Update README.md

README.md

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+---
+
+# 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:[email protected]).
+
+---
+
+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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