nohup: ignoring input
Loading tokenizer from Qwen/Qwen3-1.7B
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Loading multilingual SFT dataset from /scratch/nico/data/multilingual_v2_ci/sft_train.jsonl

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Generating train split: 89455 examples [00:01, 80582.97 examples/s]
Generating train split: 93371 examples [00:01, 77753.12 examples/s]
  Full dataset size: 93371
Formatting examples with Qwen chat template...

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Loading model: Qwen/Qwen3-1.7B

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[RANK 0] Padding-free training is enabled, but the attention implementation is not set to a supported flash attention variant. Padding-free training flattens batches into a single sequence, and only the following implementations are known to reliably support this: flash_attention_2, flash_attention_3, kernels-community/flash-attn2, kernels-community/flash-attn3, kernels-community/vllm-flash-attn3. Using other implementations may lead to unexpected behavior. To ensure compatibility, set `attn_implementation` in the model configuration to one of these supported options or verify that your attention mechanism can handle flattened sequences.
[RANK 0] You are using packing, but the attention implementation is not set to a supported flash attention variant. Packing gathers multiple samples into a single sequence, and only the following implementations are known to reliably support this: flash_attention_2, flash_attention_3, kernels-community/flash-attn2, kernels-community/flash-attn3, kernels-community/vllm-flash-attn3. Using other implementations may lead to cross-contamination between samples. To avoid this, either disable packing by setting `packing=False`, or set `attn_implementation` in the model configuration to one of these supported options.

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Starting multilingual SFT training...
[transformers] 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}.

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{'loss': '2.287', 'grad_norm': '21.88', 'learning_rate': '1.029e-07', 'entropy': '0.9958', 'num_tokens': '6.544e+05', 'mean_token_accuracy': '0.6042', 'epoch': '0.02726'}

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{'loss': '2.355', 'grad_norm': '28.12', 'learning_rate': '2.171e-07', 'entropy': '1.008', 'num_tokens': '1.302e+06', 'mean_token_accuracy': '0.601', 'epoch': '0.05451'}

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{'loss': '2.393', 'grad_norm': '26.25', 'learning_rate': '3.314e-07', 'entropy': '1.019', 'num_tokens': '1.949e+06', 'mean_token_accuracy': '0.5983', 'epoch': '0.08177'}

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{'loss': '2.445', 'grad_norm': '37.25', 'learning_rate': '4.457e-07', 'entropy': '1.023', 'num_tokens': '2.598e+06', 'mean_token_accuracy': '0.5971', 'epoch': '0.109'}

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{'loss': '2.369', 'grad_norm': '23', 'learning_rate': '5.6e-07', 'entropy': '1.024', 'num_tokens': '3.244e+06', 'mean_token_accuracy': '0.5976', 'epoch': '0.1363'}

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{'loss': '2.419', 'grad_norm': '29', 'learning_rate': '6.743e-07', 'entropy': '1.049', 'num_tokens': '3.891e+06', 'mean_token_accuracy': '0.5952', 'epoch': '0.1635'}

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{'loss': '2.403', 'grad_norm': '21', 'learning_rate': '7.886e-07', 'entropy': '1.056', 'num_tokens': '4.542e+06', 'mean_token_accuracy': '0.5937', 'epoch': '0.1908'}

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{'loss': '2.316', 'grad_norm': '26.25', 'learning_rate': '9.029e-07', 'entropy': '1.044', 'num_tokens': '5.19e+06', 'mean_token_accuracy': '0.5982', 'epoch': '0.2181'}

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{'loss': '2.317', 'grad_norm': '21.75', 'learning_rate': '1.017e-06', 'entropy': '1.038', 'num_tokens': '5.836e+06', 'mean_token_accuracy': '0.5991', 'epoch': '0.2453'}

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{'loss': '2.211', 'grad_norm': '24.88', 'learning_rate': '1.131e-06', 'entropy': '1.034', 'num_tokens': '6.489e+06', 'mean_token_accuracy': '0.6071', 'epoch': '0.2726'}

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{'loss': '2.269', 'grad_norm': '21.25', 'learning_rate': '1.246e-06', 'entropy': '1.076', 'num_tokens': '7.138e+06', 'mean_token_accuracy': '0.6004', 'epoch': '0.2998'}

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{'loss': '2.256', 'grad_norm': '24.88', 'learning_rate': '1.36e-06', 'entropy': '1.094', 'num_tokens': '7.792e+06', 'mean_token_accuracy': '0.6002', 'epoch': '0.3271'}

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{'loss': '2.167', 'grad_norm': '15.38', 'learning_rate': '1.474e-06', 'entropy': '1.1', 'num_tokens': '8.439e+06', 'mean_token_accuracy': '0.6024', 'epoch': '0.3543'}

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{'loss': '2.099', 'grad_norm': '14.81', 'learning_rate': '1.589e-06', 'entropy': '1.108', 'num_tokens': '9.092e+06', 'mean_token_accuracy': '0.608', 'epoch': '0.3816'}

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{'loss': '2.158', 'grad_norm': '19.62', 'learning_rate': '1.703e-06', 'entropy': '1.169', 'num_tokens': '9.74e+06', 'mean_token_accuracy': '0.6006', 'epoch': '0.4089'}

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{'loss': '2.015', 'grad_norm': '14', 'learning_rate': '1.817e-06', 'entropy': '1.158', 'num_tokens': '1.039e+07', 'mean_token_accuracy': '0.6119', 'epoch': '0.4361'}

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{'loss': '1.938', 'grad_norm': '13.62', 'learning_rate': '1.931e-06', 'entropy': '1.167', 'num_tokens': '1.104e+07', 'mean_token_accuracy': '0.6176', 'epoch': '0.4634'}

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{'loss': '1.929', 'grad_norm': '11.62', 'learning_rate': '2.046e-06', 'entropy': '1.214', 'num_tokens': '1.169e+07', 'mean_token_accuracy': '0.6164', 'epoch': '0.4906'}

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{'loss': '1.826', 'grad_norm': '10', 'learning_rate': '2.16e-06', 'entropy': '1.233', 'num_tokens': '1.234e+07', 'mean_token_accuracy': '0.6231', 'epoch': '0.5179'}

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{'loss': '1.735', 'grad_norm': '12', 'learning_rate': '2.274e-06', 'entropy': '1.245', 'num_tokens': '1.299e+07', 'mean_token_accuracy': '0.6286', 'epoch': '0.5451'}

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{'loss': '1.667', 'grad_norm': '7.531', 'learning_rate': '2.389e-06', 'entropy': '1.27', 'num_tokens': '1.363e+07', 'mean_token_accuracy': '0.6359', 'epoch': '0.5724'}

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{'loss': '1.617', 'grad_norm': '6', 'learning_rate': '2.503e-06', 'entropy': '1.296', 'num_tokens': '1.428e+07', 'mean_token_accuracy': '0.643', 'epoch': '0.5997'}

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{'loss': '1.639', 'grad_norm': '5.281', 'learning_rate': '2.617e-06', 'entropy': '1.36', 'num_tokens': '1.494e+07', 'mean_token_accuracy': '0.6416', 'epoch': '0.6269'}

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{'loss': '1.553', 'grad_norm': '5.188', 'learning_rate': '2.731e-06', 'entropy': '1.329', 'num_tokens': '1.559e+07', 'mean_token_accuracy': '0.6492', 'epoch': '0.6542'}

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{'loss': '1.545', 'grad_norm': '5.531', 'learning_rate': '2.846e-06', 'entropy': '1.374', 'num_tokens': '1.624e+07', 'mean_token_accuracy': '0.6514', 'epoch': '0.6814'}

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{'loss': '1.504', 'grad_norm': '6', 'learning_rate': '2.96e-06', 'entropy': '1.365', 'num_tokens': '1.689e+07', 'mean_token_accuracy': '0.6556', 'epoch': '0.7087'}

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{'loss': '1.474', 'grad_norm': '5.875', 'learning_rate': '3.074e-06', 'entropy': '1.354', 'num_tokens': '1.754e+07', 'mean_token_accuracy': '0.6621', 'epoch': '0.7359'}

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{'loss': '1.438', 'grad_norm': '6.438', 'learning_rate': '3.189e-06', 'entropy': '1.333', 'num_tokens': '1.82e+07', 'mean_token_accuracy': '0.6696', 'epoch': '0.7632'}

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{'loss': '1.487', 'grad_norm': '4.594', 'learning_rate': '3.303e-06', 'entropy': '1.39', 'num_tokens': '1.883e+07', 'mean_token_accuracy': '0.6686', 'epoch': '0.7905'}

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{'loss': '1.438', 'grad_norm': '1.992', 'learning_rate': '3.417e-06', 'entropy': '1.354', 'num_tokens': '1.948e+07', 'mean_token_accuracy': '0.6723', 'epoch': '0.8177'}

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{'loss': '1.424', 'grad_norm': '1.781', 'learning_rate': '3.531e-06', 'entropy': '1.35', 'num_tokens': '2.013e+07', 'mean_token_accuracy': '0.6767', 'epoch': '0.845'}

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{'loss': '1.361', 'grad_norm': '1.391', 'learning_rate': '3.646e-06', 'entropy': '1.303', 'num_tokens': '2.079e+07', 'mean_token_accuracy': '0.6824', 'epoch': '0.8722'}

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{'loss': '1.38', 'grad_norm': '1.383', 'learning_rate': '3.76e-06', 'entropy': '1.339', 'num_tokens': '2.144e+07', 'mean_token_accuracy': '0.683', 'epoch': '0.8995'}

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{'loss': '1.38', 'grad_norm': '1.281', 'learning_rate': '3.874e-06', 'entropy': '1.342', 'num_tokens': '2.209e+07', 'mean_token_accuracy': '0.6807', 'epoch': '0.9267'}

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{'loss': '1.342', 'grad_norm': '1.305', 'learning_rate': '3.989e-06', 'entropy': '1.312', 'num_tokens': '2.274e+07', 'mean_token_accuracy': '0.6888', 'epoch': '0.954'}

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{'loss': '1.344', 'grad_norm': '1.453', 'learning_rate': '4.103e-06', 'entropy': '1.32', 'num_tokens': '2.339e+07', 'mean_token_accuracy': '0.688', 'epoch': '0.9813'}

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Writing model shards:   0%|                              | 0/1 [00:00<?, ?it/s]

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{'loss': '1.347', 'grad_norm': '1.148', 'learning_rate': '4.217e-06', 'entropy': '1.327', 'num_tokens': '2.403e+07', 'mean_token_accuracy': '0.6872', 'epoch': '1.008'}

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{'loss': '1.347', 'grad_norm': '1.086', 'learning_rate': '4.331e-06', 'entropy': '1.33', 'num_tokens': '2.468e+07', 'mean_token_accuracy': '0.6895', 'epoch': '1.035'}

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{'loss': '1.309', 'grad_norm': '1.375', 'learning_rate': '4.446e-06', 'entropy': '1.289', 'num_tokens': '2.533e+07', 'mean_token_accuracy': '0.6927', 'epoch': '1.063'}

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{'loss': '1.342', 'grad_norm': '1.102', 'learning_rate': '4.56e-06', 'entropy': '1.326', 'num_tokens': '2.598e+07', 'mean_token_accuracy': '0.6882', 'epoch': '1.09'}

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{'loss': '1.323', 'grad_norm': '1.031', 'learning_rate': '4.674e-06', 'entropy': '1.308', 'num_tokens': '2.662e+07', 'mean_token_accuracy': '0.6947', 'epoch': '1.117'}

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{'loss': '1.312', 'grad_norm': '1.062', 'learning_rate': '4.789e-06', 'entropy': '1.301', 'num_tokens': '2.727e+07', 'mean_token_accuracy': '0.6931', 'epoch': '1.144'}

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{'loss': '1.318', 'grad_norm': '1.375', 'learning_rate': '4.903e-06', 'entropy': '1.303', 'num_tokens': '2.792e+07', 'mean_token_accuracy': '0.6949', 'epoch': '1.172'}

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{'loss': '1.289', 'grad_norm': '1.234', 'learning_rate': '5.017e-06', 'entropy': '1.276', 'num_tokens': '2.857e+07', 'mean_token_accuracy': '0.6952', 'epoch': '1.199'}

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{'loss': '1.25', 'grad_norm': '1.07', 'learning_rate': '5.131e-06', 'entropy': '1.242', 'num_tokens': '2.922e+07', 'mean_token_accuracy': '0.701', 'epoch': '1.226'}

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{'loss': '1.269', 'grad_norm': '1.703', 'learning_rate': '5.246e-06', 'entropy': '1.257', 'num_tokens': '2.987e+07', 'mean_token_accuracy': '0.6976', 'epoch': '1.253'}

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{'loss': '1.287', 'grad_norm': '1.344', 'learning_rate': '5.36e-06', 'entropy': '1.281', 'num_tokens': '3.052e+07', 'mean_token_accuracy': '0.6993', 'epoch': '1.281'}

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{'loss': '1.268', 'grad_norm': '1.172', 'learning_rate': '5.474e-06', 'entropy': '1.258', 'num_tokens': '3.117e+07', 'mean_token_accuracy': '0.6998', 'epoch': '1.308'}

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{'loss': '1.297', 'grad_norm': '1.094', 'learning_rate': '5.589e-06', 'entropy': '1.282', 'num_tokens': '3.182e+07', 'mean_token_accuracy': '0.6981', 'epoch': '1.335'}

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{'loss': '1.31', 'grad_norm': '1.062', 'learning_rate': '5.703e-06', 'entropy': '1.296', 'num_tokens': '3.247e+07', 'mean_token_accuracy': '0.6965', 'epoch': '1.363'}

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{'loss': '1.265', 'grad_norm': '1.047', 'learning_rate': '5.817e-06', 'entropy': '1.256', 'num_tokens': '3.312e+07', 'mean_token_accuracy': '0.702', 'epoch': '1.39'}

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{'loss': '1.279', 'grad_norm': '1.219', 'learning_rate': '5.931e-06', 'entropy': '1.27', 'num_tokens': '3.377e+07', 'mean_token_accuracy': '0.7015', 'epoch': '1.417'}

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{'loss': '1.272', 'grad_norm': '1.109', 'learning_rate': '6.046e-06', 'entropy': '1.265', 'num_tokens': '3.442e+07', 'mean_token_accuracy': '0.7005', 'epoch': '1.444'}

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{'loss': '1.253', 'grad_norm': '1.281', 'learning_rate': '6.16e-06', 'entropy': '1.251', 'num_tokens': '3.507e+07', 'mean_token_accuracy': '0.7026', 'epoch': '1.472'}

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{'loss': '1.291', 'grad_norm': '1.07', 'learning_rate': '6.274e-06', 'entropy': '1.283', 'num_tokens': '3.572e+07', 'mean_token_accuracy': '0.7015', 'epoch': '1.499'}

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{'loss': '1.257', 'grad_norm': '1.117', 'learning_rate': '6.389e-06', 'entropy': '1.248', 'num_tokens': '3.638e+07', 'mean_token_accuracy': '0.7024', 'epoch': '1.526'}

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{'loss': '1.263', 'grad_norm': '1.086', 'learning_rate': '6.503e-06', 'entropy': '1.258', 'num_tokens': '3.703e+07', 'mean_token_accuracy': '0.7022', 'epoch': '1.553'}

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{'loss': '1.24', 'grad_norm': '1.07', 'learning_rate': '6.617e-06', 'entropy': '1.235', 'num_tokens': '3.768e+07', 'mean_token_accuracy': '0.7057', 'epoch': '1.581'}

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{'loss': '1.265', 'grad_norm': '1.031', 'learning_rate': '6.731e-06', 'entropy': '1.262', 'num_tokens': '3.833e+07', 'mean_token_accuracy': '0.7034', 'epoch': '1.608'}

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{'loss': '1.278', 'grad_norm': '1.477', 'learning_rate': '6.846e-06', 'entropy': '1.272', 'num_tokens': '3.898e+07', 'mean_token_accuracy': '0.7013', 'epoch': '1.635'}

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{'loss': '1.228', 'grad_norm': '1.055', 'learning_rate': '6.96e-06', 'entropy': '1.221', 'num_tokens': '3.962e+07', 'mean_token_accuracy': '0.709', 'epoch': '1.662'}

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{'loss': '1.269', 'grad_norm': '1.055', 'learning_rate': '7.074e-06', 'entropy': '1.264', 'num_tokens': '4.028e+07', 'mean_token_accuracy': '0.7042', 'epoch': '1.69'}

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{'loss': '1.258', 'grad_norm': '1.125', 'learning_rate': '7.189e-06', 'entropy': '1.252', 'num_tokens': '4.092e+07', 'mean_token_accuracy': '0.7046', 'epoch': '1.717'}

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{'loss': '1.247', 'grad_norm': '1.07', 'learning_rate': '7.303e-06', 'entropy': '1.247', 'num_tokens': '4.157e+07', 'mean_token_accuracy': '0.7051', 'epoch': '1.744'}

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{'loss': '1.261', 'grad_norm': '1.344', 'learning_rate': '7.417e-06', 'entropy': '1.254', 'num_tokens': '4.222e+07', 'mean_token_accuracy': '0.7057', 'epoch': '1.771'}

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{'loss': '1.247', 'grad_norm': '1.086', 'learning_rate': '7.531e-06', 'entropy': '1.246', 'num_tokens': '4.286e+07', 'mean_token_accuracy': '0.7052', 'epoch': '1.799'}

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{'loss': '1.219', 'grad_norm': '1.008', 'learning_rate': '7.646e-06', 'entropy': '1.217', 'num_tokens': '4.352e+07', 'mean_token_accuracy': '0.708', 'epoch': '1.826'}

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{'loss': '1.229', 'grad_norm': '1.086', 'learning_rate': '7.76e-06', 'entropy': '1.225', 'num_tokens': '4.416e+07', 'mean_token_accuracy': '0.7096', 'epoch': '1.853'}

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{'loss': '1.248', 'grad_norm': '1.047', 'learning_rate': '7.874e-06', 'entropy': '1.245', 'num_tokens': '4.481e+07', 'mean_token_accuracy': '0.7061', 'epoch': '1.88'}

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{'loss': '1.215', 'grad_norm': '0.9297', 'learning_rate': '7.989e-06', 'entropy': '1.21', 'num_tokens': '4.546e+07', 'mean_token_accuracy': '0.7115', 'epoch': '1.908'}

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{'loss': '1.217', 'grad_norm': '0.9922', 'learning_rate': '8.103e-06', 'entropy': '1.211', 'num_tokens': '4.611e+07', 'mean_token_accuracy': '0.7112', 'epoch': '1.935'}

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{'loss': '1.259', 'grad_norm': '1.055', 'learning_rate': '8.217e-06', 'entropy': '1.25', 'num_tokens': '4.676e+07', 'mean_token_accuracy': '0.7066', 'epoch': '1.962'}

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{'loss': '1.256', 'grad_norm': '1.055', 'learning_rate': '8.331e-06', 'entropy': '1.254', 'num_tokens': '4.741e+07', 'mean_token_accuracy': '0.7046', 'epoch': '1.989'}

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Writing model shards:   0%|                              | 0/1 [00:00<?, ?it/s]

Writing model shards: 100%|██████████████████████| 1/1 [00:08<00:00,  8.79s/it]
Writing model shards: 100%|██████████████████████| 1/1 [00:08<00:00,  8.79s/it]

 67%|███████████████████████▎           | 735/1101 [2:27:05<1:50:10, 18.06s/it]
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{'loss': '1.223', 'grad_norm': '0.9922', 'learning_rate': '8.446e-06', 'entropy': '1.223', 'num_tokens': '4.806e+07', 'mean_token_accuracy': '0.7112', 'epoch': '2.016'}

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{'loss': '1.205', 'grad_norm': '1.023', 'learning_rate': '8.56e-06', 'entropy': '1.204', 'num_tokens': '4.871e+07', 'mean_token_accuracy': '0.7127', 'epoch': '2.044'}

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{'loss': '1.184', 'grad_norm': '1.031', 'learning_rate': '8.674e-06', 'entropy': '1.181', 'num_tokens': '4.936e+07', 'mean_token_accuracy': '0.7155', 'epoch': '2.071'}

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{'loss': '1.237', 'grad_norm': '1.047', 'learning_rate': '8.789e-06', 'entropy': '1.231', 'num_tokens': '5.001e+07', 'mean_token_accuracy': '0.7089', 'epoch': '2.098'}

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{'loss': '1.237', 'grad_norm': '1.094', 'learning_rate': '8.903e-06', 'entropy': '1.235', 'num_tokens': '5.066e+07', 'mean_token_accuracy': '0.7083', 'epoch': '2.125'}

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{'loss': '1.217', 'grad_norm': '0.9766', 'learning_rate': '9.017e-06', 'entropy': '1.214', 'num_tokens': '5.131e+07', 'mean_token_accuracy': '0.7117', 'epoch': '2.153'}

