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---
library_name: peft
license: gpl
base_model: NousResearch/GPT4-x-Vicuna-13b-fp16
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 01efdd42-cc93-4498-8836-5416e798b1c6
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. -->
[<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>
# 01efdd42-cc93-4498-8836-5416e798b1c6
This model is a fine-tuned version of [NousResearch/GPT4-x-Vicuna-13b-fp16](https://huggingface.co/NousResearch/GPT4-x-Vicuna-13b-fp16) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2513
## 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: 0.000208
- train_batch_size: 4
- eval_batch_size: 4
- seed: 80
- 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 | 0.0008 | 1 | 2.9869 |
| 2.6371 | 0.0419 | 50 | 2.4640 |
| 2.5372 | 0.0839 | 100 | 2.5468 |
| 2.5542 | 0.1258 | 150 | 2.4662 |
| 2.5761 | 0.1677 | 200 | 2.4173 |
| 2.6806 | 0.2096 | 250 | 2.3430 |
| 2.5811 | 0.2516 | 300 | 2.3232 |
| 2.3769 | 0.2935 | 350 | 2.2691 |
| 2.5571 | 0.3354 | 400 | 2.2556 |
| 2.629 | 0.3774 | 450 | 2.2486 |
| 2.5459 | 0.4193 | 500 | 2.2513 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |