---
library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: llama_8b_lima_44
  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. -->

# llama_8b_lima_44

This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the open_webui_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9029

## 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: 5.5e-06
- train_batch_size: 3
- eval_batch_size: 2
- seed: 66
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 6
- total_train_batch_size: 36
- total_eval_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: polynomial
- lr_scheduler_warmup_steps: 50
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0313        | 0.0974 | 80   | 0.9290          |
| 0.985         | 0.1948 | 160  | 0.9194          |
| 1.091         | 0.2923 | 240  | 0.9109          |
| 0.9087        | 0.3897 | 320  | 0.9030          |
| 1.0107        | 0.4871 | 400  | 0.8975          |
| 0.9108        | 0.5845 | 480  | 0.8940          |
| 1.1531        | 0.6820 | 560  | 0.9039          |
| 1.2698        | 0.7794 | 640  | 0.9069          |
| 1.1988        | 0.8768 | 720  | 0.9063          |
| 1.1649        | 0.9742 | 800  | 0.9046          |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3