---
library_name: peft
base_model: NousResearch/CodeLlama-7b-hf-flash
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8a92b98d-5b26-493c-9f01-be0f56fe4232
  results: []
---

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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<br>

# 8a92b98d-5b26-493c-9f01-be0f56fe4232

This model is a fine-tuned version of [NousResearch/CodeLlama-7b-hf-flash](https://huggingface.co/NousResearch/CodeLlama-7b-hf-flash) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6937

## 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: 0.000203
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0001 | 1    | 1.1523          |
| 2.1042        | 0.0036 | 50   | 0.9426          |
| 1.8717        | 0.0071 | 100  | 0.8532          |
| 1.6186        | 0.0107 | 150  | 0.8275          |
| 1.7291        | 0.0143 | 200  | 0.7944          |
| 1.6774        | 0.0179 | 250  | 0.7708          |
| 1.6611        | 0.0214 | 300  | 0.7363          |
| 1.5363        | 0.0250 | 350  | 0.7127          |
| 1.5478        | 0.0286 | 400  | 0.7001          |
| 1.555         | 0.0322 | 450  | 0.6947          |
| 1.6401        | 0.0357 | 500  | 0.6937          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1