Building an AI Assistant with Hugging Face

Chapter 1: Getting Started – Setup & Motivation

• Objectives: Understand what your assistant will do.

• Install tools: transformers, datasets, huggingface_hub, accelerate, optionally gradio.

• Acquire a free HF account and set up an API token.

Chapter 2: Selecting a Foundation Model

• Overview: Criteria for picking the base LLM (size, capabilities, license).

• Demo: Use transformers to load models like GPT‑2, Llama‑2, Bloom, or community‑finetuned chat models.

• Hands-on: Querying the model and analyzing responses.

Chapter 3: Instruction Fine-Tuning

• Why instruction-tune for assistant behavior.

• Prepare a JSONL dataset of instruction/query/response.

• Use transformers.Trainer or trlx for fine‑tuning.

• Optionally use LoRA/PEFT to efficiently adapt a model.

Chapter 4: Retrieval-Augmented Generation (RAG)

• Motivation: Giving your assistant access to up‑to‑date or specialized data.

• Tools: faiss (vector DB), datasets, or the HF Dataset Streams.

• Build a mini knowledge base from local docs.

• Integrate retrieval steps before inference to boost relevancy.

Chapter 5: Building an Interactive Interface

• Choose deployment: a CLI, Web UI (gradio), or integration into a chatbot platform.

• Walkthrough: Build a Gradio UI that sends user input and displays responses.

• Showcase: how retrieval results show up in chat.

Chapter 6: Model Hosting & Deployment

• Upload your custom model to 🤗 Hub.

• Use HF Inference API or deploy using Accelerate + FastAPI (or TorchServe) on your own server.

• Secure endpoints, manage resource limits (GPU vs CPU).

Chapter 7: Evaluating & Monitoring Assistant Quality

• Set up evaluation metrics: BLEU, ROUGE, or human‑in‑the‑loop feedback.

• Logging conversations, rating responses.

• Optionally use HF Evaluator or commercial tools.

Chapter 8: Optional: Multi-Turn Conversations & Context Management

• Strategies to maintain dialogue context.

• Token budgeting: sliding window vs retrieval.

• Demo: Maintaining context through longer sessions.

Chapter 9: Advanced Features & Improvements

• Add tool‑use plugin: call external APIs (weather, calculators, search).

• Use langchain + HF model for tool orchestration.

• Add safety filters, profanity cleanup, guardrails.

Chapter 10: Next Steps & Best Practices

• Scaling to larger datasets or real-time knowledge bases.

• Collaborating with community, publishing via HF Spaces.

• Ethics, licensing, cost monitoring, and open-source contribution.

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🛠️ What These Chapters Cover

Module	Microsoft Copilot Studio	HF Equivalent
Setup	Azure resources, accounts	HF token, local env, accelerate
Models	MS-certified models	HF models (Llama, GPT2, BLOOM)
Instruction tuning	Fine-tune via Studio UI	HF Transformers + PEFT or LoRA
RAG	Copilot ingestion	faiss, datasets, retrieval pipelines
Chat UI	Copilot chat preview	gradio or chatbot interfaces
Deploy	Azure endpoints	HF Hub + API / self-hosted
Monitor	Studio metrics	Custom eval & HF hub analytics

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