---
license: other
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: both
  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. -->

# both

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the chess_explain_both_00, the chess_explain_both_01, the chess_explain_both_02, the chess_explain_both_03, the chess_explain_both_04, the chess_explain_both_05, the chess_explain_both_06, the chess_explain_both_07, the chess_explain_both_08, the chess_explain_both_09, the chess_explain_both_10, the chess_explain_both_11, the chess_explain_both_12, the chess_explain_both_13 and the chess_explain_both_14 datasets.
It achieves the following results on the evaluation set:
- Loss: 0.0369

## 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: 5e-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0144        | 1.6260 | 1000 | 0.0148          |
| 0.0068        | 3.2520 | 2000 | 0.0183          |
| 0.003         | 4.8780 | 3000 | 0.0207          |
| 0.0001        | 6.5041 | 4000 | 0.0359          |


### Framework versions

- Transformers 4.43.4
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1