004.safetensors: 28%|ââââââââââââââââââââââ | 1.08G/3.86G [00:04<00:11, 242MB/s]
model-00002-of-00004.safetensors: 29%|âââââââââââââââââââââââ | 1.13G/3.86G [00:04<00:10, 267MB/s]
model-00002-of-00004.safetensors: 31%|ââââââââââââââââââââââââ | 1.18G/3.86G [00:04<00:09, 298MB/s]
model-00002-of-00004.safetensors: 32%|âââââââââââââââââââââââââ | 1.24G/3.86G [00:04<00:07, 335MB/s]
model-00002-of-00004.safetensors: 33%|ââââââââââââââââââââââââââ | 1.29G/3.86G [00:04<00:08, 289MB/s]
model-00002-of-00004.safetensors: 35%|ââââââââââââââââââââââââââââ | 1.36G/3.86G [00:05<00:13, 184MB/s]
model-00002-of-00004.safetensors: 37%|âââââââââââââââââââââââââââââ | 1.41G/3.86G [00:05<00:11, 213MB/s]
model-00002-of-00004.safetensors: 38%|ââââââââââââââââââââââââââââââ | 1.48G/3.86G [00:05<00:09, 254MB/s]
model-00002-of-00004.safetensors: 40%|ââââââââââââââââââââââââââââââââ | 1.54G/3.86G [00:06<00:08, 277MB/s]
model-00002-of-00004.safetensors: 42%|âââââââââââââââââââââââââââââââââ | 1.61G/3.86G [00:06<00:07, 313MB/s]
model-00002-of-00004.safetensors: 43%|ââââââââââââââââââââââââââââââââââ | 1.68G/3.86G [00:06<00:06, 350MB/s]
model-00002-of-00004.safetensors: 45%|ââââââââââââââââââââââââââââââââââââ | 1.75G/3.86G [00:06<00:05, 377MB/s]
model-00002-of-00004.safetensors: 47%|âââââââââââââââââââââââââââââââââââââ | 1.81G/3.86G [00:06<00:05, 385MB/s]
model-00002-of-00004.safetensors: 49%|ââââââââââââââââââââââââââââââââââââââ | 1.88G/3.86G [00:06<00:04, 415MB/s]
model-00002-of-00004.safetensors: 50%|ââââââââââââââââââââââââââââââââââââââââ | 1.95G/3.86G [00:06<00:04, 426MB/s]
model-00002-of-00004.safetensors: 52%|âââââââââââââââââââââââââââââââââââââââââ | 2.01G/3.86G [00:07<00:04, 449MB/s]
model-00002-of-00004.safetensors: 54%|ââââââââââââââââââââââââââââââââââââââââââ | 2.08G/3.86G [00:07<00:03, 456MB/s]
model-00002-of-00004.safetensors: 56%|ââââââââââââââââââââââââââââââââââââââââââââ | 2.15G/3.86G [00:07<00:03, 478MB/s]
model-00002-of-00004.safetensors: 57%|âââââââââââââââââââââââââââââââââââââââââââââ | 2.22G/3.86G [00:07<00:03, 422MB/s]
model-00002-of-00004.safetensors: 59%|ââââââââââââââââââââââââââââââââââââââââââââââ | 2.28G/3.86G [00:07<00:03, 439MB/s]
model-00002-of-00004.safetensors: 61%|ââââââââââââââââââââââââââââââââââââââââââââââââ | 2.35G/3.86G [00:07<00:03, 422MB/s]
model-00002-of-00004.safetensors: 63%|âââââââââââââââââââââââââââââââââââââââââââââââââ | 2.42G/3.86G [00:08<00:03, 370MB/s]
model-00002-of-00004.safetensors: 64%|âââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.48G/3.86G [00:08<00:03, 364MB/s]
model-00002-of-00004.safetensors: 66%|ââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.55G/3.86G [00:08<00:03, 380MB/s]
model-00002-of-00004.safetensors: 68%|âââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.62G/3.86G [00:08<00:03, 398MB/s]
model-00002-of-00004.safetensors: 69%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.69G/3.86G [00:08<00:02, 422MB/s]
model-00002-of-00004.safetensors: 71%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.75G/3.86G [00:08<00:02, 406MB/s]
model-00002-of-00004.safetensors: 73%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.82G/3.86G [00:09<00:02, 399MB/s]
model-00002-of-00004.safetensors: 75%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.89G/3.86G [00:09<00:02, 403MB/s]
model-00002-of-00004.safetensors: 76%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.95G/3.86G [00:09<00:02, 441MB/s]
