/venv/main/lib/python3.12/site-packages/trl/__init__.py:203: UserWarning: TRL currently supports vLLM versions: 0.10.2, 0.11.0, 0.11.1, 0.11.2. You have version 0.13.0 installed. We recommend installing a supported version to avoid compatibility issues.
  if is_vllm_available():
Loading config from: configs/runtime_config.yaml
Random seed set to: 42
============================================================
Training Configuration:
  Framework: accelerate
  Method: sft
============================================================
W&B Project: aimo3-exp-1-train
W&B Run: qwen3-0.6b-sft-lora-20260113_061956-001
wandb: Currently logged in as: vinoku (vinokuteam) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
wandb: WARNING Using a boolean value for 'reinit' is deprecated. Use 'return_previous' or 'finish_previous' instead.
wandb: setting up run 3fkbe45r
wandb: Tracking run with wandb version 0.23.1
wandb: Run data is saved locally in /workspace/distributed-sft-rl-inference-pipeline/wandb/run-20260112_222013-3fkbe45r
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run qwen3-0.6b-sft-lora-20260113_061956-001
wandb: ⭐️ View project at https://wandb.ai/vinokuteam/aimo3-exp-1-train
wandb: 🚀 View run at https://wandb.ai/vinokuteam/aimo3-exp-1-train/runs/3fkbe45r
wandb: Detected [openai] in use.
wandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.
wandb: For more information, check out the docs at: https://weave-docs.wandb.ai/
W&B Run: qwen3-0.6b-sft-lora-20260113_061956-001
W&B URL: https://wandb.ai/vinokuteam/aimo3-exp-1-train/runs/3fkbe45r
W&B logging enabled (ID: 3fkbe45r)
Loading model: Qwen/Qwen3-0.6B
ZeRO-0 mode (no HfDeepSpeedConfig needed)
`torch_dtype` is deprecated! Use `dtype` instead!
Building datasets...
Loading dataset from HuggingFace: HAD653/GSM8K-OpenMath-MathReason-13k
Train size: 13857, Test size: 416
Dataset size after dropping NaN: 13857
Dataset size after dropping NaN: 416
Final train size: 13857, test size: 416

============================================================
SAMPLE TRAINING DATA (first example):
============================================================
<|im_start|>system
You are a helpful and reflective maths assistant. Please reason step by step to put the answer in \boxed{}.<|im_end|>
<|im_start|>user
Albert is wondering how much pizza he can eat in one day. He buys 2 large pizzas and 2 small pizzas. A large pizza has 16 slices and a small pizza has 8 slices. If he eats it all, how many pieces does he eat that day?<|im_end|>
<|im_start|>assistant
<think>
Problem:
Albert buys 2 large pizzas (16 slices each) and 2 small pizzas (8 slices each). Find total slices eaten.

Reasoning:
1. Large pizza slices = 2*16 = 32.
2. Small pizza slices = 2*8 = 16.
3. Total slices = 32+16 = 48.

Answer:
48
</think>

\boxed{48}<|im_end|>
============================================================

Creating LoRA configuration...
Using Accelerate + SFT training pipeline
Initializing SFTTrainer...

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Starting training...
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}.
[2026-01-12 22:20:47] WARNING accelerator.py:2164: Gradient accumulation steps mismatch: GradientAccumulationPlugin has 1, DeepSpeed config has 4. Using DeepSpeed's value.

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{'loss': 2.0751, 'grad_norm': 0.1237564780666222, 'learning_rate': 4.000000000000001e-06, 'entropy': 0.94697265625, 'num_tokens': 21152.0, 'mean_token_accuracy': 0.66548752784729, 'epoch': 0.01}

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{'eval_loss': 2.0517611503601074, 'eval_runtime': 13.824, 'eval_samples_per_second': 30.093, 'eval_steps_per_second': 15.046, 'eval_entropy': 0.9272273137019231, 'eval_num_tokens': 21152.0, 'eval_mean_token_accuracy': 0.6686571506926646, 'epoch': 0.01}

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{'train_runtime': 27.5551, 'train_samples_per_second': 2.903, 'train_steps_per_second': 0.363, 'train_loss': 2.0750574111938476, 'epoch': 0.01}

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Saving final model to: ./checkpoints/final_model
Training metrics: {'val_loss': 2.0517611503601074, 'train_loss': 2.0751}
Number of training samples: 13857
Saved metrics.json to checkpoints/final_model/metrics.json
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Warning: Could not create run tag: 409 Client Error: Conflict for url: https://huggingface.co/api/models/vinoku89/aimo3-exp-1/tag/main (Request ID: Root=1-696573e6-143cc58e016376b56072dfc8;2c01acd5-4334-4a67-912b-ea1bd7169a04)

Tag reference exists already
Updated 'latest' tag
Updated 'best' tag (val_loss: 2.0518 < 2.0519)
Upload complete: https://huggingface.co/vinoku89/aimo3-exp-1
Uploaded logs/runtime_config_20260113_061956.yaml to vinoku89/aimo3-exp-1 (main branch)

============================================================
Model pushed successfully!
============================================================
  URL: https://huggingface.co/vinoku89/aimo3-exp-1
  Run Tag: qwen0.6b-sft-e1-13.9k-loss2.05-20260112-001
  Tags: qwen0.6b-sft-e1-13.9k-loss2.05-20260112-001, latest, best (if lowest loss)

  To load this specific run:
    from peft import PeftModel
    model = PeftModel.from_pretrained('base_model', 'vinoku89/aimo3-exp-1', revision='qwen0.6b-sft-e1-13.9k-loss2.05-20260112-001')

  To load the best run:
    model = PeftModel.from_pretrained('base_model', 'vinoku89/aimo3-exp-1', revision='best')

  To load the latest run:
    model = PeftModel.from_pretrained('base_model', 'vinoku89/aimo3-exp-1', revision='latest')
============================================================

wandb: updating run metadata
wandb: uploading history steps 1-2, summary, console lines 52-52; uploading output.log; uploading wandb-summary.json
wandb: uploading output.log; uploading config.yaml
wandb: uploading console lines 54-89
wandb: 
wandb: Run history:
wandb:             eval/entropy ▁
wandb:                eval/loss ▁
wandb: eval/mean_token_accuracy ▁
wandb:          eval/num_tokens ▁
wandb:             eval/runtime ▁
wandb:  eval/samples_per_second ▁
wandb:    eval/steps_per_second ▁
wandb:            train/entropy ▁
wandb:              train/epoch ▁▁▁
wandb:        train/global_step ▁▁▁
wandb:                       +5 ...
wandb: 
wandb: Run summary:
wandb:             eval/entropy 0.92723
wandb:                eval/loss 2.05176
wandb: eval/mean_token_accuracy 0.66866
wandb:          eval/num_tokens 21152
wandb:             eval/runtime 13.824
wandb:  eval/samples_per_second 30.093
wandb:    eval/steps_per_second 15.046
wandb:               total_flos 67838115053568.0
wandb:            train/entropy 0.94697
wandb:              train/epoch 0.00577
wandb:                      +10 ...
wandb: 
wandb: 🚀 View run qwen3-0.6b-sft-lora-20260113_061956-001 at: https://wandb.ai/vinokuteam/aimo3-exp-1-train/runs/3fkbe45r
wandb: ⭐️ View project at: https://wandb.ai/vinokuteam/aimo3-exp-1-train
wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20260112_222013-3fkbe45r/logs
Training completed successfully!
[W112 22:21:34.601229775 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
[W112 22:21:36.393460385 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())