LLAMA 3.1 Deployment with FAISS, Fine-Tuning Guidance, and Flask Integration
Budget: €30 – €250 EUR
We are seeking a skilled freelancer to set up LLAMA 3.1 (8B) locally with a vector database (FAISS), focusing on both high-quality AI tuning and efficient vector database management. The goal is to build a robust system that integrates seamlessly with a Flask-based interface (developed by our team) to manage fine-tuning and document workflows.
Key Deliverables:
- Deploy LLAMA 3.1 with FAISS in Docker, ensuring GPU optimization.
- Integrate the setup with a Flask-based GUI for document uploads and fine-tuning management.
- Provide clear guidance and best practices for fine-tuning the model using PyTorch.
- Offer strategies for organizing and optimizing the FAISS vector database.
- Deliver comprehensive documentation for replicating the setup internally.
We require someone with experience in deploying LLMs and vector databases who can ensure the system is portable (Docker-based) and capable of delivering accurate, high-quality AI responses.
Key Deliverables:
- Deploy LLAMA 3.1 with FAISS in Docker, ensuring GPU optimization.
- Integrate the setup with a Flask-based GUI for document uploads and fine-tuning management.
- Provide clear guidance and best practices for fine-tuning the model using PyTorch.
- Offer strategies for organizing and optimizing the FAISS vector database.
- Deliver comprehensive documentation for replicating the setup internally.
We require someone with experience in deploying LLMs and vector databases who can ensure the system is portable (Docker-based) and capable of delivering accurate, high-quality AI responses.