model-00002-of-00004.safetensors: 78%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.02G/3.86G [00:09<00:01, 457MB/s]
model-00002-of-00004.safetensors: 80%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.09G/3.86G [00:09<00:01, 474MB/s]
model-00002-of-00004.safetensors: 82%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.15G/3.86G [00:09<00:01, 465MB/s]
model-00002-of-00004.safetensors: 83%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.22G/3.86G [00:09<00:01, 474MB/s]
model-00002-of-00004.safetensors: 85%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.29G/3.86G [00:10<00:01, 487MB/s]
model-00002-of-00004.safetensors: 87%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.36G/3.86G [00:10<00:01, 480MB/s]
model-00002-of-00004.safetensors: 89%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.42G/3.86G [00:10<00:00, 480MB/s]
model-00002-of-00004.safetensors: 90%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.49G/3.86G [00:10<00:00, 476MB/s]
model-00002-of-00004.safetensors: 92%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.56G/3.86G [00:10<00:00, 485MB/s]
model-00002-of-00004.safetensors: 94%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.62G/3.86G [00:10<00:00, 454MB/s]
model-00002-of-00004.safetensors: 96%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.69G/3.86G [00:10<00:00, 461MB/s]
model-00002-of-00004.safetensors: 97%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.76G/3.86G [00:11<00:00, 475MB/s]
model-00002-of-00004.safetensors: 99%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.82G/3.86G [00:11<00:00, 446MB/s]
model-00002-of-00004.safetensors: 100%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 3.86G/3.86G [00:11<00:00, 444MB/s]
model-00002-of-00004.safetensors: 100%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 3.86G/3.86G [00:11<00:00, 341MB/s]
model-00003-of-00004.safetensors: 0%| | 0.00/3.86G [00:00<?, ?B/s]
model-00003-of-00004.safetensors: 0%| | 1.37M/3.86G [00:01<51:34, 1.25MB/s]
model-00003-of-00004.safetensors: 0%|â | 15.4M/3.86G [00:01<03:45, 17.1MB/s]
model-00003-of-00004.safetensors: 2%|ââ | 79.2M/3.86G [00:01<00:55, 67.9MB/s]
model-00003-of-00004.safetensors: 7%|ââââââ | 280M/3.86G [00:01<00:13, 258MB/s]
model-00003-of-00004.safetensors: 9%|âââââââ | 347M/3.86G [00:02<00:11, 295MB/s]
model-00003-of-00004.safetensors: 11%|âââââââââ | 414M/3.86G [00:02<00:12, 279MB/s]
model-00003-of-00004.safetensors: 12%|ââââââââââ | 478M/3.86G [00:02<00:12, 273MB/s]
model-00003-of-00004.safetensors: 14%|ââââââââââââ | 545M/3.86G [00:02<00:10, 319MB/s]
model-00003-of-00004.safetensors: 16%|âââââââââââââ | 612M/3.86G [00:02<00:09, 360MB/s]
model-00003-of-00004.safetensors: 18%|ââââââââââââââ | 678M/3.86G [00:02<00:08, 384MB/s]
model-00003-of-00004.safetensors: 19%|ââââââââââââââââ | 745M/3.86G [00:03<00:07, 418MB/s]
model-00003-of-00004.safetensors: 21%|âââââââââââââââââ | 812M/3.86G [00:03<00:09, 316MB/s]
model-00003-of-00004.safetensors: 23%|ââââââââââââââââââ | 879M/3.86G [00:03<00:08, 337MB/s]
model-00003-of-00004.safetensors: 24%|ââââââââââââââââââââ | 946M/3.86G [00:03<00:07, 373MB/s]
model-00003-of-00004.safetensors: 26%|âââââââââââââââââââââ | 1.01G/3.86G [00:04<00:09, 303MB/s]
model-00003-of-00004.safetensors: 28%|ââââââââââââââââââââââ | 1.07G/3.86G [00:04<00:09, 283MB/s]
model-00003-of-00004.safetensors: 29%|âââââââââââââââââââââââ | 1.13G/3.86G [00:04<00:10, 257MB/s]
