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--- |
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base_model: MathGenie/Mistral-7B-Ours-SFT |
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tags: |
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- math |
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model-index: |
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- name: Mistral-7B-Ours-SFT-SCDPO |
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results: [] |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: text-generation |
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--- |
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# Mistral-7B-Ours-SFT-SCDPO |
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This model is a fine-tuned version of MathGenie/Mistral-7B-Ours-SFT. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1793 |
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- Rewards/chosen: 0.2587 |
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- Rewards/rejected: -7.0301 |
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- Rewards/accuracies: 0.8947 |
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- Rewards/margins: 7.2889 |
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- Logps/rejected: -253.7773 |
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- Logps/chosen: -80.3105 |
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- Logits/rejected: -2.3417 |
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- Logits/chosen: -2.3846 |
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## Model description |
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This is a model fine-tuned for mathematical problem-solving. |
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## Intended uses & limitations |
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The model is intended for solving math problems. |
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## Training and evaluation data |
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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: 2 |
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- eval_batch_size: 4 |
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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: 8 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| |
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| 0.3963 | 0.21 | 100 | 0.3636 | 1.8634 | -0.1518 | 0.8816 | 2.0152 | -184.9944 | -64.2644 | -2.7112 | -2.7505 | |
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| 0.2849 | 0.43 | 200 | 0.2598 | 0.7706 | -3.7221 | 0.8816 | 4.4927 | -220.6974 | -75.1921 | -2.5067 | -2.5475 | |
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| 0.2496 | 0.64 | 300 | 0.2295 | 0.9323 | -4.2717 | 0.8684 | 5.2040 | -226.1934 | -73.5753 | -2.5080 | -2.5494 | |
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| 0.2331 | 0.86 | 400 | 0.2089 | 0.7871 | -4.8912 | 0.8684 | 5.6783 | -232.3884 | -75.0269 | -2.4967 | -2.5382 | |
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| 0.0874 | 1.07 | 500 | 0.1872 | 0.6345 | -5.7444 | 0.8816 | 6.3789 | -240.9202 | -76.5527 | -2.4323 | -2.4761 | |
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| 0.1217 | 1.28 | 600 | 0.1832 | 0.2282 | -6.6907 | 0.8684 | 6.9188 | -250.3827 | -80.6161 | -2.3741 | -2.4172 | |
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| 0.0966 | 1.5 | 700 | 0.1807 | 0.1849 | -7.0125 | 0.8816 | 7.1975 | -253.6012 | -81.0485 | -2.3503 | -2.3940 | |
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| 0.0755 | 1.71 | 800 | 0.1802 | 0.3224 | -6.9539 | 0.8947 | 7.2763 | -253.0150 | -79.6739 | -2.3437 | -2.3867 | |
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| 0.1177 | 1.93 | 900 | 0.1793 | 0.2587 | -7.0301 | 0.8947 | 7.2889 | -253.7773 | -80.3105 | -2.3417 | -2.3846 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.2 |
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