Update README.md

README.md

@@ -1,23 +1,179 @@
 ---
+language:
+- en
+license: apache-2.0
+base_model: unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit
 tags:
+- legal
+- indian-law
+- BNS
+- BNSS
+- BSA
+- criminal-law
+- qwen
+- qwen2.5
 - gguf
 - llama.cpp
+- ollama
+- qlora
 - unsloth
+- domain-adaptation
+- instruction-tuning
+- question-answering
+- law
+- india
+datasets:
+- GSMS-B/Indian-Legal-QA-BNS-BNSS-BSA
+pipeline_tag: text-generation
+---
+
+# ⚖️🐉 Indian Legal Qwen 2.5 — 1.5B (GGUF)
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Base%20Model-Qwen%202.5%201.5B-6366F1?style=for-the-badge" alt="Base Model"/>
+  <img src="https://img.shields.io/badge/Type-GGUF%20Quantized-A855F7?style=for-the-badge" alt="Type"/>
+  <img src="https://img.shields.io/badge/Domain-Indian%20Criminal%20Law-DC2626?style=for-the-badge" alt="Domain"/>
+  <img src="https://img.shields.io/badge/Method-QLoRA-2563EB?style=for-the-badge" alt="Method"/>
+  <img src="https://img.shields.io/badge/Acts-BNS%20%7C%20BNSS%20%7C%20BSA-16A34A?style=for-the-badge" alt="Acts"/>
+  <img src="https://img.shields.io/badge/License-Apache%202.0-F59E0B?style=for-the-badge" alt="License"/>
+</p>
+
+> 🟡 **This is the GGUF-quantized version** — for CPU inference via Ollama or llama.cpp. For full-precision inference see the [Merged Model](https://huggingface.co/GSMS-B/Indian-Legal-Qwen2.5-1.5B) · For lightweight adapter loading see the [Adapter](https://huggingface.co/GSMS-B/Indian-Legal-Qwen2.5-1.5B-Adapter).
+
+---
+
+## 📖 Model Description
+
+**Indian Legal Qwen 2.5 — 1.5B (GGUF)** is a quantized, CPU-friendly version of [`GSMS-B/Indian-Legal-Qwen2.5-1.5B`](https://huggingface.co/GSMS-B/Indian-Legal-Qwen2.5-1.5B), a domain-adapted model fine-tuned using QLoRA on a structured question-answer dataset covering all **1,059 sections** of India's three landmark 2023 criminal justice reform acts:
+
+| Act | Full Name | Replaces | Sections |
+|---|---|---|---|
+| 📕 **BNS 2023** | Bharatiya Nyaya Sanhita | IPC 1860 | 358 |
+| 📗 **BNSS 2023** | Bharatiya Nagarik Suraksha Sanhita | CrPC 1973 | 531 |
+| 📘 **BSA 2023** | Bharatiya Sakshya Adhiniyam | Indian Evidence Act 1872 | 170 |
+
+Trained on **6,354 instruction-format QA pairs** — 6 question types per section covering definitions, scenarios, legal elements, exceptions, and consequences — giving it broad, structured coverage of India's reformed criminal law framework. As the smallest model in the family, this GGUF build is ideal for fast, fully offline CPU inference.
+
+---
+
+## 🔗 Model Family — Qwen 2.5 1.5B
+
+| Variant | Repo | Best For |
+|---|---|---|
+| 🟢 **Merged** | [GSMS-B/Indian-Legal-Qwen2.5-1.5B](https://huggingface.co/GSMS-B/Indian-Legal-Qwen2.5-1.5B) | Out-of-the-box inference, Gradio / API deployment |
+| 🔵 **LoRA Adapter** | [GSMS-B/Indian-Legal-Qwen2.5-1.5B-Adapter](https://huggingface.co/GSMS-B/Indian-Legal-Qwen2.5-1.5B-Adapter) | Lightweight loading on top of base model |
+| 🟡 **GGUF (this repo)** | `GSMS-B/Indian-Legal-Qwen2.5-1.5B-GGUF` | CPU inference via Ollama / llama.cpp |
+
+---
+
+## 🚀 Quick Start
+
+### 💻 Run with Ollama
+```bash
+ollama run hf.co/GSMS-B/Indian-Legal-Qwen2.5-1.5B-GGUF
+```
+
+### ⚙️ Run with llama.cpp
+```bash
+./llama-cli \
+  -hf GSMS-B/Indian-Legal-Qwen2.5-1.5B-GGUF \
+  -p "What is a Zero FIR under BNSS 2023?" \
+  -n 300 \
+  --temp 0.1
+```
+
+### 🐍 Run with llama-cpp-python
+```python
+from llama_cpp import Llama
+
+llm = Llama.from_pretrained(
+    repo_id="GSMS-B/Indian-Legal-Qwen2.5-1.5B-GGUF",
+    filename="*.gguf",
+)
+
+SYSTEM = "You are an expert legal assistant specializing in Indian criminal law — BNS, BNSS, and BSA 2023."
+
+response = llm.create_chat_completion(
+    messages=[
+        {"role": "system", "content": SYSTEM},
+        {"role": "user", "content": "What is a Zero FIR under BNSS 2023?"}
+    ],
+    temperature=0.1,
+    max_tokens=300
+)
+
+print(response["choices"][0]["message"]["content"])
+```
+
+---
+
+## 🎯 Recommended Use Cases
+
+> ⚠️ **Important Note:** This model has been domain-adapted on structured QA data and works best as a **component in a larger pipeline** rather than a standalone answer engine. Direct usage without retrieval context may produce incomplete or imprecise answers on complex legal queries.
+
+### ✅ Where this model excels
+
+| Use Case | 💡 How to Use |
+|---|---|
+| 🔍 **RAG Pipeline** | Pair with a BM25 or vector retriever over BNS/BNSS/BSA texts; feed retrieved sections as context for grounded, citation-backed answers |
+| 🤖 **Legal Chatbot Backend** | Use as the generation backbone of a legal assistant app with a ChromaDB / FAISS document store |
+| 📚 **Legal Education Tool** | Build interactive Q&A apps for law students and practitioners learning the 2023 criminal justice reforms |
+| 🔎 **Section Lookup Assistant** | Combine with a section index to surface the exact BNS / BNSS / BSA provision relevant to a given situation |
+| 💻 **Offline / Edge Deployment** | Smallest model in the family, runnable on consumer CPUs without a GPU — ideal for local apps, kiosks, or low-resource environments |
+| 📝 **Structured Legal Summarization** | Summarize individual sections when the section text is supplied as input context |
+| 🏛️ **Legal NLP Research** | Benchmark Indian criminal law understanding across model families (Qwen vs Llama) |
+| ⚖️ **Comparative Law Analysis** | Highlight differences between old acts (IPC/CrPC/IEA) and their 2023 replacements |
+
+### ❌ Not recommended for
+- Standalone legal advice without a retrieval component
+- High-stakes legal decisions without qualified human review
+- Jurisdictions or acts outside BNS / BNSS / BSA 2023
+
+---
+
+## 🏋️ Training Details
+
+| Property | Value |
+|---|---|
+| 🤖 Base model | `unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit` |
+| 🔧 Fine-tuning method | QLoRA |
+| 🎛️ LoRA rank | 64 |
+| 🎛️ LoRA alpha | 128 |
+| 🧩 Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| 📊 Training data | 6,354 QA pairs — 1,059 sections × 6 question types |
+| 🔁 Epochs | 3 |
+| 📦 Batch size (per device) | 4 |
+| 📈 Learning rate | 2e-4 |
+| ⚙️ Optimizer | adamw_8bit |
+| 💻 Hardware | Google Colab T4 GPU |
+| 🛠️ Framework | Unsloth + TRL SFTTrainer |
+| 💬 Prompt format | ChatML |
+| 🗜️ Quantization | GGUF (converted from merged FP16 model) |
 