model-00003-of-00004.safetensors: 30%|ââââââââââââââââââââââââ | 1.16G/3.86G [00:04<00:11, 227MB/s]
model-00003-of-00004.safetensors: 31%|ââââââââââââââââââââââââ | 1.19G/3.86G [00:05<00:14, 184MB/s]
model-00003-of-00004.safetensors: 31%|âââââââââââââââââââââââââ | 1.21G/3.86G [00:05<00:17, 154MB/s]
model-00003-of-00004.safetensors: 33%|ââââââââââââââââââââââââââ | 1.26G/3.86G [00:05<00:12, 200MB/s]
model-00003-of-00004.safetensors: 33%|ââââââââââââââââââââââââââ | 1.29G/3.86G [00:05<00:13, 192MB/s]
model-00003-of-00004.safetensors: 35%|ââââââââââââââââââââââââââââ | 1.35G/3.86G [00:05<00:10, 230MB/s]
model-00003-of-00004.safetensors: 37%|âââââââââââââââââââââââââââââ | 1.42G/3.86G [00:05<00:08, 283MB/s]
model-00003-of-00004.safetensors: 38%|ââââââââââââââââââââââââââââââ | 1.48G/3.86G [00:06<00:06, 345MB/s]
model-00003-of-00004.safetensors: 40%|ââââââââââââââââââââââââââââââââ | 1.55G/3.86G [00:06<00:05, 387MB/s]
model-00003-of-00004.safetensors: 42%|âââââââââââââââââââââââââââââââââ | 1.61G/3.86G [00:06<00:06, 343MB/s]
model-00003-of-00004.safetensors: 43%|ââââââââââââââââââââââââââââââââââ | 1.68G/3.86G [00:06<00:06, 356MB/s]
model-00003-of-00004.safetensors: 45%|ââââââââââââââââââââââââââââââââââââ | 1.75G/3.86G [00:06<00:05, 405MB/s]
model-00003-of-00004.safetensors: 47%|âââââââââââââââââââââââââââââââââââââ | 1.81G/3.86G [00:06<00:04, 427MB/s]
model-00003-of-00004.safetensors: 49%|ââââââââââââââââââââââââââââââââââââââ | 1.88G/3.86G [00:06<00:04, 429MB/s]
model-00003-of-00004.safetensors: 50%|ââââââââââââââââââââââââââââââââââââââââ | 1.95G/3.86G [00:07<00:04, 448MB/s]
model-00003-of-00004.safetensors: 52%|âââââââââââââââââââââââââââââââââââââââââ | 2.01G/3.86G [00:07<00:04, 448MB/s]
model-00003-of-00004.safetensors: 54%|ââââââââââââââââââââââââââââââââââââââââââ | 2.08G/3.86G [00:07<00:04, 380MB/s]
model-00003-of-00004.safetensors: 55%|ââââââââââââââââââââââââââââââââââââââââââââ | 2.14G/3.86G [00:07<00:05, 322MB/s]
model-00003-of-00004.safetensors: 57%|âââââââââââââââââââââââââââââââââââââââââââââ | 2.20G/3.86G [00:07<00:04, 362MB/s]
model-00003-of-00004.safetensors: 58%|ââââââââââââââââââââââââââââââââââââââââââââââ | 2.26G/3.86G [00:08<00:04, 364MB/s]
model-00003-of-00004.safetensors: 60%|âââââââââââââââââââââââââââââââââââââââââââââââ | 2.31G/3.86G [00:08<00:04, 365MB/s]
model-00003-of-00004.safetensors: 61%|ââââââââââââââââââââââââââââââââââââââââââââââââ | 2.37G/3.86G [00:08<00:03, 389MB/s]
model-00003-of-00004.safetensors: 63%|ââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.45G/3.86G [00:08<00:03, 385MB/s]
model-00003-of-00004.safetensors: 65%|âââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.50G/3.86G [00:08<00:03, 402MB/s]
model-00003-of-00004.safetensors: 66%|ââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.57G/3.86G [00:08<00:03, 427MB/s]
model-00003-of-00004.safetensors: 68%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.63G/3.86G [00:08<00:02, 442MB/s]
model-00003-of-00004.safetensors: 70%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.70G/3.86G [00:09<00:02, 454MB/s]
model-00003-of-00004.safetensors: 72%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.77G/3.86G [00:09<00:02, 463MB/s]
model-00003-of-00004.safetensors: 73%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.84G/3.86G [00:09<00:02, 471MB/s]
model-00003-of-00004.safetensors: 75%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.90G/3.86G [00:09<00:02, 469MB/s]
