---
library_name: peft
license: mit
base_model: NousResearch/Nous-Capybara-7B-V1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9017ccaa-678a-4990-bb34-2298e3f66057
  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>

# 9017ccaa-678a-4990-bb34-2298e3f66057

This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6304

## 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: 0.000217
- 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        | 0.0000 | 1    | 1.1474          |
| 0.7831        | 0.0021 | 50   | 0.7893          |
| 0.7393        | 0.0042 | 100  | 0.7602          |
| 0.7043        | 0.0063 | 150  | 0.7188          |
| 0.6935        | 0.0084 | 200  | 0.7025          |
| 0.6702        | 0.0105 | 250  | 0.6787          |
| 0.6508        | 0.0126 | 300  | 0.6603          |
| 0.6159        | 0.0148 | 350  | 0.6443          |
| 0.6354        | 0.0169 | 400  | 0.6358          |
| 0.6225        | 0.0190 | 450  | 0.6312          |
| 0.6781        | 0.0211 | 500  | 0.6304          |


### Framework versions

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