 ---
 
-# Indian-Legal-Qwen2.5-1.5B-GGUF : GGUF
+## 📊 Training Dataset
+
+| 📂 Dataset | 🔗 Link |
+|---|---|
+| Indian Legal QA — BNS + BNSS + BSA 2023 | [GSMS-B/Indian-Legal-QA-BNS-BNSS-BSA](https://huggingface.co/datasets/GSMS-B/Indian-Legal-QA-BNS-BNSS-BSA) |
+
+**6 question types per section:**
+`definitional_topic` · `definitional_section` · `scenario` · `elements` · `exceptions` · `consequence`
+
+---
 
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+## 👤 Author
 
-**Example usage**:
-- For text only LLMs:    `llama-cli -hf GSMS-B/Indian-Legal-Qwen2.5-1.5B-GGUF --jinja`
-- For multimodal models: `llama-mtmd-cli -hf GSMS-B/Indian-Legal-Qwen2.5-1.5B-GGUF --jinja`
+**GSMS-B** — Bugatha Ganasyam Mani Sankar
+🤗 [Hugging Face Profile](https://huggingface.co/GSMS-B)
 
-## Available Model files:
-- `qwen2.5-1.5b-instruct.Q4_K_M.gguf`
+---
+
+## ⚠️ Disclaimer
+
+This model is intended for **research and educational purposes only**. It does not constitute legal advice. Outputs should not be relied upon for any legal decision without review by a qualified legal professional. The model's responses reflect patterns in training data and may contain errors or omissions.
+
+---
 
-## Ollama
-An Ollama Modelfile is included for easy deployment.
-This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+*⚡ Fine-tuned using [Unsloth](https://github.com/unslothai/unsloth) for training efficiency.*