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---
library_name: peft
license: llama2
base_model: lmsys/vicuna-7b-v1.5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c3cd10fd-4f32-419f-a445-d2d1cd850e9f
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>
# c3cd10fd-4f32-419f-a445-d2d1cd850e9f
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9151
## 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: 0.000204
- 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 | 0.0008 | 1 | 1.1151 |
| 1.0892 | 0.0400 | 50 | 1.0019 |
| 1.0308 | 0.0799 | 100 | 0.9805 |
| 1.0422 | 0.1199 | 150 | 0.9733 |
| 1.0099 | 0.1599 | 200 | 0.9468 |
| 1.0221 | 0.1998 | 250 | 0.9447 |
| 0.991 | 0.2398 | 300 | 0.9229 |
| 0.9879 | 0.2798 | 350 | 0.9188 |
| 0.981 | 0.3197 | 400 | 0.9123 |
| 0.9851 | 0.3597 | 450 | 0.9104 |
| 0.9026 | 0.3997 | 500 | 0.9151 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |