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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: Qwen/Qwen3-0.6B-Base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- timarni/MNLP_M3_mcqa_dataset |
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model-index: |
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- name: outputs/base_it_hard |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.9.2` |
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```yaml |
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base_model: Qwen/Qwen3-0.6B-Base |
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# Automatically upload checkpoint and final model to HF |
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# hub_model_id: username/custom_model_name |
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plugins: |
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin |
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strict: false |
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chat_template: qwen3 |
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datasets: |
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- path: timarni/MNLP_M3_mcqa_dataset |
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name: stem_instruction_tuning_hard |
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type: alpaca |
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split: train |
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val_set_size: 0.1 |
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output_dir: ./outputs/base_it_hard |
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dataset_prepared_path: last_run_prepared |
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sequence_len: 2048 # 4096 |
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sample_packing: true |
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eval_sample_packing: true |
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pad_to_sequence_len: true |
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# To be sure that no LORA is done |
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adapter: null |
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lora: false |
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merge_lora: false |
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wandb_project: mnlp_project |
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wandb_entity: tim-arni |
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wandb_watch: |
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wandb_name: base_it_hard |
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wandb_log_model: |
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gradient_accumulation_steps: 4 # 2 |
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micro_batch_size: 2 # 1 |
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num_epochs: 5 |
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optimizer: adamw_torch |
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lr_scheduler: cosine |
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learning_rate: 0.00001 # 0.00005 |
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cosine_min_lr_ratio: 0.1 |
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bf16: auto |
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tf32: true |
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gradient_checkpointing: offload |
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gradient_checkpointing_kwargs: |
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use_reentrant: false |
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resume_from_checkpoint: |
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logging_steps: 1 |
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flash_attention: true |
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warmup_ratio: 0.05 |
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evals_per_epoch: 4 |
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saves_per_epoch: 2 |
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save_total_limit: 10 |
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weight_decay: 0.01 |
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special_tokens: |
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``` |
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</details><br> |
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# outputs/base_it_hard |
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This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on the timarni/MNLP_M3_mcqa_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.5354 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 4 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 45 |
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- num_epochs: 5.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.8271 | 0.0055 | 1 | 6.2702 | |
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| 0.1398 | 0.2490 | 45 | 4.7948 | |
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| 0.1439 | 0.4979 | 90 | 4.3628 | |
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| 0.1377 | 0.7469 | 135 | 4.2137 | |
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| 0.1436 | 0.9959 | 180 | 4.2396 | |
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| 0.1086 | 1.2434 | 225 | 4.2662 | |
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| 0.1018 | 1.4924 | 270 | 4.3334 | |
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| 0.1226 | 1.7414 | 315 | 4.3240 | |
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| 0.13 | 1.9903 | 360 | 4.3957 | |
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| 0.1269 | 2.2379 | 405 | 4.3869 | |
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| 0.11 | 2.4869 | 450 | 4.4244 | |
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| 0.1081 | 2.7358 | 495 | 4.4782 | |
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| 0.1139 | 2.9848 | 540 | 4.5098 | |
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| 0.1041 | 3.2324 | 585 | 4.4869 | |
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| 0.1052 | 3.4813 | 630 | 4.5032 | |
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| 0.1143 | 3.7303 | 675 | 4.5032 | |
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| 0.1144 | 3.9793 | 720 | 4.5265 | |
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| 0.104 | 4.2268 | 765 | 4.5161 | |
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| 0.1343 | 4.4758 | 810 | 4.5280 | |
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| 0.1217 | 4.7248 | 855 | 4.5158 | |
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| 0.1158 | 4.9737 | 900 | 4.5354 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.5.1 |
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- Tokenizers 0.21.1 |
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