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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/Qwen2-0.5B |
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tags: |
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- trl |
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- reward-trainer |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: reward-model |
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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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# reward-model |
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This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5217 |
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- Accuracy: 0.727 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| 0.636 | 0.0516 | 50 | 0.6010 | 0.688 | |
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| 0.5793 | 0.1032 | 100 | 0.5676 | 0.703 | |
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| 0.5807 | 0.1548 | 150 | 0.5732 | 0.705 | |
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| 0.5572 | 0.2064 | 200 | 0.5513 | 0.706 | |
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| 0.5695 | 0.2580 | 250 | 0.5472 | 0.718 | |
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| 0.5596 | 0.3096 | 300 | 0.5283 | 0.723 | |
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| 0.54 | 0.3612 | 350 | 0.5445 | 0.715 | |
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| 0.5291 | 0.4128 | 400 | 0.5387 | 0.722 | |
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| 0.539 | 0.4644 | 450 | 0.5461 | 0.726 | |
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| 0.5248 | 0.5160 | 500 | 0.5402 | 0.724 | |
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| 0.5263 | 0.5676 | 550 | 0.5271 | 0.726 | |
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| 0.5222 | 0.6192 | 600 | 0.5238 | 0.724 | |
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| 0.5259 | 0.6708 | 650 | 0.5200 | 0.728 | |
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| 0.5118 | 0.7224 | 700 | 0.5190 | 0.728 | |
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| 0.513 | 0.7740 | 750 | 0.5213 | 0.731 | |
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| 0.5141 | 0.8256 | 800 | 0.5253 | 0.729 | |
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| 0.5197 | 0.8772 | 850 | 0.5256 | 0.724 | |
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| 0.4968 | 0.9288 | 900 | 0.5231 | 0.726 | |
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| 0.4983 | 0.9804 | 950 | 0.5217 | 0.727 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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