---
library_name: peft
base_model: mtzig/prm800k_llama_debug_full
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: v3c_llama_lora
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# v3c_llama_lora

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.
It achieves the following results on the evaluation set:
- Loss: 0.4195
- Accuracy: 0.8128
- Precision: 0.7778
- Recall: 0.42
- F1: 0.5455

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 765837
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0      | 0    | 0.6173          | 0.7487   | 1.0       | 0.06   | 0.1132 |
| 0.3808        | 0.0492 | 40   | 0.5695          | 0.7487   | 0.8       | 0.08   | 0.1455 |
| 0.3036        | 0.0984 | 80   | 0.4816          | 0.7647   | 0.6364    | 0.28   | 0.3889 |
| 0.305         | 0.1476 | 120  | 0.4852          | 0.8021   | 0.7241    | 0.42   | 0.5316 |
| 0.256         | 0.1967 | 160  | 0.4328          | 0.8021   | 0.7826    | 0.36   | 0.4932 |
| 0.2062        | 0.2459 | 200  | 0.4699          | 0.7861   | 0.75      | 0.3    | 0.4286 |
| 0.2004        | 0.2951 | 240  | 0.4480          | 0.7807   | 0.7143    | 0.3    | 0.4225 |
| 0.2241        | 0.3443 | 280  | 0.4449          | 0.7807   | 0.7143    | 0.3    | 0.4225 |
| 0.1505        | 0.3935 | 320  | 0.4088          | 0.8182   | 0.75      | 0.48   | 0.5854 |
| 0.1752        | 0.4427 | 360  | 0.4386          | 0.7861   | 0.75      | 0.3    | 0.4286 |
| 0.2382        | 0.4919 | 400  | 0.4186          | 0.8128   | 0.7778    | 0.42   | 0.5455 |
| 0.238         | 0.5410 | 440  | 0.4313          | 0.7914   | 0.7391    | 0.34   | 0.4658 |
| 0.1448        | 0.5902 | 480  | 0.4161          | 0.8128   | 0.7778    | 0.42   | 0.5455 |
| 0.2096        | 0.6394 | 520  | 0.4251          | 0.7968   | 0.75      | 0.36   | 0.4865 |
| 0.204         | 0.6886 | 560  | 0.4413          | 0.7914   | 0.7391    | 0.34   | 0.4658 |
| 0.1545        | 0.7378 | 600  | 0.4312          | 0.7968   | 0.75      | 0.36   | 0.4865 |
| 0.1883        | 0.7870 | 640  | 0.4288          | 0.8021   | 0.76      | 0.38   | 0.5067 |
| 0.2403        | 0.8362 | 680  | 0.4288          | 0.8021   | 0.76      | 0.38   | 0.5067 |
| 0.1937        | 0.8853 | 720  | 0.4245          | 0.8021   | 0.76      | 0.38   | 0.5067 |
| 0.164         | 0.9345 | 760  | 0.4182          | 0.8075   | 0.7692    | 0.4    | 0.5263 |
| 0.2185        | 0.9837 | 800  | 0.4195          | 0.8128   | 0.7778    | 0.42   | 0.5455 |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3