model-00003-of-00004.safetensors: 77%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.97G/3.86G [00:09<00:01, 448MB/s]
model-00003-of-00004.safetensors: 78%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.03G/3.86G [00:09<00:01, 429MB/s]
model-00003-of-00004.safetensors: 80%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.10G/3.86G [00:09<00:01, 425MB/s]
model-00003-of-00004.safetensors: 82%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.17G/3.86G [00:10<00:01, 433MB/s]
model-00003-of-00004.safetensors: 84%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.24G/3.86G [00:10<00:01, 436MB/s]
model-00003-of-00004.safetensors: 85%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.30G/3.86G [00:10<00:01, 418MB/s]
model-00003-of-00004.safetensors: 87%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.37G/3.86G [00:10<00:01, 423MB/s]
model-00003-of-00004.safetensors: 89%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.44G/3.86G [00:10<00:01, 427MB/s]
model-00003-of-00004.safetensors: 91%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.50G/3.86G [00:10<00:00, 429MB/s]
model-00003-of-00004.safetensors: 92%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.57G/3.86G [00:11<00:00, 440MB/s]
model-00003-of-00004.safetensors: 94%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.64G/3.86G [00:11<00:00, 409MB/s]
model-00003-of-00004.safetensors: 96%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.71G/3.86G [00:11<00:00, 424MB/s]
model-00003-of-00004.safetensors: 98%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.78G/3.86G [00:11<00:00, 415MB/s]
model-00003-of-00004.safetensors: 100%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 3.86G/3.86G [00:11<00:00, 392MB/s]
model-00003-of-00004.safetensors: 100%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 3.86G/3.86G [00:11<00:00, 327MB/s]
model-00004-of-00004.safetensors: 0%| | 0.00/3.56G [00:00<?, ?B/s]
model-00004-of-00004.safetensors: 0%| | 9.42k/3.56G [00:01<163:05:23, 6.06kB/s]
model-00004-of-00004.safetensors: 1%|â | 44.7M/3.56G [00:01<01:50, 31.8MB/s]
model-00004-of-00004.safetensors: 4%|ââââ | 145M/3.56G [00:02<00:38, 87.6MB/s]
model-00004-of-00004.safetensors: 6%|âââââ | 202M/3.56G [00:02<00:30, 110MB/s]
model-00004-of-00004.safetensors: 8%|ââââââ | 269M/3.56G [00:02<00:21, 150MB/s]
model-00004-of-00004.safetensors: 9%|ââââââââ | 336M/3.56G [00:03<00:18, 177MB/s]
model-00004-of-00004.safetensors: 11%|âââââââââ | 403M/3.56G [00:03<00:15, 198MB/s]
model-00004-of-00004.safetensors: 13%|âââââââââââ | 470M/3.56G [00:03<00:14, 219MB/s]
model-00004-of-00004.safetensors: 15%|ââââââââââââ | 538M/3.56G [00:03<00:11, 253MB/s]
model-00004-of-00004.safetensors: 17%|ââââââââââââââ | 604M/3.56G [00:04<00:10, 279MB/s]
model-00004-of-00004.safetensors: 19%|âââââââââââââââ | 671M/3.56G [00:04<00:09, 313MB/s]
model-00004-of-00004.safetensors: 21%|âââââââââââââââââ | 738M/3.56G [00:04<00:09, 305MB/s]
model-00004-of-00004.safetensors: 23%|ââââââââââââââââââ | 805M/3.56G [00:04<00:08, 331MB/s]
model-00004-of-00004.safetensors: 25%|ââââââââââââââââââââ | 873M/3.56G [00:04<00:09, 284MB/s]
model-00004-of-00004.safetensors: 27%|ââââââââââââââââââââââ | 970M/3.56G [00:05<00:13, 197MB/s]
model-00004-of-00004.safetensors: 29%|âââââââââââââââââââââââ | 1.04G/3.56G [00:05<00:11, 228MB/s]
