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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/reasoning_SFT |
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model-index: |
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- name: outputs/qwen3_reasoning_sft |
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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/reasoning_SFT |
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type: chat_template |
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split: train |
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field_messages: conversations |
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# message_property_mappings: |
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# role: from |
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# content: value |
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val_set_size: 0.1 |
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output_dir: ./outputs/qwen3_reasoning_sft |
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dataset_prepared_path: last_run_prepared |
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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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sequence_len: 4096 #2048 |
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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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wandb_project: mnlp_project |
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wandb_entity: tim-arni |
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wandb_watch: |
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wandb_name: qwen3_reasoning_sft |
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wandb_log_model: |
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gradient_accumulation_steps: 2 # 16 following https://unsloth.ai/blog/qwen3 |
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micro_batch_size: 1 # 2 |
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num_epochs: 6 |
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optimizer: adamw_torch |
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lr_scheduler: cosine |
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learning_rate: 0.00005 # 0.0002 |
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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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logging_steps: 1 |
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gradient_clipping: 1.0 |
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flash_attention: true |
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warmup_ratio: 0.03 |
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evals_per_epoch: 4 |
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saves_per_epoch: 2 |
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save_total_limit: 25 |
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weight_decay: 1e-4 |
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special_tokens: |
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``` |
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</details><br> |
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# outputs/qwen3_reasoning_sft |
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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/reasoning_SFT dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8020 |
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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: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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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: 47 |
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- num_epochs: 6.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.965 | 0.0037 | 1 | 0.8999 | |
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| 0.8101 | 0.2505 | 67 | 0.7453 | |
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| 0.6077 | 0.5009 | 134 | 0.7342 | |
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| 0.5874 | 0.7514 | 201 | 0.7270 | |
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| 0.4362 | 1.0 | 268 | 0.7260 | |
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| 0.6779 | 1.2505 | 335 | 0.7269 | |
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| 0.505 | 1.5009 | 402 | 0.7310 | |
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| 0.4969 | 1.7514 | 469 | 0.7274 | |
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| 0.309 | 2.0 | 536 | 0.7332 | |
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| 0.5954 | 2.2505 | 603 | 0.7428 | |
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| 0.4302 | 2.5009 | 670 | 0.7514 | |
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| 0.4301 | 2.7514 | 737 | 0.7491 | |
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| 0.23 | 3.0 | 804 | 0.7559 | |
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| 0.5296 | 3.2505 | 871 | 0.7683 | |
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| 0.3761 | 3.5009 | 938 | 0.7857 | |
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| 0.3916 | 3.7514 | 1005 | 0.7818 | |
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| 0.1842 | 4.0 | 1072 | 0.7863 | |
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| 0.4926 | 4.2505 | 1139 | 0.7980 | |
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| 0.3469 | 4.5009 | 1206 | 0.8004 | |
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| 0.3697 | 4.7514 | 1273 | 0.7908 | |
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| 0.1665 | 5.0 | 1340 | 0.7925 | |
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| 0.4773 | 5.2505 | 1407 | 0.8187 | |
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| 0.3364 | 5.5009 | 1474 | 0.8071 | |
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| 0.3622 | 5.7514 | 1541 | 0.8020 | |
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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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