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zhangjie6/Llama-3-8B-Instruct-qlora-adapter
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---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-3-8B-Instruct-qlora-adapter
results: []
---
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/lengxingxin/Llama-3-8B-Instruct-2/runs/uud2yqsc)
# Llama-3-8B-Instruct-qlora-adapter
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1380
## 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: 0.0001
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8846 | 0.2395 | 10 | 0.5030 |
| 0.3505 | 0.4790 | 20 | 0.1996 |
| 0.1636 | 0.7186 | 30 | 0.1449 |
| 0.1429 | 0.9581 | 40 | 0.1380 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1