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README.md
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---
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library_name: peft
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base_model: mtzig/prm800k_llama_debug_full
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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: v3c_llama_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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# v3c_llama_lora
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This model is a fine-tuned version of [mtzig/prm800k_llama_debug_full](https://huggingface.co/mtzig/prm800k_llama_debug_full) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4195
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- Accuracy: 0.8128
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- Precision: 0.7778
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- Recall: 0.42
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- F1: 0.5455
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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: 4
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- eval_batch_size: 4
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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: 4
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- total_train_batch_size: 64
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- total_eval_batch_size: 16
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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 | 0.6173 | 0.7487 | 1.0 | 0.06 | 0.1132 |
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| 0.3808 | 0.0492 | 40 | 0.5695 | 0.7487 | 0.8 | 0.08 | 0.1455 |
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| 0.3036 | 0.0984 | 80 | 0.4816 | 0.7647 | 0.6364 | 0.28 | 0.3889 |
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| 0.305 | 0.1476 | 120 | 0.4852 | 0.8021 | 0.7241 | 0.42 | 0.5316 |
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| 0.256 | 0.1967 | 160 | 0.4328 | 0.8021 | 0.7826 | 0.36 | 0.4932 |
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| 0.2062 | 0.2459 | 200 | 0.4699 | 0.7861 | 0.75 | 0.3 | 0.4286 |
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| 0.2004 | 0.2951 | 240 | 0.4480 | 0.7807 | 0.7143 | 0.3 | 0.4225 |
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| 0.2241 | 0.3443 | 280 | 0.4449 | 0.7807 | 0.7143 | 0.3 | 0.4225 |
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| 0.1505 | 0.3935 | 320 | 0.4088 | 0.8182 | 0.75 | 0.48 | 0.5854 |
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| 0.1752 | 0.4427 | 360 | 0.4386 | 0.7861 | 0.75 | 0.3 | 0.4286 |
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| 0.2382 | 0.4919 | 400 | 0.4186 | 0.8128 | 0.7778 | 0.42 | 0.5455 |
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| 0.238 | 0.5410 | 440 | 0.4313 | 0.7914 | 0.7391 | 0.34 | 0.4658 |
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| 0.1448 | 0.5902 | 480 | 0.4161 | 0.8128 | 0.7778 | 0.42 | 0.5455 |
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| 0.2096 | 0.6394 | 520 | 0.4251 | 0.7968 | 0.75 | 0.36 | 0.4865 |
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| 0.204 | 0.6886 | 560 | 0.4413 | 0.7914 | 0.7391 | 0.34 | 0.4658 |
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| 0.1545 | 0.7378 | 600 | 0.4312 | 0.7968 | 0.75 | 0.36 | 0.4865 |
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| 0.1883 | 0.7870 | 640 | 0.4288 | 0.8021 | 0.76 | 0.38 | 0.5067 |
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| 0.2403 | 0.8362 | 680 | 0.4288 | 0.8021 | 0.76 | 0.38 | 0.5067 |
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| 0.1937 | 0.8853 | 720 | 0.4245 | 0.8021 | 0.76 | 0.38 | 0.5067 |
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| 0.164 | 0.9345 | 760 | 0.4182 | 0.8075 | 0.7692 | 0.4 | 0.5263 |
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| 0.2185 | 0.9837 | 800 | 0.4195 | 0.8128 | 0.7778 | 0.42 | 0.5455 |
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### Framework versions
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- PEFT 0.13.2
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- Transformers 4.46.3
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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
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