---
library_name: peft
license: apache-2.0
base_model: NousResearch/Genstruct-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 79b4e4b6-3b72-4dbe-8757-62a1b9aa6a20
  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>

# 79b4e4b6-3b72-4dbe-8757-62a1b9aa6a20

This model is a fine-tuned version of [NousResearch/Genstruct-7B](https://huggingface.co/NousResearch/Genstruct-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2338

## 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: 0.000207
- 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        | 0.0014 | 1    | 3.1059          |
| 3.3681        | 0.0690 | 50   | 2.3035          |
| 3.7154        | 0.1380 | 100  | 2.3163          |
| 3.4886        | 0.2070 | 150  | 1.9904          |
| 2.9133        | 0.2761 | 200  | 1.8560          |
| 4.7279        | 0.3451 | 250  | 1.8166          |
| 2.9482        | 0.4141 | 300  | 1.5225          |
| 2.815         | 0.4831 | 350  | 1.4472          |
| 2.5287        | 0.5521 | 400  | 1.2898          |
| 2.4048        | 0.6211 | 450  | 1.2415          |
| 2.4292        | 0.6901 | 500  | 1.2338          |


### Framework versions

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