---
library_name: peft
license: other
base_model: mistralai/Mistral-7B-Instruct-v0.3
tags:
- llama-factory
- lora
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: factory_mistral_results
  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. -->

# factory_mistral_results

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2260
- Accuracy: 0.9587

## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Use 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: 0.03
- num_epochs: 9.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3179        | 1.0   | 32   | 0.3321          | 0.9217   |
| 0.206         | 2.0   | 64   | 0.2425          | 0.9408   |
| 0.1447        | 3.0   | 96   | 0.2109          | 0.9489   |
| 0.1067        | 4.0   | 128  | 0.2062          | 0.9527   |
| 0.0612        | 5.0   | 160  | 0.2128          | 0.9539   |
| 0.0491        | 6.0   | 192  | 0.2169          | 0.9549   |
| 0.0378        | 7.0   | 224  | 0.2166          | 0.9584   |
| 0.0294        | 8.0   | 256  | 0.2224          | 0.9588   |
| 0.0215        | 9.0   | 288  | 0.2260          | 0.9587   |


### Framework versions

- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1