model-00004-of-00004.safetensors: 31%|ââââââââââââââââââââââââ | 1.09G/3.56G [00:06<00:10, 230MB/s]
model-00004-of-00004.safetensors: 32%|âââââââââââââââââââââââââ | 1.13G/3.56G [00:06<00:09, 244MB/s]
model-00004-of-00004.safetensors: 34%|âââââââââââââââââââââââââââ | 1.20G/3.56G [00:06<00:08, 265MB/s]
model-00004-of-00004.safetensors: 36%|ââââââââââââââââââââââââââââ | 1.27G/3.56G [00:06<00:07, 296MB/s]
model-00004-of-00004.safetensors: 37%|âââââââââââââââââââââââââââââ | 1.31G/3.56G [00:06<00:07, 306MB/s]
model-00004-of-00004.safetensors: 38%|ââââââââââââââââââââââââââââââ | 1.35G/3.56G [00:06<00:07, 300MB/s]
model-00004-of-00004.safetensors: 40%|âââââââââââââââââââââââââââââââ | 1.41G/3.56G [00:07<00:12, 174MB/s]
model-00004-of-00004.safetensors: 42%|âââââââââââââââââââââââââââââââââ | 1.48G/3.56G [00:07<00:10, 205MB/s]
model-00004-of-00004.safetensors: 43%|ââââââââââââââââââââââââââââââââââ | 1.54G/3.56G [00:07<00:08, 237MB/s]
model-00004-of-00004.safetensors: 45%|ââââââââââââââââââââââââââââââââââââ | 1.61G/3.56G [00:08<00:07, 265MB/s]
model-00004-of-00004.safetensors: 47%|âââââââââââââââââââââââââââââââââââââ | 1.68G/3.56G [00:08<00:06, 307MB/s]
model-00004-of-00004.safetensors: 49%|âââââââââââââââââââââââââââââââââââââââ | 1.75G/3.56G [00:08<00:05, 344MB/s]
model-00004-of-00004.safetensors: 51%|ââââââââââââââââââââââââââââââââââââââââ | 1.81G/3.56G [00:08<00:04, 364MB/s]
model-00004-of-00004.safetensors: 53%|ââââââââââââââââââââââââââââââââââââââââââ | 1.88G/3.56G [00:08<00:04, 380MB/s]
model-00004-of-00004.safetensors: 55%|âââââââââââââââââââââââââââââââââââââââââââ | 1.95G/3.56G [00:08<00:04, 393MB/s]
model-00004-of-00004.safetensors: 57%|âââââââââââââââââââââââââââââââââââââââââââââ | 2.01G/3.56G [00:08<00:03, 408MB/s]
model-00004-of-00004.safetensors: 58%|ââââââââââââââââââââââââââââââââââââââââââââââ | 2.08G/3.56G [00:09<00:03, 386MB/s]
model-00004-of-00004.safetensors: 60%|âââââââââââââââââââââââââââââââââââââââââââââââ | 2.15G/3.56G [00:09<00:03, 408MB/s]
model-00004-of-00004.safetensors: 62%|âââââââââââââââââââââââââââââââââââââââââââââââââ | 2.20G/3.56G [00:09<00:03, 344MB/s]
model-00004-of-00004.safetensors: 63%|ââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.25G/3.56G [00:09<00:03, 353MB/s]
model-00004-of-00004.safetensors: 65%|âââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.31G/3.56G [00:09<00:03, 358MB/s]
model-00004-of-00004.safetensors: 66%|ââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.36G/3.56G [00:09<00:03, 359MB/s]
model-00004-of-00004.safetensors: 68%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.44G/3.56G [00:10<00:03, 366MB/s]
model-00004-of-00004.safetensors: 70%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.51G/3.56G [00:10<00:02, 359MB/s]
model-00004-of-00004.safetensors: 72%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.55G/3.56G [00:10<00:02, 355MB/s]
model-00004-of-00004.safetensors: 74%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.62G/3.56G [00:10<00:02, 381MB/s]
model-00004-of-00004.safetensors: 75%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.68G/3.56G [00:10<00:02, 384MB/s]
model-00004-of-00004.safetensors: 77%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.75G/3.56G [00:10<00:02, 389MB/s]
model-00004-of-00004.safetensors: 79%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.82G/3.56G [00:11<00:01, 409MB/s]
model-00004-of-00004.safetensors: 81%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.89G/3.56G [00:11<00:01, 402MB/s]
