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README.md

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+
+---
+license: apache-2.0
+language:
+- en
+- ar
+pipeline_tag: text-generation
+tags:
+- rag
+- hallucination-detection
+- evaluation
+- qwen
+- peft
+- lora
+- classification
+---
+
+# 🧠 Balash Faty — RAG Hallucination Judge (EN/AR)
+
+This model is a **fine-tuned Qwen2.5-3B-Instruct** model specialized in detecting **hallucinations in Retrieval-Augmented Generation (RAG)** answers in both English and Arabic.
+
+It acts as an **LLM judge** that determines whether an answer is **fully supported by the retrieved context**.
+
+---
+
+## 🎯 Task
+
+Given:
+
+- **Context** (retrieved documents)
+- **Question**
+- **Answer** (generated by an LLM)
+
+The model outputs:
+
+```
+
+PASS  → Answer is grounded in the context
+FAIL  → Answer contains hallucinations or unsupported claims
+
+```
+
+---
+
+## 🏗 Base Model
+
+- **Model:** Qwen/Qwen2.5-3B-Instruct  
+- **Fine-tuning:** LoRA → merged into base weights  
+- **Languages:** English + Arabic
+- **Training Objective:** Hallucination classification for RAG systems  
+
+---
+
+## ⚙️ Inference Format
+
+**Prompt Template:**
+
+```
+
+You are a system that detects hallucinations in RAG answers.
+
+Decide whether the answer is fully supported by the context.
+Reply with only one word: PASS or FAIL.
+
+[CONTEXT]
+{context}
+
+[QUESTION]
+{question}
+
+[ANSWER]
+{answer}
+
+Judgment:
+
+````
+
+---
+
+## 💻 Example (Python)
+
+```python
+import requests
+
+API_URL = "YOUR_HF_ENDPOINT_URL"
+HF_TOKEN = "hf_xxx"
+
+headers = {
+    "Authorization": f"Bearer {HF_TOKEN}",
+    "Content-Type": "application/json"
+}
+
+def judge(context, question, answer):
+    prompt = f"""You are a system that detects hallucinations in RAG answers.
+
+Decide whether the answer is fully supported by the context.
+Reply with only one word: PASS or FAIL.
+
+[CONTEXT]
+{context}
+
+[QUESTION]
+{question}
+
+[ANSWER]
+{answer}
+
+Judgment:"""
+
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "max_new_tokens": 5,
+            "do_sample": False,
+            "temperature": 0.0
+        }
+    }
+
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()[0]["generated_text"]
+````
+
+---
+
+## 📊 Training Data
+
+The model was trained on a labeled dataset of RAG examples from HaluBench:
+
+| Field    | Description          |
+| -------- | -------------------- |
+| Context  | Retrieved passages   |
+| Question | User query           |
+| Answer   | LLM-generated answer |
+| Label    | PASS / FAIL          |
+
+The dataset is balanced between grounded and hallucinated answers.
+
+---
+
+## 🚀 Intended Use
+
+✅ Evaluating RAG pipelines
+✅ LLM-as-a-judge research
+✅ Automatic hallucination detection
+✅ Benchmarking grounding quality
+
+❌ Not for open-ended chat
+❌ Not a knowledge source
+
+---
+
+## 🧩 Deployment
+
+Optimized for **low-latency inference** using Hugging Face **Text Generation Inference (TGI)** endpoints.
+
+---
+
+## 👤 Author
+
+Ahmed Abolfadl
+B.Sc. Computer Science & Engineering — German University in Cairo
+Research focus: ML, AI, Data Science
+
+---
+
+## 📅 Model Version
+
+Uploaded on: 2026-01-26