| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: Qwen/Qwen3-0.6B-Base |
| tags: |
| - generated_from_trainer |
| datasets: |
| - timarni/MNLP_M2_mcqa_dataset |
| model-index: |
| - name: outputs/base_test_set |
| 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: Qwen/Qwen3-0.6B-Base |
| # 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_M2_mcqa_dataset |
| type: alpaca |
| split: train |
| |
| shuffle_merged_datasets: true |
| |
| val_set_size: 0.1 |
| output_dir: ./outputs/base_test_set |
| dataset_prepared_path: last_run_prepared |
| |
| sequence_len: 4096 #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: false |
| pad_to_sequence_len: true |
| # train_on_inputs: true # 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: base_test_set |
| wandb_log_model: |
| |
| gradient_accumulation_steps: 16 # 2 |
| micro_batch_size: 2 # 1 |
| num_epochs: 3 |
| optimizer: adamw_torch |
| lr_scheduler: cosine |
| learning_rate: 0.00005 # 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: 20 |
| special_tokens: |
| |
| ``` |
|
|
| </details><br> |
|
|
| # outputs/base_test_set |
|
|
| 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_M2_mcqa_dataset dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1689 |
| |
| ## 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-05 |
| - train_batch_size: 2 |
| - eval_batch_size: 2 |
| - seed: 42 |
| - gradient_accumulation_steps: 16 |
| - total_train_batch_size: 32 |
| - 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 |
| - num_epochs: 3.0 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:----:|:---------------:| |
| | 0.4926 | 0.6957 | 1 | 0.2934 | |
| | 0.197 | 1.0 | 2 | 0.1959 | |
| | 0.127 | 1.6957 | 3 | 0.1689 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.51.3 |
| - Pytorch 2.5.1+cu121 |
| - Datasets 3.5.1 |
| - Tokenizers 0.21.1 |
|
|