model-00004-of-00004.safetensors: 83%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 2.95G/3.56G [00:11<00:01, 401MB/s]
model-00004-of-00004.safetensors: 85%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.02G/3.56G [00:11<00:01, 415MB/s]
model-00004-of-00004.safetensors: 87%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.09G/3.56G [00:11<00:01, 423MB/s]
model-00004-of-00004.safetensors: 89%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.15G/3.56G [00:11<00:00, 423MB/s]
model-00004-of-00004.safetensors: 91%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.22G/3.56G [00:12<00:00, 416MB/s]
model-00004-of-00004.safetensors: 92%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.29G/3.56G [00:12<00:00, 431MB/s]
model-00004-of-00004.safetensors: 94%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.36G/3.56G [00:12<00:00, 417MB/s]
model-00004-of-00004.safetensors: 96%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.42G/3.56G [00:12<00:00, 425MB/s]
model-00004-of-00004.safetensors: 98%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3.49G/3.56G [00:12<00:00, 437MB/s]
model-00004-of-00004.safetensors: 100%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 3.56G/3.56G [00:12<00:00, 445MB/s]
model-00004-of-00004.safetensors: 100%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 3.56G/3.56G [00:12<00:00, 277MB/s]
Loading checkpoint shards: 0%| | 0/4 [00:00<?, ?it/s]
Loading checkpoint shards: 25%|âââââââââââââââââââââââ | 1/4 [00:06<00:18, 6.20s/it]
Loading checkpoint shards: 50%|ââââââââââââââââââââââââââââââââââââââââââââââ | 2/4 [00:12<00:12, 6.48s/it]
Loading checkpoint shards: 75%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 3/4 [00:20<00:06, 6.99s/it]
Loading checkpoint shards: 100%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 4/4 [00:25<00:00, 6.17s/it]
Loading checkpoint shards: 100%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 4/4 [00:25<00:00, 6.35s/it]
generation_config.json: 0%| | 0.00/243 [00:00<?, ?B/s]
generation_config.json: 100%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 243/243 [00:00<00:00, 2.95MB/s]
[2025-10-22 12:56:50,899] [INFO] [axolotl.loaders.model._prepare_model_for_quantization:863] [PID:2418] converting PEFT model w/ prepare_model_for_kbit_training
[2025-10-22 12:56:50,901] [INFO] [axolotl.loaders.model._configure_embedding_dtypes:345] [PID:2418] Converting modules to torch.bfloat16
[2025-10-22 12:56:50,903] [DEBUG] [axolotl.loaders.model.log_gpu_memory_usage:127] [PID:2418] Memory usage after model load 11.676GB (+11.676GB allocated, +13.172GB reserved)
trainable params: 20,185,088 || all params: 7,635,801,600 || trainable%: 0.2643
[2025-10-22 12:56:51,121] [DEBUG] [axolotl.loaders.model.log_gpu_memory_usage:127] [PID:2418] after adapters 8.565GB (+8.565GB allocated, +13.248GB reserved)
[2025-10-22 12:56:57,347] [INFO] [axolotl.train.save_initial_configs:398] [PID:2418] Pre-saving adapter config to /workspace/fine-tuning/output...
[2025-10-22 12:56:57,347] [INFO] [axolotl.train.save_initial_configs:402] [PID:2418] Pre-saving tokenizer to /workspace/fine-tuning/output...
[2025-10-22 12:56:57,464] [INFO] [axolotl.train.save_initial_configs:407] [PID:2418] Pre-saving model config to /workspace/fine-tuning/output...
[2025-10-22 12:56:57,466] [INFO] [axolotl.train.execute_training:196] [PID:2418] Starting trainer...
