Julien Heiduk
Let's talk about recommandation systems and ML in real-world business scenarios.
FastMCP Server with Hugging Face Hub Resources
Build a FastMCP server that exposes Hugging Face Hub models and datasets as queryable MCP resources for LLM agents.
FastMCP Server with Hugging Face Hub ResourcesQuerying the Hugging Face Hub with a Tiny LLM and FastMCP
Load Qwen2.5-0.5B-Instruct, connect it to a FastMCP server via context injection, and answer live questions about the Hugging Face Hub catalogue.
Querying the Hugging Face Hub with a Tiny LLM and FastMCPFine-Tune LLMs with QLoRA
Fine-tune a large language model with QLoRA on a single GPU, then serve it at high throughput using vLLM’s PagedAttention engine.
Fine-Tune LLMs with QLoRAGCN with PyTorch for Co-Purchase Recommendation
Graph Convolutional Networks turn co-purchase data into a graph and learn item embeddings for recommendation. A full PyTorch Geometric implementation included.
GCN with PyTorch for Co-Purchase RecommendationRetrieval-Augmented Generation with LangChain
RAG grounds LLM answers in your own documents. Learn how to build a production-ready RAG pipeline with LangChain, FAISS, and OpenAI in Python.
Retrieval-Augmented Generation with LangChain