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 72%|█████████████████████████▏         | 793/1101 [2:38:39<1:01:35, 12.00s/it]nohup: ignoring input
nohup: ignoring input
usage: sft_multilingual_impl.py [-h] [--train_file TRAIN_FILE]
                                [--max_samples MAX_SAMPLES]
                                [--output_dir OUTPUT_DIR]
                                [--num_train_epochs NUM_TRAIN_EPOCHS]
                                [--learning_rate LEARNING_RATE]
                                [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE]
                                [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS]
                                [--warmup_ratio WARMUP_RATIO] [--seed SEED]
sft_multilingual_impl.py: error: unrecognized arguments: --resume_from_checkpoint /scratch/checkpoints/multilingual_sft_v2_ci_bs1_ga16_lr2e5_ep3/checkpoint-734
usage: sft_multilingual_impl.py [-h] [--train_file TRAIN_FILE]
                                [--max_samples MAX_SAMPLES]
                                [--output_dir OUTPUT_DIR]
                                [--num_train_epochs NUM_TRAIN_EPOCHS]
                                [--learning_rate LEARNING_RATE]
                                [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE]
                                [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS]
                                [--warmup_ratio WARMUP_RATIO] [--seed SEED]
sft_multilingual_impl.py: error: unrecognized arguments: --resume_from_checkpoint /scratch/checkpoints/multilingual_sft_v2_ci_bs1_ga16_lr2e5_ep3/checkpoint-734
nohup: ignoring input
usage: sft_multilingual_impl.py [-h] [--train_file TRAIN_FILE]
                                [--max_samples MAX_SAMPLES]
                                [--output_dir OUTPUT_DIR]
                                [--num_train_epochs NUM_TRAIN_EPOCHS]
                                [--learning_rate LEARNING_RATE]
                                [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE]
                                [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS]
                                [--warmup_ratio WARMUP_RATIO] [--seed SEED]
sft_multilingual_impl.py: error: unrecognized arguments: --resume_from_checkpoint /scratch/checkpoints/multilingual_sft_v2_ci_bs1_ga16_lr2e5_ep3/checkpoint-734
nohup: ignoring input
usage: sft_multilingual_impl.py [-h] [--train_file TRAIN_FILE]
                                [--max_samples MAX_SAMPLES]
                                [--output_dir OUTPUT_DIR]
                                [--num_train_epochs NUM_TRAIN_EPOCHS]
                                [--learning_rate LEARNING_RATE]
                                [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE]
                                [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS]
                                [--warmup_ratio WARMUP_RATIO] [--seed SEED]
sft_multilingual_impl.py: error: unrecognized arguments: --resume_from_checkpoint /scratch/checkpoints/multilingual_sft_v2_ci_bs1_ga16_lr2e5_ep3/checkpoint-734
nohup: ignoring input
Loading tokenizer from Qwen/Qwen3-1.7B
Loading multilingual SFT dataset from /scratch/nico/data/multilingual_v2_ci/sft_train.jsonl
  Full dataset size: 93371
Formatting examples with Qwen chat template...
  After formatting: 93371 examples
Filtering examples > 4096 tokens...
  Removed 0 examples exceeding 4096 tokens
  Final training set: 93371 examples

Loading model: Qwen/Qwen3-1.7B
Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.

Loading weights:   0%|                                 | 0/311 [00:00<?, ?it/s]
Loading weights: 100%|█████████████████████| 311/311 [00:00<00:00, 6552.25it/s]
  W&B logging disabled
[RANK 0] Padding-free training is enabled, but the attention implementation is not set to a supported flash attention variant. Padding-free training flattens batches into a single sequence, and only the following implementations are known to reliably support this: flash_attention_2, flash_attention_3, kernels-community/flash-attn2, kernels-community/flash-attn3, kernels-community/vllm-flash-attn3. Using other implementations may lead to unexpected behavior. To ensure compatibility, set `attn_implementation` in the model configuration to one of these supported options or verify that your attention mechanism can handle flattened sequences.
[RANK 0] You are using packing, but the attention implementation is not set to a supported flash attention variant. Packing gathers multiple samples into a single sequence, and only the following implementations are known to reliably support this: flash_attention_2, flash_attention_3, kernels-community/flash-attn2, kernels-community/flash-attn3, kernels-community/vllm-flash-attn3. Using other implementations may lead to cross-contamination between samples. To avoid this, either disable packing by setting `packing=False`, or set `attn_implementation` in the model configuration to one of these supported options.

Starting multilingual SFT training...
[transformers] 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}.
[transformers] There were missing keys in the checkpoint model loaded: ['lm_head.weight'].
nohup: ignoring input
Loading tokenizer from Qwen/Qwen3-1.7B
Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
Loading multilingual SFT dataset from /scratch/nico/data/multilingual_v2_ci/sft_train.jsonl
  Full dataset size: 93371
Formatting examples with Qwen chat template...
nohup: ignoring input
  After formatting: 93371 examples
Filtering examples > 4096 tokens...

  0%|                                                 | 0/1101 [00:00<?, ?it/s]  Removed 0 examples exceeding 4096 tokens
  Final training set: 93371 examples

Loading model: Qwen/Qwen3-1.7B

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Loading weights: 100%|█████████████████████| 311/311 [00:00<00:00, 5992.22it/s]
  W&B logging disabled
[RANK 0] Padding-free training is enabled, but the attention implementation is not set to a supported flash attention variant. Padding-free training flattens batches into a single sequence, and only the following implementations are known to reliably support this: flash_attention_2, flash_attention_3, kernels-community/flash-attn2, kernels-community/flash-attn3, kernels-community/vllm-flash-attn3. Using other implementations may lead to unexpected behavior. To ensure compatibility, set `attn_implementation` in the model configuration to one of these supported options or verify that your attention mechanism can handle flattened sequences.
[RANK 0] You are using packing, but the attention implementation is not set to a supported flash attention variant. Packing gathers multiple samples into a single sequence, and only the following implementations are known to reliably support this: flash_attention_2, flash_attention_3, kernels-community/flash-attn2, kernels-community/flash-attn3, kernels-community/vllm-flash-attn3. Using other implementations may lead to cross-contamination between samples. To avoid this, either disable packing by setting `packing=False`, or set `attn_implementation` in the model configuration to one of these supported options.
Loading tokenizer from Qwen/Qwen3-1.7B

Starting multilingual SFT training...
[transformers] 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}.
Loading multilingual SFT dataset from /scratch/nico/data/multilingual_v2_ci/sft_train.jsonl
[transformers] There were missing keys in the checkpoint model loaded: ['lm_head.weight'].
  Full dataset size: 93371
Formatting examples with Qwen chat template...
  After formatting: 93371 examples
Filtering examples > 4096 tokens...
  Removed 0 examples exceeding 4096 tokens
  Final training set: 93371 examples

Loading model: Qwen/Qwen3-1.7B

 67%|██████████████████████████             | 735/1101 [00:12<00:06, 59.30it/s]Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.

Loading weights:   0%|                                 | 0/311 [00:00<?, ?it/s]
Loading weights: 100%|█████████████████████| 311/311 [00:00<00:00, 5928.47it/s]
  W&B logging disabled
[RANK 0] Padding-free training is enabled, but the attention implementation is not set to a supported flash attention variant. Padding-free training flattens batches into a single sequence, and only the following implementations are known to reliably support this: flash_attention_2, flash_attention_3, kernels-community/flash-attn2, kernels-community/flash-attn3, kernels-community/vllm-flash-attn3. Using other implementations may lead to unexpected behavior. To ensure compatibility, set `attn_implementation` in the model configuration to one of these supported options or verify that your attention mechanism can handle flattened sequences.
[RANK 0] You are using packing, but the attention implementation is not set to a supported flash attention variant. Packing gathers multiple samples into a single sequence, and only the following implementations are known to reliably support this: flash_attention_2, flash_attention_3, kernels-community/flash-attn2, kernels-community/flash-attn3, kernels-community/vllm-flash-attn3. Using other implementations may lead to cross-contamination between samples. To avoid this, either disable packing by setting `packing=False`, or set `attn_implementation` in the model configuration to one of these supported options.

