This guide provides instructions to set up a text generation pipeline with streamlit using a fine-tuned LLaMA 2 model, leveraging QLoRA for parameter-efficient fine-tuning (PEFT). The model is trained with the Guanaco dataset, and streamlit is used to create a simple web-based interface for testing and showcasing the model's capabilities.
Requirements Python 3.7+ CUDA-compatible GPU Hugging Face Transformers library Datasets library PyTorch with CUDA support bitsandbytes LoRA (Low-Rank Adaptation) streamlit Ngrok Docker (optional, for containerization) Setup To set up this project, ensure you have the necessary libraries installed. You can use a virtual environment to keep the dependencies organized:
python -m venv llm_venv
source llm_venv/bin/activate # Linux/macOS
.\llm_venv\Scripts\activate # Windows
pip install torch transformers datasets bitsandbytes peft streamlit pyngrok
Fine-Tuning the LLaMA 2 Model The provided code snippet fine-tunes the LLaMA 2 model using QLoRA. The dataset used for fine-tuning is mlabonne/guanaco-llama2-1k, which contains 1000 instruction-following samples.
Key Parameters for Fine-Tuning LoRA Parameters: Set the rank, alpha, and dropout for LoRA layers. bitsandbytes Parameters: Enable 4-bit precision loading for memory efficiency. TrainingArguments Parameters: Configure the training process, including the number of epochs, batch size, and learning rate. Fine-Tuning Process Load the dataset and tokenizer. Configure the LoRA settings. Create the SFTTrainer instance for supervised fine-tuning. Train the model with the configured parameters. Save the fine-tuned model to disk for later use. Building the Streamlit Interface Streamlit is used to create a simple web-based interface that allows users to interact with the fine-tuned model.
Function to Generate Responses A simple function, generate_response, is defined to take a user prompt and generate a response using the fine-tuned model. This function is used as the primary interface for Streamlit.
Streamlit Interface The Streamlit interface is configured with a text input for user prompts and a text output for the generated responses. Additional parameters control the model's behavior, such as temperature, repetition_penalty, and top-k filtering.
Running Streamlit with Ngrok To make the Streamlit interface accessible on a public URL, Ngrok is used to create a secure tunnel to the local Streamlit server. This allows others to access the interface over the internet.
Docker Support For a more portable and reproducible setup, consider using Docker. A Dockerfile can be used to create an environment to run the Streamlit server. Here's a brief summary of how to build and run the Docker container:
Build the Docker Image: docker build -t streamlit-llama2 .
Run the Docker Container: docker run --gpus all -p 8000:8000 streamlit-llama2
Access the streamlit Interface: Once the container is running, you can access the streamlit interface at http://localhost:8000. If using Ngrok, update the streamlit server port and connect with Ngrok to get a public URL. Running the Project To run the project without Docker, execute the provided Python script. This script:
Fine-tunes the LLaMA 2 model using the specified parameters. Sets up the streamlit interface with the generate_response function. Starts the Ngrok tunnel and launches the streamlit server.
Conclusion With this guide, you can create a text generation pipeline using a fine-tuned LLaMA 2 model, integrating QLoRA for efficient fine-tuning, and streamlit for a web-based interface. This setup is suitable for prototyping AI-powered applications that require text generation capabilities.