| | --- |
| | library_name: transformers |
| | base_model: timarni/qwen3_dpo |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - timarni/MNLP_M3_mcqa_dataset |
| | model-index: |
| | - name: outputs/dpo_it_bal |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | [<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) |
| | <details><summary>See axolotl config</summary> |
| |
|
| | axolotl version: `0.9.2` |
| | ```yaml |
| | base_model: timarni/qwen3_dpo |
| | # Automatically upload checkpoint and final model to HF |
| | # hub_model_id: username/custom_model_name |
| | |
| | plugins: |
| | - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin |
| | strict: false |
| | |
| | chat_template: qwen3 |
| | datasets: |
| | - path: timarni/MNLP_M3_mcqa_dataset # timarni/MNLP_intstruction_tuning |
| | name: stem_instruction_tuning_balanced_mini |
| | type: alpaca |
| | split: train |
| | |
| | shuffle_merged_datasets: true |
| | |
| | val_set_size: 0.1 |
| | output_dir: ./outputs/dpo_it_bal |
| | dataset_prepared_path: last_run_prepared |
| | |
| | sequence_len: 2048 #2048 |
| | sample_packing: true # was true -> need to check if it actually learns on the samples or not (better understand te hyperparam and event. install axolotl to debug) |
| | eval_sample_packing: true |
| | pad_to_sequence_len: true |
| | train_on_inputs: false # NEW |
| | # group_by_length: false NEW? |
| | |
| | # To be sure that no LORA is done |
| | adapter: null |
| | lora: false |
| | merge_lora: false |
| | |
| | wandb_project: mnlp_project |
| | wandb_entity: tim-arni |
| | wandb_watch: |
| | wandb_name: wiki_it_bal |
| | wandb_log_model: |
| | |
| | gradient_accumulation_steps: 2 |
| | micro_batch_size: 1 # 2 |
| | num_epochs: 6 |
| | optimizer: adamw_torch |
| | lr_scheduler: cosine |
| | learning_rate: 5e-6 # 0.00005 |
| | cosine_min_lr_ratio: 0.1 |
| | |
| | warmup_ratio: 0.05 |
| | weight_decay: 0.01 |
| | |
| | bf16: auto |
| | tf32: true |
| | |
| | gradient_checkpointing: offload |
| | gradient_checkpointing_kwargs: |
| | use_reentrant: false |
| | resume_from_checkpoint: |
| | logging_steps: 1 |
| | gradient_clipping: 1.0 # or max_grad_norm? |
| | flash_attention: true |
| | |
| | evals_per_epoch: 4 |
| | saves_per_epoch: 2 |
| | save_total_limit: 25 |
| | special_tokens: |
| | |
| | ``` |
| |
|
| | </details><br> |
| |
|
| | # outputs/dpo_it_bal |
| |
|
| | This model is a fine-tuned version of [timarni/qwen3_dpo](https://huggingface.co/timarni/qwen3_dpo) on the timarni/MNLP_M3_mcqa_dataset dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1734 |
| | |
| | ## Model description |
| | |
| | More information needed |
| | |
| | ## Intended uses & limitations |
| | |
| | More information needed |
| | |
| | ## Training and evaluation data |
| | |
| | More information needed |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 5e-06 |
| | - train_batch_size: 1 |
| | - eval_batch_size: 1 |
| | - seed: 42 |
| | - distributed_type: multi-GPU |
| | - num_devices: 4 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 8 |
| | - total_eval_batch_size: 4 |
| | - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_steps: 24 |
| | - num_epochs: 6.0 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:------:|:----:|:---------------:| |
| | | 2.0143 | 0.0122 | 1 | 1.8038 | |
| | | 0.988 | 0.2561 | 21 | 0.9164 | |
| | | 0.2107 | 0.5122 | 42 | 0.1900 | |
| | | 0.1751 | 0.7683 | 63 | 0.1814 | |
| | | 0.1691 | 1.0244 | 84 | 0.1785 | |
| | | 0.1521 | 1.2805 | 105 | 0.1759 | |
| | | 0.1458 | 1.5366 | 126 | 0.1759 | |
| | | 0.1822 | 1.7927 | 147 | 0.1749 | |
| | | 0.153 | 2.0488 | 168 | 0.1736 | |
| | | 0.1603 | 2.3049 | 189 | 0.1739 | |
| | | 0.1474 | 2.5610 | 210 | 0.1751 | |
| | | 0.2087 | 2.8171 | 231 | 0.1738 | |
| | | 0.1599 | 3.0732 | 252 | 0.1732 | |
| | | 0.1411 | 3.3293 | 273 | 0.1734 | |
| | | 0.2014 | 3.5854 | 294 | 0.1744 | |
| | | 0.1507 | 3.8415 | 315 | 0.1735 | |
| | | 0.1684 | 4.0976 | 336 | 0.1735 | |
| | | 0.1547 | 4.3537 | 357 | 0.1731 | |
| | | 0.1469 | 4.6098 | 378 | 0.1738 | |
| | | 0.155 | 4.8659 | 399 | 0.1736 | |
| | | 0.162 | 5.1220 | 420 | 0.1735 | |
| | | 0.1274 | 5.3780 | 441 | 0.1732 | |
| | | 0.1397 | 5.6341 | 462 | 0.1736 | |
| | | 0.1333 | 5.8902 | 483 | 0.1734 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.51.3 |
| | - Pytorch 2.5.1+cu121 |
| | - Datasets 3.5.1 |
| | - Tokenizers 0.21.1 |
| |
|