  0%|                                                 | 0/1101 [00:00<?, ?it/s]Traceback (most recent call last):
  File "/scratch/nico/training/sft_multilingual_impl.py", line 210, in <module>
    main()
  File "/scratch/nico/training/sft_multilingual_impl.py", line 150, in main
    trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)
  File "/usr/local/lib/python3.12/dist-packages/transformers/trainer.py", line 1427, in train
    return inner_training_loop(
           ^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformers/trainer.py", line 1509, in _inner_training_loop
    self._run_epoch(
  File "/usr/local/lib/python3.12/dist-packages/transformers/trainer.py", line 1737, in _run_epoch
    tr_loss_step = self.training_step(model, inputs, num_items_in_batch)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/trl/trainer/sft_trainer.py", line 1395, in training_step
    return super().training_step(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformers/trainer.py", line 1909, in training_step
    loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/trl/trainer/sft_trainer.py", line 1283, in compute_loss
    (loss, outputs) = super().compute_loss(
                      ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformers/trainer.py", line 1981, in compute_loss
    outputs = model(**inputs)
              ^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1787, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/accelerate/utils/operations.py", line 823, in forward
    return model_forward(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/accelerate/utils/operations.py", line 811, in __call__
    return convert_to_fp32(self.model_forward(*args, **kwargs))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/amp/autocast_mode.py", line 44, in decorate_autocast
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformers/utils/generic.py", line 900, in wrapper
    output = func(self, *args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformers/models/qwen3/modeling_qwen3.py", line 509, in forward
    loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformers/loss/loss_utils.py", line 56, in ForCausalLMLoss
    logits = logits.float()
             ^^^^^^^^^^^^^^
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.31 GiB. GPU 0 has a total capacity of 39.49 GiB of which 1.39 GiB is free. Process 984 has 25.86 GiB memory in use. Including non-PyTorch memory, this process has 11.82 GiB memory in use. Process 1450 has 416.00 MiB memory in use. Of the allocated memory 11.30 GiB is allocated by PyTorch, and 29.39 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

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Traceback (most recent call last):
  File "/scratch/nico/training/sft_multilingual_impl.py", line 210, in <module>
    main()
  File "/scratch/nico/training/sft_multilingual_impl.py", line 142, in main
    trainer = SFTTrainer(
              ^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/trl/trainer/sft_trainer.py", line 990, in __init__
    super().__init__(
  File "/usr/local/lib/python3.12/dist-packages/transformers/trainer.py", line 480, in __init__
    self._move_model_to_device(model, args.device)
  File "/usr/local/lib/python3.12/dist-packages/transformers/trainer.py", line 4415, in _move_model_to_device
    model = model.to(device)
            ^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformers/modeling_utils.py", line 3650, in to
    return super().to(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1381, in to
    return self._apply(convert)
           ^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 933, in _apply
    module._apply(fn)
  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 933, in _apply
    module._apply(fn)
  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 933, in _apply
    module._apply(fn)
  [Previous line repeated 2 more times]
  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 964, in _apply
    param_applied = fn(param)
                    ^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1367, in convert
    return t.to(
           ^^^^^
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 24.00 MiB. GPU 0 has a total capacity of 39.49 GiB of which 4.38 MiB is free. Process 984 has 25.86 GiB memory in use. Process 1317 has 11.82 GiB memory in use. Including non-PyTorch memory, this process has 1.79 GiB memory in use. Of the allocated memory 1.38 GiB is allocated by PyTorch, and 5.93 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

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{'loss': '1.226', 'grad_norm': '0.9922', 'learning_rate': '8.446e-06', 'entropy': '1.228', 'num_tokens': '3.926e+05', 'mean_token_accuracy': '0.7088', 'epoch': '2.016'}

 67%|██████████████████████████▏            | 740/1101 [01:12<01:41,  3.56it/s]
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{'loss': '1.205', 'grad_norm': '1', 'learning_rate': '8.56e-06', 'entropy': '1.204', 'num_tokens': '1.044e+06', 'mean_token_accuracy': '0.7126', 'epoch': '2.044'}

 68%|██████████████████████████▌            | 750/1101 [03:12<33:59,  5.81s/it]
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{'loss': '1.183', 'grad_norm': '1.031', 'learning_rate': '8.674e-06', 'entropy': '1.181', 'num_tokens': '1.697e+06', 'mean_token_accuracy': '0.7156', 'epoch': '2.071'}

 69%|█████████████████████████▌           | 760/1101 [05:12<1:06:34, 11.71s/it]
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 70%|█████████████████████████▉           | 770/1101 [07:11<1:06:09, 11.99s/it]
                                                                               
{'loss': '1.237', 'grad_norm': '1.062', 'learning_rate': '8.789e-06', 'entropy': '1.231', 'num_tokens': '2.342e+06', 'mean_token_accuracy': '0.7089', 'epoch': '2.098'}

 70%|█████████████████████████▉           | 770/1101 [07:11<1:06:09, 11.99s/it]
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 71%|██████████████████████████▏          | 780/1101 [09:11<1:04:02, 11.97s/it]
                                                                               
{'loss': '1.237', 'grad_norm': '1.094', 'learning_rate': '8.903e-06', 'entropy': '1.235', 'num_tokens': '2.995e+06', 'mean_token_accuracy': '0.7084', 'epoch': '2.125'}

 71%|██████████████████████████▏          | 780/1101 [09:11<1:04:02, 11.97s/it]
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{'loss': '1.217', 'grad_norm': '0.9844', 'learning_rate': '9.017e-06', 'entropy': '1.214', 'num_tokens': '3.646e+06', 'mean_token_accuracy': '0.7117', 'epoch': '2.153'}

 72%|██████████████████████████▌          | 790/1101 [11:11<1:02:31, 12.06s/it]
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 73%|██████████████████████████▉          | 800/1101 [13:10<1:00:00, 11.96s/it]
                                                                               
{'loss': '1.229', 'grad_norm': '1.25', 'learning_rate': '9.131e-06', 'entropy': '1.23', 'num_tokens': '4.296e+06', 'mean_token_accuracy': '0.7098', 'epoch': '2.18'}

 73%|██████████████████████████▉          | 800/1101 [13:10<1:00:00, 11.96s/it]
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{'loss': '1.21', 'grad_norm': '1.023', 'learning_rate': '9.246e-06', 'entropy': '1.211', 'num_tokens': '4.944e+06', 'mean_token_accuracy': '0.7118', 'epoch': '2.207'}

 74%|████████████████████████████▋          | 810/1101 [15:09<57:36, 11.88s/it]
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 74%|█████████████████████████████          | 820/1101 [17:09<56:05, 11.98s/it]
                                                                               
{'loss': '1.154', 'grad_norm': '1.008', 'learning_rate': '9.36e-06', 'entropy': '1.152', 'num_tokens': '5.591e+06', 'mean_token_accuracy': '0.7216', 'epoch': '2.234'}

 74%|█████████████████████████████          | 820/1101 [17:09<56:05, 11.98s/it]
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{'loss': '1.215', 'grad_norm': '1.055', 'learning_rate': '9.474e-06', 'entropy': '1.207', 'num_tokens': '6.243e+06', 'mean_token_accuracy': '0.714', 'epoch': '2.262'}

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{'loss': '1.203', 'grad_norm': '0.9922', 'learning_rate': '9.589e-06', 'entropy': '1.205', 'num_tokens': '6.884e+06', 'mean_token_accuracy': '0.713', 'epoch': '2.289'}

