---
library_name: transformers
tags: []
---



---

# Fine-Tuning LLaMA-2-7b with QLoRA on Custom Dataset

This repository provides a setup and script for fine-tuning the LLaMA-2-7b model using QLoRA (Quantized Low-Rank Adaptation) with custom datasets. The script is designed for efficiency and flexibility in training large language models (LLMs) by leveraging advanced techniques such as 4-bit quantization and LoRA.

## Overview

The script fine-tunes a pre-trained LLaMA-2-7b model using a custom dataset, applying QLoRA techniques to optimize performance. It utilizes the `transformers`, `datasets`, `peft`, and `trl` libraries for model management, data processing, and training. The setup includes support for mixed precision training, gradient checkpointing, and advanced quantization techniques to enhance the efficiency of the fine-tuning process.

## Components

### 1. Dependencies

Ensure the following libraries are installed:
- `torch`
- `datasets`
- `transformers`
- `peft`
- `trl`

Install them using pip if they are not already available:
```bash
pip install torch datasets transformers peft trl
```

### 2. Model and Dataset

- **Model**: The base model used is `LLaMA-2-7b`. The script loads this model from a specified local directory.
- **Dataset**: The training data is loaded from a specified directory. The dataset should be formatted in a way that the `"text"` field contains the training examples.

### 3. QLoRA Configuration

QLoRA parameters are used to configure the quantization and adaptation process:
- **LoRA Attention Dimension (`lora_r`)**: 64
- **LoRA Alpha Parameter (`lora_alpha`)**: 16
- **LoRA Dropout Probability (`lora_dropout`)**: 0.1

### 4. BitsAndBytes Configuration

Quantization settings for the model:
- **Use 4-bit Precision**: True
- **Compute Data Type**: `float16`
- **Quantization Type**: `nf4`
- **Nested Quantization**: False

### 5. Training Configuration

Training parameters are defined as follows:
- **Output Directory**: `./results`
- **Number of Epochs**: 300
- **Batch Size**: 4
- **Gradient Accumulation Steps**: 1
- **Learning Rate**: 2e-4
- **Weight Decay**: 0.001
- **Optimizer**: `paged_adamw_32bit`
- **Learning Rate Scheduler**: `cosine`
- **Gradient Clipping**: 0.3
- **Warmup Ratio**: 0.03
- **Logging Steps**: 25
- **Save Steps**: 0

### 6. Training and Evaluation

The script includes preprocessing of the dataset, model initialization with QLoRA, and training using `SFTTrainer` from the `trl` library. It supports mixed precision training and gradient checkpointing to enhance training efficiency.

### 7. Usage Instructions

1. **Update File Paths**: Adjust `model_name`, `dataset_name`, and `new_model` paths according to your environment.
2. **Run the Script**: Execute the script in your Python environment to start the fine-tuning process.

```bash
python fine_tune_llama.py
```

3. **Monitor Training**: Use TensorBoard or similar tools to monitor the training progress.

### 8. Model Saving

After training, the model is saved to the specified directory (`new_model`). This trained model can be loaded for further evaluation or deployment.

## Example Configuration

Here’s an example configuration used for fine-tuning:

```python
model_name = "/data/bio-eng-llm/llm_repo/NousResearch/Llama-2-7b-chat-hf"
dataset_name = "/data/bio-eng-llm/llm_repo/mlabonne/guanaco-llama2-1k"
new_model = "/data/bio-eng-llm/llm_repo/mlabonne/llama-2-7b-miniguanaco"

lora_r = 64
lora_alpha = 16
lora_dropout = 0.1

use_4bit = True
bnb_4bit_compute_dtype = "float16"
bnb_4bit_quant_type = "nf4"
use_nested_quant = False

output_dir = "./results"
num_train_epochs = 300
fp16 = False
bf16 = False
per_device_train_batch_size = 4
gradient_accumulation_steps = 1
gradient_checkpointing = True
max_grad_norm = 0.3
learning_rate = 2e-4
weight_decay = 0.001
optim = "paged_adamw_32bit"
lr_scheduler_type = "cosine"
max_steps = -1
warmup_ratio = 0.03
group_by_length = True
save_steps = 0
logging_steps = 25
```

## License

This repository is licensed under the [MIT License](LICENSE).

## Contact

For questions or issues, please contact [author](mailto:forootan@example.com).

---

This README provides a comprehensive guide to understanding and utilizing the script for fine-tuning the LLaMA-2-7b model using advanced techniques. Adjust file paths and parameters as needed based on your specific requirements.












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