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--- |
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library_name: peft |
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base_model: peiyi9979/math-shepherd-mistral-7b-prm |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: v1_5_mistral_lora32 |
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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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# v1_5_mistral_lora32 |
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This model is a fine-tuned version of [peiyi9979/math-shepherd-mistral-7b-prm](https://huggingface.co/peiyi9979/math-shepherd-mistral-7b-prm) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3153 |
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- Accuracy: 0.8614 |
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- Precision: 0.7273 |
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- Recall: 0.7547 |
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- F1: 0.7407 |
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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: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 765837 |
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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: 64 |
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- total_eval_batch_size: 32 |
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- optimizer: Use OptimizerNames.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_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 0 | 0 | 0.5958 | 0.7376 | 0.5 | 0.0660 | 0.1167 | |
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| 0.6819 | 0.0575 | 20 | 0.5694 | 0.7450 | 0.6154 | 0.0755 | 0.1345 | |
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| 0.4789 | 0.1149 | 40 | 0.4776 | 0.7550 | 0.5263 | 0.6604 | 0.5858 | |
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| 0.4108 | 0.1724 | 60 | 0.4043 | 0.8045 | 0.6286 | 0.6226 | 0.6256 | |
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| 0.3486 | 0.2299 | 80 | 0.3752 | 0.8366 | 0.6961 | 0.6698 | 0.6827 | |
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| 0.2936 | 0.2874 | 100 | 0.3650 | 0.8465 | 0.7115 | 0.6981 | 0.7048 | |
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| 0.3788 | 0.3448 | 120 | 0.3579 | 0.8465 | 0.6864 | 0.7642 | 0.7232 | |
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| 0.3918 | 0.4023 | 140 | 0.3483 | 0.8564 | 0.7449 | 0.6887 | 0.7157 | |
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| 0.3386 | 0.4598 | 160 | 0.3409 | 0.8614 | 0.7193 | 0.7736 | 0.7455 | |
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| 0.2864 | 0.5172 | 180 | 0.3257 | 0.8663 | 0.75 | 0.7358 | 0.7429 | |
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| 0.2581 | 0.5747 | 200 | 0.3223 | 0.8663 | 0.7407 | 0.7547 | 0.7477 | |
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| 0.3373 | 0.6322 | 220 | 0.3174 | 0.8663 | 0.75 | 0.7358 | 0.7429 | |
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| 0.3006 | 0.6897 | 240 | 0.3172 | 0.8564 | 0.7222 | 0.7358 | 0.7290 | |
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| 0.3157 | 0.7471 | 260 | 0.3143 | 0.8639 | 0.7339 | 0.7547 | 0.7442 | |
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| 0.291 | 0.8046 | 280 | 0.3137 | 0.8639 | 0.7339 | 0.7547 | 0.7442 | |
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| 0.2578 | 0.8621 | 300 | 0.3168 | 0.8639 | 0.7339 | 0.7547 | 0.7442 | |
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| 0.3223 | 0.9195 | 320 | 0.3157 | 0.8614 | 0.7273 | 0.7547 | 0.7407 | |
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| 0.3448 | 0.9770 | 340 | 0.3153 | 0.8614 | 0.7273 | 0.7547 | 0.7407 | |
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### Framework versions |
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- PEFT 0.13.2 |
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- Transformers 4.46.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