 76%|█████████████████████████████▊         | 840/1101 [21:07<50:50, 11.69s/it]
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 77%|██████████████████████████████         | 850/1101 [23:06<49:49, 11.91s/it]
                                                                               
{'loss': '1.198', 'grad_norm': '1.016', 'learning_rate': '9.703e-06', 'entropy': '1.198', 'num_tokens': '7.533e+06', 'mean_token_accuracy': '0.7126', 'epoch': '2.316'}

 77%|██████████████████████████████         | 850/1101 [23:06<49:49, 11.91s/it]
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{'loss': '1.228', 'grad_norm': '1.086', 'learning_rate': '9.817e-06', 'entropy': '1.224', 'num_tokens': '8.183e+06', 'mean_token_accuracy': '0.71', 'epoch': '2.343'}

 78%|██████████████████████████████▍        | 860/1101 [25:06<47:33, 11.84s/it]
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{'loss': '1.222', 'grad_norm': '1.102', 'learning_rate': '9.931e-06', 'entropy': '1.218', 'num_tokens': '8.83e+06', 'mean_token_accuracy': '0.7119', 'epoch': '2.371'}

 79%|██████████████████████████████▊        | 870/1101 [27:05<45:59, 11.95s/it]
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 80%|███████████████████████████████▏       | 880/1101 [29:03<43:59, 11.94s/it]
                                                                               
{'loss': '1.194', 'grad_norm': '1.055', 'learning_rate': '1.005e-05', 'entropy': '1.196', 'num_tokens': '9.475e+06', 'mean_token_accuracy': '0.7176', 'epoch': '2.398'}

 80%|███████████████████████████████▏       | 880/1101 [29:03<43:59, 11.94s/it]
 80%|███████████████████████████████▏       | 881/1101 [29:15<43:46, 11.94s/it]
 80%|███████████████████████████████▏       | 882/1101 [29:27<43:53, 12.03s/it]
 80%|███████████████████████████████▎       | 883/1101 [29:39<43:16, 11.91s/it]
 80%|███████████████████████████████▎       | 884/1101 [29:51<43:05, 11.91s/it]
 80%|███████████████████████████████▎       | 885/1101 [30:03<43:16, 12.02s/it]
 80%|███████████████████████████████▍       | 886/1101 [30:15<42:55, 11.98s/it]
 81%|███████████████████████████████▍       | 887/1101 [30:27<42:56, 12.04s/it]
 81%|███████████████████████████████▍       | 888/1101 [30:39<42:36, 12.00s/it]
 81%|███████████████████████████████▍       | 889/1101 [30:51<42:04, 11.91s/it]
 81%|███████████████████████████████▌       | 890/1101 [31:03<41:59, 11.94s/it]
                                                                               
{'loss': '1.219', 'grad_norm': '1.047', 'learning_rate': '1.016e-05', 'entropy': '1.218', 'num_tokens': '1.013e+07', 'mean_token_accuracy': '0.7131', 'epoch': '2.425'}

 81%|███████████████████████████████▌       | 890/1101 [31:03<41:59, 11.94s/it]
 81%|███████████████████████████████▌       | 891/1101 [31:15<41:47, 11.94s/it]
 81%|███████████████████████████████▌       | 892/1101 [31:27<41:47, 12.00s/it]
 81%|███████████████████████████████▋       | 893/1101 [31:38<41:05, 11.86s/it]
 81%|███████████████████████████████▋       | 894/1101 [31:51<41:12, 11.94s/it]
 81%|███████████████████████████████▋       | 895/1101 [32:03<41:03, 11.96s/it]
 81%|███████████████████████████████▋       | 896/1101 [32:15<41:06, 12.03s/it]
 81%|███████████████████████████████▊       | 897/1101 [32:27<40:50, 12.01s/it]
 82%|███████████████████████████████▊       | 898/1101 [32:39<40:33, 11.99s/it]
 82%|███████████████████████████████▊       | 899/1101 [32:50<40:00, 11.88s/it]
 82%|███████████████████████████████▉       | 900/1101 [33:02<39:52, 11.90s/it]
                                                                               
{'loss': '1.247', 'grad_norm': '1.094', 'learning_rate': '1.027e-05', 'entropy': '1.244', 'num_tokens': '1.077e+07', 'mean_token_accuracy': '0.7092', 'epoch': '2.452'}

 82%|███████████████████████████████▉       | 900/1101 [33:02<39:52, 11.90s/it]
 82%|███████████████████████████████▉       | 901/1101 [33:14<39:25, 11.83s/it]
 82%|███████████████████████████████▉       | 902/1101 [33:25<38:20, 11.56s/it]
 82%|███████████████████████████████▉       | 903/1101 [33:37<38:33, 11.69s/it]
 82%|████████████████████████████████       | 904/1101 [33:49<38:38, 11.77s/it]
 82%|████████████████████████████████       | 905/1101 [34:01<38:38, 11.83s/it]
 82%|████████████████████████████████       | 906/1101 [34:13<38:44, 11.92s/it]
 82%|████████████████████████████████▏      | 907/1101 [34:25<38:33, 11.92s/it]
 82%|████████████████████████████████▏      | 908/1101 [34:37<38:34, 11.99s/it]
 83%|████████████████████████████████▏      | 909/1101 [34:49<38:19, 11.97s/it]
 83%|████████████████████████████████▏      | 910/1101 [35:01<38:15, 12.02s/it]
                                                                               
{'loss': '1.232', 'grad_norm': '1.008', 'learning_rate': '1.039e-05', 'entropy': '1.229', 'num_tokens': '1.142e+07', 'mean_token_accuracy': '0.7122', 'epoch': '2.48'}

 83%|████████████████████████████████▏      | 910/1101 [35:01<38:15, 12.02s/it]
 83%|████████████████████████████████▎      | 911/1101 [35:13<37:30, 11.84s/it]
 83%|████████████████████████████████▎      | 912/1101 [35:24<37:20, 11.86s/it]
 83%|████████████████████████████████▎      | 913/1101 [35:37<37:26, 11.95s/it]
 83%|████████████████████████████████▍      | 914/1101 [35:49<37:15, 11.95s/it]
 83%|████████████████████████████████▍      | 915/1101 [36:00<36:46, 11.86s/it]
 83%|████████████████████████████████▍      | 916/1101 [36:12<36:30, 11.84s/it]
 83%|████████████████████████████████▍      | 917/1101 [36:24<36:39, 11.96s/it]
 83%|████████████████████████████████▌      | 918/1101 [36:36<36:24, 11.94s/it]
 83%|████████████████████████████████▌      | 919/1101 [36:48<36:25, 12.01s/it]
 84%|████████████████████████████████▌      | 920/1101 [37:00<36:11, 12.00s/it]
                                                                               
{'loss': '1.182', 'grad_norm': '0.9844', 'learning_rate': '1.05e-05', 'entropy': '1.183', 'num_tokens': '1.207e+07', 'mean_token_accuracy': '0.7172', 'epoch': '2.507'}

