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

# no_explain

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_noexplain_00, the chess_explain_noexplain_01, the chess_explain_noexplain_02, the chess_explain_noexplain_03, the chess_explain_noexplain_04, the chess_explain_noexplain_05, the chess_explain_noexplain_06, the chess_explain_noexplain_07, the chess_explain_noexplain_08, the chess_explain_noexplain_09, the chess_explain_noexplain_10, the chess_explain_noexplain_11, the chess_explain_noexplain_12, the chess_explain_noexplain_13 and the chess_explain_noexplain_14 datasets.
It achieves the following results on the evaluation set:
- Loss: 0.0932

## 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: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 1024
- total_eval_batch_size: 512
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.0429        | 0.8010 | 1000  | 0.0422          |
| 0.0329        | 1.6015 | 2000  | 0.0336          |
| 0.0275        | 2.4021 | 3000  | 0.0297          |
| 0.0202        | 3.2026 | 4000  | 0.0292          |
| 0.0194        | 4.0032 | 5000  | 0.0294          |
| 0.0119        | 4.8042 | 6000  | 0.0311          |
| 0.0048        | 5.6047 | 7000  | 0.0439          |
| 0.0013        | 6.4053 | 8000  | 0.0538          |
| 0.0004        | 7.2058 | 9000  | 0.0670          |
| 0.0003        | 8.0064 | 10000 | 0.0698          |
| 0.0           | 8.8074 | 11000 | 0.0894          |
| 0.0           | 9.6079 | 12000 | 0.0931          |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 2.21.0
- Tokenizers 0.21.0