0%| | 0/63 [00:00<?, ?it/s][2025-10-22 12:56:58,802] [WARNING] [py.warnings._showwarnmsg:110] [PID:2418] /root/miniconda3/envs/py3.11/lib/python3.11/site-packages/bitsandbytes/autograd/_functions.py:186: UserWarning: MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization
warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
2%|ââ | 1/63 [00:04<04:23, 4.24s/it]
3%|ââââ | 2/63 [00:07<03:35, 3.53s/it]
5%|ââââââ | 3/63 [00:10<03:29, 3.50s/it]
6%|ââââââââ | 4/63 [00:13<03:15, 3.31s/it]
8%|ââââââââââ | 5/63 [00:16<03:06, 3.22s/it]
10%|ââââââââââââ | 6/63 [00:19<03:01, 3.19s/it]
11%|âââââââââââââ | 7/63 [00:22<02:50, 3.04s/it]
13%|âââââââââââââââ | 8/63 [00:25<02:46, 3.03s/it]
14%|âââââââââââââââââ | 9/63 [00:28<02:39, 2.95s/it]
16%|âââââââââââââââââââ | 10/63 [00:32<02:49, 3.20s/it]
{'loss': 2.8315, 'grad_norm': 0.5804110765457153, 'learning_rate': 0.00019357168190404936, 'memory/max_active (GiB)': 19.67, 'memory/max_allocated (GiB)': 19.67, 'memory/device_reserved (GiB)': 21.65, 'tokens_per_second_per_gpu': 1089.23, 'epoch': 0.48}
16%|âââââââââââââââââââ | 10/63 [00:32<02:49, 3.20s/it]
17%|âââââââââââââââââââââ | 11/63 [00:35<02:52, 3.32s/it]
19%|ââââââââââââââââââââââ | 12/63 [00:38<02:41, 3.17s/it]
21%|ââââââââââââââââââââââââ | 13/63 [00:41<02:33, 3.08s/it]
22%|ââââââââââââââââââââââââââ | 14/63 [00:44<02:23, 2.93s/it]
24%|ââââââââââââââââââââââââââââ | 15/63 [00:47<02:21, 2.95s/it]
25%|ââââââââââââââââââââââââââââââ | 16/63 [00:50<02:20, 2.98s/it]
27%|ââââââââââââââââââââââââââââââââ | 17/63 [00:52<02:13, 2.91s/it]
29%|ââââââââââââââââââââââââââââââââââ | 18/63 [00:55<02:11, 2.93s/it]
30%|âââââââââââââââââââââââââââââââââââ | 19/63 [00:58<02:06, 2.87s/it]
32%|âââââââââââââââââââââââââââââââââââââ | 20/63 [01:01<02:02, 2.85s/it]
{'loss': 2.5331, 'grad_norm': 0.5418248772621155, 'learning_rate': 0.00016405931786981755, 'memory/max_active (GiB)': 17.12, 'memory/max_allocated (GiB)': 17.12, 'memory/device_reserved (GiB)': 21.65, 'tokens_per_second_per_gpu': 1399.77, 'epoch': 0.95}
32%|âââââââââââââââââââââââââââââââââââââ | 20/63 [01:01<02:02, 2.85s/it]
33%|âââââââââââââââââââââââââââââââââââââââ | 21/63 [01:04<01:58, 2.82s/it]
35%|âââââââââââââââââââââââââââââââââââââââââ | 22/63 [01:07<02:00, 2.94s/it]
37%|âââââââââââââââââââââââââââââââââââââââââââ | 23/63 [01:09<01:53, 2.83s/it]
38%|âââââââââââââââââââââââââââââââââââââââââââââ | 24/63 [01:13<01:57, 3.01s/it]
40%|ââââââââââââââââââââââââââââââââââââââââââââââ | 25/63 [01:16<01:51, 2.93s/it]
41%|ââââââââââââââââââââââââââââââââââââââââââââââââ | 26/63 [01:18<01:46, 2.87s/it]
43%|ââââââââââââââââââââââââââââââââââââââââââââââââââ | 27/63 [01:21<01:45, 2.93s/it]
44%|ââââââââââââââââââââââââââââââââââââââââââââââââââââ | 28/63 [01:25<01:51, 3.19s/it]
46%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 29/63 [01:28<01:46, 3.15s/it]
48%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 30/63 [01:31<01:38, 2.97s/it]
{'loss': 2.2908, 'grad_norm': 0.926287829875946, 'learning_rate': 0.00011792807588107357, 'memory/max_active (GiB)': 19.67, 'memory/max_allocated (GiB)': 19.67, 'memory/device_reserved (GiB)': 21.66, 'tokens_per_second_per_gpu': 1503.62, 'epoch': 1.43}
48%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 30/63 [01:31<01:38, 2.97s/it]
49%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 31/63 [01:33<01:30, 2.84s/it]
51%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 32/63 [01:36<01:28, 2.85s/it]
52%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 33/63 [01:39<01:25, 2.84s/it]
54%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 34/63 [01:42<01:23, 2.87s/it]
56%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 35/63 [01:45<01:17, 2.78s/it]
57%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 36/63 [01:48<01:17, 2.87s/it]
59%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 37/63 [01:50<01:13, 2.84s/it]
60%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 38/63 [01:53<01:12, 2.90s/it]
62%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 39/63 [01:56<01:10, 2.93s/it]
