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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: v3d_mistral_lora |
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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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# v3d_mistral_lora |
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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: 0.3848 |
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- Accuracy: 0.8329 |
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- Precision: 0.7398 |
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- Recall: 0.4527 |
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- F1: 0.5617 |
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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 | 0.6112 | 0.7506 | 0.3659 | 0.0746 | 0.1240 | |
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| 0.5265 | 0.0532 | 20 | 0.6081 | 0.7647 | 0.5455 | 0.0299 | 0.0566 | |
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| 0.4127 | 0.1064 | 40 | 0.5412 | 0.7682 | 0.7 | 0.0348 | 0.0664 | |
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| 0.3347 | 0.1596 | 60 | 0.5020 | 0.7741 | 0.7368 | 0.0697 | 0.1273 | |
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| 0.3077 | 0.2128 | 80 | 0.4463 | 0.7965 | 0.6944 | 0.2488 | 0.3663 | |
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| 0.307 | 0.2660 | 100 | 0.4498 | 0.8 | 0.6782 | 0.2935 | 0.4097 | |
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| 0.2413 | 0.3191 | 120 | 0.4316 | 0.8141 | 0.7087 | 0.3632 | 0.4803 | |
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| 0.326 | 0.3723 | 140 | 0.4107 | 0.8235 | 0.7257 | 0.4080 | 0.5223 | |
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| 0.2404 | 0.4255 | 160 | 0.4615 | 0.8094 | 0.7671 | 0.2786 | 0.4088 | |
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| 0.2962 | 0.4787 | 180 | 0.4205 | 0.8282 | 0.7619 | 0.3980 | 0.5229 | |
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| 0.2727 | 0.5319 | 200 | 0.4830 | 0.8 | 0.7627 | 0.2239 | 0.3462 | |
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| 0.2844 | 0.5851 | 220 | 0.4187 | 0.8259 | 0.7524 | 0.3930 | 0.5163 | |
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| 0.2632 | 0.6383 | 240 | 0.4037 | 0.8235 | 0.7339 | 0.3980 | 0.5161 | |
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| 0.2499 | 0.6915 | 260 | 0.3885 | 0.8247 | 0.7241 | 0.4179 | 0.5300 | |
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| 0.2121 | 0.7447 | 280 | 0.3953 | 0.8224 | 0.7232 | 0.4030 | 0.5176 | |
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| 0.2704 | 0.7979 | 300 | 0.3849 | 0.8329 | 0.736 | 0.4577 | 0.5644 | |
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| 0.2333 | 0.8511 | 320 | 0.3878 | 0.8318 | 0.7417 | 0.4428 | 0.5545 | |
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| 0.2896 | 0.9043 | 340 | 0.3886 | 0.8306 | 0.7395 | 0.4378 | 0.55 | |
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| 0.2398 | 0.9574 | 360 | 0.3848 | 0.8329 | 0.7398 | 0.4527 | 0.5617 | |
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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 |