 84%|████████████████████████████████▌      | 920/1101 [37:00<36:11, 12.00s/it]
 84%|████████████████████████████████▌      | 921/1101 [37:12<35:52, 11.96s/it]
 84%|████████████████████████████████▋      | 922/1101 [37:24<35:20, 11.85s/it]
 84%|████████████████████████████████▋      | 923/1101 [37:36<35:10, 11.86s/it]
 84%|████████████████████████████████▋      | 924/1101 [37:48<35:16, 11.96s/it]
 84%|████████████████████████████████▊      | 925/1101 [38:00<35:01, 11.94s/it]
 84%|████████████████████████████████▊      | 926/1101 [38:12<35:00, 12.00s/it]
 84%|████████████████████████████████▊      | 927/1101 [38:24<34:43, 11.97s/it]
 84%|████████████████████████████████▊      | 928/1101 [38:36<34:30, 11.97s/it]
 84%|████████████████████████████████▉      | 929/1101 [38:48<34:31, 12.04s/it]
 84%|████████████████████████████████▉      | 930/1101 [39:00<34:10, 11.99s/it]
                                                                               
{'loss': '1.217', 'grad_norm': '1.008', 'learning_rate': '1.062e-05', 'entropy': '1.212', 'num_tokens': '1.272e+07', 'mean_token_accuracy': '0.7126', 'epoch': '2.534'}

 84%|████████████████████████████████▉      | 930/1101 [39:00<34:10, 11.99s/it]
 85%|████████████████████████████████▉      | 931/1101 [39:12<34:13, 12.08s/it]
 85%|█████████████████████████████████      | 932/1101 [39:24<33:55, 12.04s/it]
 85%|█████████████████████████████████      | 933/1101 [39:36<33:42, 12.04s/it]
 85%|█████████████████████████████████      | 934/1101 [39:48<33:26, 12.01s/it]
 85%|█████████████████████████████████      | 935/1101 [40:00<33:11, 12.00s/it]
 85%|█████████████████████████████████▏     | 936/1101 [40:12<33:14, 12.09s/it]
 85%|█████████████████████████████████▏     | 937/1101 [40:24<32:53, 12.04s/it]
 85%|█████████████████████████████████▏     | 938/1101 [40:36<32:51, 12.09s/it]
 85%|█████████████████████████████████▎     | 939/1101 [40:48<32:33, 12.06s/it]
 85%|█████████████████████████████████▎     | 940/1101 [41:01<32:29, 12.11s/it]
                                                                               
{'loss': '1.184', 'grad_norm': '0.9258', 'learning_rate': '1.073e-05', 'entropy': '1.184', 'num_tokens': '1.337e+07', 'mean_token_accuracy': '0.7159', 'epoch': '2.561'}

 85%|█████████████████████████████████▎     | 940/1101 [41:01<32:29, 12.11s/it]
 85%|█████████████████████████████████▎     | 941/1101 [41:13<32:13, 12.08s/it]
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 86%|█████████████████████████████████▍     | 944/1101 [41:48<31:04, 11.88s/it]
 86%|█████████████████████████████████▍     | 945/1101 [42:00<31:09, 11.98s/it]
 86%|█████████████████████████████████▌     | 946/1101 [42:12<30:54, 11.97s/it]
 86%|█████████████████████████████████▌     | 947/1101 [42:24<30:50, 12.02s/it]
 86%|█████████████████████████████████▌     | 948/1101 [42:36<30:34, 11.99s/it]
 86%|█████████████████████████████████▌     | 949/1101 [42:48<30:19, 11.97s/it]
 86%|█████████████████████████████████▋     | 950/1101 [43:00<30:17, 12.04s/it]
                                                                               
{'loss': '1.21', 'grad_norm': '1.016', 'learning_rate': '1.085e-05', 'entropy': '1.208', 'num_tokens': '1.402e+07', 'mean_token_accuracy': '0.7146', 'epoch': '2.589'}

 86%|█████████████████████████████████▋     | 950/1101 [43:00<30:17, 12.04s/it]
 86%|█████████████████████████████████▋     | 951/1101 [43:12<29:50, 11.94s/it]
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 87%|█████████████████████████████████▊     | 953/1101 [43:36<29:29, 11.95s/it]
 87%|█████████████████████████████████▊     | 954/1101 [43:48<29:28, 12.03s/it]
 87%|█████████████████████████████████▊     | 955/1101 [44:00<29:11, 12.00s/it]
 87%|█████████████████████████████████▊     | 956/1101 [44:12<29:08, 12.06s/it]
 87%|█████████████████████████████████▉     | 957/1101 [44:24<28:53, 12.04s/it]
 87%|█████████████████████████████████▉     | 958/1101 [44:36<28:34, 11.99s/it]
 87%|█████████████████████████████████▉     | 959/1101 [44:48<28:30, 12.05s/it]
 87%|██████████████████████████████████     | 960/1101 [45:00<27:50, 11.85s/it]
                                                                               
{'loss': '1.151', 'grad_norm': '0.9648', 'learning_rate': '1.096e-05', 'entropy': '1.153', 'num_tokens': '1.467e+07', 'mean_token_accuracy': '0.7211', 'epoch': '2.616'}

 87%|██████████████████████████████████     | 960/1101 [45:00<27:50, 11.85s/it]
 87%|██████████████████████████████████     | 961/1101 [45:12<27:48, 11.92s/it]
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 87%|██████████████████████████████████     | 963/1101 [45:36<27:37, 12.01s/it]
 88%|██████████████████████████████████▏    | 964/1101 [45:48<27:22, 11.99s/it]
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 88%|██████████████████████████████████▏    | 966/1101 [46:12<27:03, 12.03s/it]
 88%|██████████████████████████████████▎    | 967/1101 [46:24<26:45, 11.98s/it]
 88%|██████████████████████████████████▎    | 968/1101 [46:35<26:15, 11.85s/it]
 88%|██████████████████████████████████▎    | 969/1101 [46:47<25:49, 11.74s/it]
 88%|██████████████████████████████████▎    | 970/1101 [46:59<25:54, 11.86s/it]
                                                                               
{'loss': '1.217', 'grad_norm': '0.9922', 'learning_rate': '1.107e-05', 'entropy': '1.217', 'num_tokens': '1.532e+07', 'mean_token_accuracy': '0.7137', 'epoch': '2.643'}

 88%|██████████████████████████████████▎    | 970/1101 [46:59<25:54, 11.86s/it]
 88%|██████████████████████████████████▍    | 971/1101 [47:11<25:44, 11.88s/it]
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 88%|██████████████████████████████████▍    | 973/1101 [47:35<25:32, 11.97s/it]
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 89%|██████████████████████████████████▌    | 975/1101 [47:59<25:04, 11.94s/it]
 89%|██████████████████████████████████▌    | 976/1101 [48:10<24:47, 11.90s/it]
 89%|██████████████████████████████████▌    | 977/1101 [48:22<24:26, 11.83s/it]
 89%|██████████████████████████████████▋    | 978/1101 [48:34<24:18, 11.86s/it]
 89%|██████████████████████████████████▋    | 979/1101 [48:46<24:07, 11.87s/it]
 89%|██████████████████████████████████▋    | 980/1101 [48:58<24:07, 11.96s/it]
                                                                               
{'loss': '1.172', 'grad_norm': '0.9922', 'learning_rate': '1.119e-05', 'entropy': '1.178', 'num_tokens': '1.597e+07', 'mean_token_accuracy': '0.7182', 'epoch': '2.671'}