63%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 40/63 [01:59<01:07, 2.95s/it]
{'loss': 2.3613, 'grad_norm': 0.741166353225708, 'learning_rate': 6.714576180891654e-05, 'memory/max_active (GiB)': 13.69, 'memory/max_allocated (GiB)': 13.69, 'memory/device_reserved (GiB)': 21.66, 'tokens_per_second_per_gpu': 1292.51, 'epoch': 1.9}
63%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 40/63 [01:59<01:07, 2.95s/it]
65%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 41/63 [02:02<01:05, 2.97s/it]
67%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 42/63 [02:05<01:01, 2.91s/it]
68%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 43/63 [02:08<01:00, 3.02s/it]
70%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 44/63 [02:12<01:00, 3.20s/it]
71%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 45/63 [02:15<00:56, 3.11s/it]
73%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 46/63 [02:18<00:52, 3.11s/it]
75%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 47/63 [02:21<00:49, 3.08s/it]
76%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 48/63 [02:25<00:48, 3.23s/it]
78%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 49/63 [02:28<00:43, 3.10s/it]
79%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 50/63 [02:31<00:39, 3.07s/it]
{'loss': 2.1182, 'grad_norm': 0.8406546115875244, 'learning_rate': 2.4886806912948035e-05, 'memory/max_active (GiB)': 19.67, 'memory/max_allocated (GiB)': 19.67, 'memory/device_reserved (GiB)': 21.66, 'tokens_per_second_per_gpu': 1380.03, 'epoch': 2.38}
79%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 50/63 [02:31<00:39, 3.07s/it]
81%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 51/63 [02:33<00:35, 2.97s/it]
83%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 52/63 [02:36<00:32, 2.93s/it]
84%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 53/63 [02:39<00:29, 2.92s/it]
86%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 54/63 [02:42<00:26, 2.92s/it]
87%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 55/63 [02:45<00:23, 2.91s/it]
89%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 56/63 [02:48<00:20, 2.91s/it]
90%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 57/63 [02:51<00:18, 3.04s/it]
92%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 58/63 [02:54<00:14, 2.99s/it]
94%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 59/63 [02:57<00:11, 2.95s/it]
95%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 60/63 [03:00<00:08, 2.95s/it]
{'loss': 2.1277, 'grad_norm': 0.7622830271720886, 'learning_rate': 2.1144314904642195e-06, 'memory/max_active (GiB)': 17.48, 'memory/max_allocated (GiB)': 17.48, 'memory/device_reserved (GiB)': 21.66, 'tokens_per_second_per_gpu': 1322.61, 'epoch': 2.86}
95%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 60/63 [03:00<00:08, 2.95s/it]
97%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 61/63 [03:03<00:05, 2.92s/it]
98%|âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ | 62/63 [03:05<00:02, 2.91s/it]
100%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 63/63 [03:08<00:00, 2.87s/it][2025-10-22 13:00:06,761] [INFO] [axolotl.core.trainers.base._save:671] [PID:2418] Saving model checkpoint to /workspace/fine-tuning/output/checkpoint-63
{'train_runtime': 189.206, 'train_samples_per_second': 5.328, 'train_steps_per_second': 0.333, 'train_loss': 2.3706827390761602, 'memory/max_active (GiB)': 13.15, 'memory/max_allocated (GiB)': 13.15, 'memory/device_reserved (GiB)': 21.66, 'epoch': 3.0}
100%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 63/63 [03:09<00:00, 2.87s/it]
100%|ââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ| 63/63 [03:09<00:00, 3.00s/it]
[2025-10-22 13:00:08,876] [INFO] [axolotl.train.save_trained_model:218] [PID:2418] Training completed! Saving trained model to /workspace/fine-tuning/output.
[2025-10-22 13:00:09,025] [INFO] [axolotl.train.save_trained_model:336] [PID:2418] Model successfully saved to /workspace/fine-tuning/output
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