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--- |
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library_name: transformers |
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base_model: timarni/qwen3_dpo_100k |
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tags: |
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- generated_from_trainer |
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datasets: |
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- timarni/MNLP_STEM_IT |
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model-index: |
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- name: outputs/dpo_100k_STEM_IT |
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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: timarni/qwen3_dpo_100k |
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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_STEM_IT |
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type: alpaca |
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split: train |
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shuffle_merged_datasets: true |
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val_set_size: 0.1 |
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output_dir: ./outputs/dpo_100k_STEM_IT |
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dataset_prepared_path: last_run_prepared |
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sequence_len: 4096 #2048 |
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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) |
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eval_sample_packing: true |
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pad_to_sequence_len: true |
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# train_on_inputs: true # NEW |
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# group_by_length: false NEW? |
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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: dpo_100k_STEM_IT |
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wandb_log_model: |
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gradient_accumulation_steps: 16 # 2 |
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micro_batch_size: 2 # 1 |
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num_epochs: 3 |
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optimizer: adamw_torch |
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lr_scheduler: cosine |
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learning_rate: 0.00005 # 0.00005 |
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# cosine_min_lr_ratio: 0.1 |
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warmup_ratio: 0.05 |
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weight_decay: 0.01 |
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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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gradient_clipping: 1.0 # or max_grad_norm? |
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flash_attention: true |
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evals_per_epoch: 4 |
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saves_per_epoch: 2 |
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save_total_limit: 20 |
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special_tokens: |
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``` |
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</details><br> |
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# outputs/dpo_100k_STEM_IT |
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This model is a fine-tuned version of [timarni/qwen3_dpo_100k](https://huggingface.co/timarni/qwen3_dpo_100k) on the timarni/MNLP_STEM_IT dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1704 |
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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: 5e-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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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 32 |
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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: 12 |
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- num_epochs: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.0297 | 0.0124 | 1 | 1.0920 | |
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| 0.1602 | 0.2479 | 20 | 0.1863 | |
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| 0.1495 | 0.4957 | 40 | 0.1758 | |
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| 0.1409 | 0.7436 | 60 | 0.1709 | |
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| 0.1497 | 0.9915 | 80 | 0.1653 | |
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| 0.1118 | 1.2479 | 100 | 0.1638 | |
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| 0.1119 | 1.4957 | 120 | 0.1595 | |
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| 0.1068 | 1.7436 | 140 | 0.1590 | |
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| 0.1085 | 1.9915 | 160 | 0.1571 | |
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| 0.0833 | 2.2479 | 180 | 0.1672 | |
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| 0.0759 | 2.4957 | 200 | 0.1706 | |
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| 0.0875 | 2.7436 | 220 | 0.1705 | |
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| 0.0756 | 2.9915 | 240 | 0.1704 | |
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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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