 89%|██████████████████████████████████▋    | 980/1101 [48:58<24:07, 11.96s/it]
 89%|██████████████████████████████████▋    | 981/1101 [49:10<23:56, 11.97s/it]
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 89%|██████████████████████████████████▊    | 983/1101 [49:34<23:15, 11.82s/it]
 89%|██████████████████████████████████▊    | 984/1101 [49:46<23:15, 11.93s/it]
 89%|██████████████████████████████████▉    | 985/1101 [49:58<23:03, 11.93s/it]
 90%|██████████████████████████████████▉    | 986/1101 [50:10<22:49, 11.91s/it]
 90%|██████████████████████████████████▉    | 987/1101 [50:22<22:44, 11.97s/it]
 90%|██████████████████████████████████▉    | 988/1101 [50:33<22:13, 11.80s/it]
 90%|███████████████████████████████████    | 989/1101 [50:45<21:55, 11.75s/it]
 90%|███████████████████████████████████    | 990/1101 [50:57<21:50, 11.80s/it]
                                                                               
{'loss': '1.177', 'grad_norm': '0.9688', 'learning_rate': '1.13e-05', 'entropy': '1.174', 'num_tokens': '1.662e+07', 'mean_token_accuracy': '0.7186', 'epoch': '2.698'}

 90%|███████████████████████████████████    | 990/1101 [50:57<21:50, 11.80s/it]
 90%|███████████████████████████████████    | 991/1101 [51:09<21:49, 11.90s/it]
 90%|███████████████████████████████████▏   | 992/1101 [51:21<21:38, 11.91s/it]
 90%|███████████████████████████████████▏   | 993/1101 [51:33<21:34, 11.98s/it]
 90%|███████████████████████████████████▏   | 994/1101 [51:45<21:17, 11.94s/it]
 90%|███████████████████████████████████▏   | 995/1101 [51:57<21:09, 11.98s/it]
 90%|███████████████████████████████████▎   | 996/1101 [52:09<21:01, 12.01s/it]
 91%|███████████████████████████████████▎   | 997/1101 [52:21<20:45, 11.97s/it]
 91%|███████████████████████████████████▎   | 998/1101 [52:33<20:39, 12.03s/it]
 91%|███████████████████████████████████▍   | 999/1101 [52:45<20:24, 12.01s/it]
 91%|██████████████████████████████████▌   | 1000/1101 [52:57<20:18, 12.06s/it]
                                                                               
{'loss': '1.16', 'grad_norm': '1.062', 'learning_rate': '1.142e-05', 'entropy': '1.159', 'num_tokens': '1.727e+07', 'mean_token_accuracy': '0.7205', 'epoch': '2.725'}

 91%|██████████████████████████████████▌   | 1000/1101 [52:57<20:18, 12.06s/it]
 91%|██████████████████████████████████▌   | 1001/1101 [53:09<20:01, 12.01s/it]
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 91%|██████████████████████████████████▌   | 1003/1101 [53:33<19:39, 12.03s/it]
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 91%|██████████████████████████████████▋   | 1006/1101 [54:09<19:00, 12.01s/it]
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 92%|██████████████████████████████████▊   | 1010/1101 [54:57<18:16, 12.05s/it]
                                                                               
{'loss': '1.185', 'grad_norm': '1.031', 'learning_rate': '1.153e-05', 'entropy': '1.183', 'num_tokens': '1.792e+07', 'mean_token_accuracy': '0.7198', 'epoch': '2.752'}

 92%|██████████████████████████████████▊   | 1010/1101 [54:57<18:16, 12.05s/it]
 92%|██████████████████████████████████▉   | 1011/1101 [55:09<18:02, 12.03s/it]
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{'loss': '1.2', 'grad_norm': '1.055', 'learning_rate': '1.165e-05', 'entropy': '1.2', 'num_tokens': '1.858e+07', 'mean_token_accuracy': '0.719', 'epoch': '2.78'}

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{'loss': '1.169', 'grad_norm': '0.9609', 'learning_rate': '1.176e-05', 'entropy': '1.17', 'num_tokens': '1.923e+07', 'mean_token_accuracy': '0.7213', 'epoch': '2.807'}

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{'loss': '1.187', 'grad_norm': '0.9883', 'learning_rate': '1.187e-05', 'entropy': '1.185', 'num_tokens': '1.987e+07', 'mean_token_accuracy': '0.7195', 'epoch': '2.834'}

 94%|██████████████████████████████████  | 1040/1101 [1:00:56<12:11, 12.00s/it]
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{'loss': '1.164', 'grad_norm': '0.957', 'learning_rate': '1.199e-05', 'entropy': '1.166', 'num_tokens': '2.052e+07', 'mean_token_accuracy': '0.7222', 'epoch': '2.861'}

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{'loss': '1.168', 'grad_norm': '1.234', 'learning_rate': '1.21e-05', 'entropy': '1.17', 'num_tokens': '2.118e+07', 'mean_token_accuracy': '0.72', 'epoch': '2.889'}

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{'loss': '1.187', 'grad_norm': '1.164', 'learning_rate': '1.222e-05', 'entropy': '1.187', 'num_tokens': '2.183e+07', 'mean_token_accuracy': '0.7183', 'epoch': '2.916'}

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{'loss': '1.194', 'grad_norm': '1.086', 'learning_rate': '1.233e-05', 'entropy': '1.193', 'num_tokens': '2.248e+07', 'mean_token_accuracy': '0.7175', 'epoch': '2.943'}

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{'loss': '1.188', 'grad_norm': '1.219', 'learning_rate': '1.245e-05', 'entropy': '1.186', 'num_tokens': '2.313e+07', 'mean_token_accuracy': '0.72', 'epoch': '2.97'}

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{'loss': '1.18', 'grad_norm': '1.117', 'learning_rate': '1.256e-05', 'entropy': '1.181', 'num_tokens': '2.378e+07', 'mean_token_accuracy': '0.7191', 'epoch': '2.998'}

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Writing model shards:   0%|                              | 0/1 [00:00<?, ?it/s]

Writing model shards: 100%|██████████████████████| 1/1 [00:08<00:00,  8.98s/it]
Writing model shards: 100%|██████████████████████| 1/1 [00:08<00:00,  8.98s/it]

                                                                               
{'train_runtime': '4406', 'train_samples_per_second': '3.997', 'train_steps_per_second': '0.25', 'train_loss': '0.3994', 'entropy': '1.182', 'num_tokens': '2.383e+07', 'mean_token_accuracy': '0.7228', 'epoch': '3'}

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100%|████████████████████████████████████| 1101/1101 [1:13:25<00:00,  4.00s/it]

Saving model to /scratch/checkpoints/multilingual_sft_v2_ci_bs1_ga16_lr2e5_ep3

Writing model shards:   0%|                              | 0/1 [00:00<?, ?it/s]
Writing model shards: 100%|██████████████████████| 1/1 [00:10<00:00, 10.03s/it]
Writing model shards: 100%|██████████████████████| 1/1 [00:10<00:00, 10.03s/it]
  Saved /scratch/checkpoints/multilingual_sft_v2_ci_bs1_ga16_lr2e5_ep3/generation_config.json
  Embedded chat_template into tokenizer_config.json

Done! Checkpoint saved to: /scratch/checkpoints/multilingual_sft_v2_ci_bs1_ga16_lr2e5_ep3
Next steps:
  1. Test generation from /scratch/checkpoints/multilingual_sft_v2_ci_bs1_ga16_lr2e5_ep3
  2. Push to HF multilingual_model repo