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--- |
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library_name: transformers |
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license: mit |
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base_model: CMU-AIRe/e3-1.7B |
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tags: |
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- llama-factory |
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- full |
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- generated_from_trainer |
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model-index: |
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- name: e3-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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# e3-sft |
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This model is a fine-tuned version of [CMU-AIRe/e3-1.7B](https://huggingface.co/CMU-AIRe/e3-1.7B) on the hardmath_sft_2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6364 |
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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-07 |
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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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- gradient_accumulation_steps: 32 |
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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_with_min_lr |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 100.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.7025 | 4.0 | 16 | 0.7606 | |
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| 0.9105 | 8.0 | 32 | 0.7590 | |
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| 0.8193 | 12.0 | 48 | 0.7550 | |
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| 0.6939 | 16.0 | 64 | 0.7460 | |
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| 0.6623 | 20.0 | 80 | 0.7418 | |
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| 0.8112 | 24.0 | 96 | 0.7389 | |
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| 0.708 | 28.0 | 112 | 0.7154 | |
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| 0.6471 | 32.0 | 128 | 0.7097 | |
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| 0.9019 | 36.0 | 144 | 0.7050 | |
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| 0.7328 | 40.0 | 160 | 0.7007 | |
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| 0.8191 | 44.0 | 176 | 0.6938 | |
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| 0.6327 | 48.0 | 192 | 0.6752 | |
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| 0.6903 | 52.0 | 208 | 0.6604 | |
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| 0.7467 | 56.0 | 224 | 0.6533 | |
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| 0.7364 | 60.0 | 240 | 0.6489 | |
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| 0.7706 | 64.0 | 256 | 0.6460 | |
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| 0.7777 | 68.0 | 272 | 0.6441 | |
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| 0.6391 | 72.0 | 288 | 0.6419 | |
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| 0.648 | 76.0 | 304 | 0.6408 | |
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| 0.704 | 80.0 | 320 | 0.6398 | |
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| 0.6316 | 84.0 | 336 | 0.6387 | |
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| 0.6232 | 88.0 | 352 | 0.6380 | |
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| 0.6545 | 92.0 | 368 | 0.6372 | |
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| 0.7126 | 96.0 | 384 | 0.6364 | |
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| 0.6465 | 100.0 | 400 | 0.6364 | |
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### Framework versions |
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- Transformers 4.55.0 |
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- Pytorch 2.5.1 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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