---
license: llama3
library_name: peft
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
datasets:
- yihanwang617/ultrachat_200k_processed_indicator_0.6_4k
model-index:
- name: llama-3-qlora-ultrachat-200k-processed-indicator-0.6
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# llama-3-qlora-ultrachat-200k-processed-indicator-0.6

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the yihanwang617/ultrachat_200k_processed_indicator_0.6_4k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0235

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.008         | 0.9997 | 3247 | 1.0235          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.40.1
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1