kevindoescode/HALT-RAG-Demo

🛡️ HALT-RAG: Hallucination-Aware Retrieval-Augmented Generation

A complete, end-to-end research-style demo system for Google Colab (A100 GPU).

Quick Start

1. Download HALT_RAG_Demo.ipynb
2. Open in Google Colab
3. Select A100 GPU runtime (Runtime → Change runtime type → A100)
4. Run all cells

Direct Colab link: Open in Colab (or upload manually)

What's Included

Section	Description
1. Setup	Package installation + GPU verification
2. Dataset	55 synthetic test cases (5 domains, 3 difficulties)
3. Retrieval	3 strategies: Hybrid (BM25+FAISS RRF), Dense, Two-stage rerank
4-5. RAG + Models	TinyLlama-1.1B-Chat, DistilGPT2, Extractive-Fallback
6. Agents	PlannerAgent, ExecutorAgent, CriticAgent, LoggingAgent
7. Tools	RetrievalTool, VerificationTool, KeywordSearchTool
8. Hallucination Detection	Multi-signal: overlap, grounding, semantic similarity, factual indicators
9. Logging	Structured [PLANNER], [EXECUTOR], [CRITIC], [LOG] output
10. Dynamic Update	add_new_document() — live KB updates without retraining
11. Evaluation	Full pipeline over 495 total runs
12. Plots	6 matplotlib visualizations
13. Summary	Best strategy/model, observations, limitations

Requirements

• Google Colab Pro with A100 GPU
• ~3 GB VRAM (TinyLlama + DistilGPT2 + embeddings)
• ~10 minutes total runtime

Key Features

• No external agent frameworks — all agents are simple Python classes
• No fabricated results — all metrics computed from actual model outputs
• Explicit limitations stated at the end
• Reproducible — deterministic generation (do_sample=False)

License

MIT

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "kevindoescode/HALT-RAG-Demo"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.