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Model: GSMS-B/Indian-Legal-Llama-3.2-3B Source: Original Platform
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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
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tags:
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- legal
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- indian-law
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- BNS
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- BNSS
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- BSA
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- criminal-law
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- llama
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- llama3
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- qlora
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- unsloth
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- domain-adaptation
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- instruction-tuning
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- question-answering
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- law
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- india
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datasets:
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- GSMS-B/Indian-Legal-QA-BNS-BNSS-BSA
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pipeline_tag: text-generation
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---
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# ⚖️ Indian Legal Llama 3.2 — 3B
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<p align="center">
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<img src="https://img.shields.io/badge/Base%20Model-Llama%203.2%203B-7C3AED?style=for-the-badge&logo=meta" alt="Base Model"/> <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"/>
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</p>
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---
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## 📖 Model Description
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**Indian Legal Llama 3.2 — 3B** is a domain-adapted version of [`unsloth/Llama-3.2-3B-Instruct`](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct), fine-tuned using QLoRA on a structured question-answer dataset covering all 1,059 sections of India's three 2023 criminal justice acts:
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| Act | Full Name | Replaces | Sections |
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|---|---|---|---|
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| 📕 **BNS 2023** | Bharatiya Nyaya Sanhita | IPC 1860 | 358 |
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| 📗 **BNSS 2023** | Bharatiya Nagarik Suraksha Sanhita | CrPC 1973 | 531 |
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| 📘 **BSA 2023** | Bharatiya Sakshya Adhiniyam | Indian Evidence Act 1872 | 170 |
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The model was trained on **6,354 instruction-format QA pairs** — 6 questions per section covering definitions, scenarios, legal elements, exceptions, and consequences — giving it broad coverage of Indian criminal law provisions.
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---
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## 🔗 Model Variants
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| Variant | Repo | Best For |
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|---|---|---|
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| 🟢 **Merged (this repo)** | `GSMS-B/Indian-Legal-Llama-3.2-3B` | Out-of-the-box inference, Gradio/API deployment |
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| 🔵 **LoRA Adapter** | [GSMS-B/Indian-Legal-Llama-3.2-3B-Adapter](https://huggingface.co/GSMS-B/Indian-Legal-Llama-3.2-3B-Adapter) | Lightweight loading on top of base model |
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| 🟡 **GGUF (Quantized)** | [GSMS-B/Indian-Legal-Llama-3.2-3B-GGUF](https://huggingface.co/GSMS-B/Indian-Legal-Llama-3.2-3B-GGUF) | CPU inference via Ollama / llama.cpp |
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---
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## 🚀 Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "GSMS-B/Indian-Legal-Llama-3.2-3B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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SYSTEM = "You are an expert legal assistant specializing in Indian criminal law — BNS, BNSS, and BSA 2023."
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def ask(question):
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messages = [
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{"role": "system", "content": SYSTEM},
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{"role": "user", "content": question}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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out = model.generate(**inputs, max_new_tokens=300, temperature=0.1,
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do_sample=True, pad_token_id=tokenizer.eos_token_id)
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return tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
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print(ask("What is a Zero FIR under BNSS 2023?"))
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```
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### 💻 Run locally with Ollama (GGUF)
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```bash
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ollama run hf.co/GSMS-B/Indian-Legal-Llama-3.2-3B-GGUF
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```
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---
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## 🎯 Recommended Use Cases
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> ⚠️ **Important Note:** This model has been domain-adapted on structured QA data and works best when used as a component in a larger system rather than as a standalone answer engine. Direct usage may produce incomplete or imprecise answers on complex legal queries.
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### ✅ Where this model works well
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| Use Case | How to Use |
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|---|---|
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| 🔍 **RAG (Retrieval-Augmented Generation)** | Use a retriever (BM25, vector search) to fetch relevant BNS/BNSS/BSA sections, then pass to this model as context for grounded answers |
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| 🤖 **Legal Chatbot Backend** | Combine with a document store of the actual act texts; use this model for generation with retrieved context |
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| 📚 **Legal Education Tool** | Build Q&A apps for law students learning the new 2023 acts |
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| 🔎 **Section Lookup Assistant** | Pair with a section index to quickly surface which section of BNS/BNSS/BSA applies to a given situation |
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| 🧪 **Research & Experimentation** | Fine-tune further on specific sub-domains (e.g., only BNSS procedure, only BSA evidence rules) |
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| 📝 **Structured Legal Summarization** | Summarize specific sections when given the section text as input context |
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### ❌ Not recommended for
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- Standalone legal advice without a retrieval component
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- High-stakes legal decisions without human expert review
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- Jurisdictions outside BNS / BNSS / BSA 2023 scope
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---
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## 🏋️ Training Details
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| Property | Value |
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|---|---|
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| Base model | meta-llama/Llama-3.2-3B-Instruct |
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| Fine-tuning method | QLoRA |
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| LoRA rank | 64 |
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| LoRA alpha | 128 |
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| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| Training data | 6,354 QA pairs — 1,059 sections × 6 question types |
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| Epochs | 3 |
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| Batch size (effective) | 4 |
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| Learning rate | 2e-4 |
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| Optimizer | adamw_8bit |
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| Hardware | Google Colab T4 GPU |
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| Framework | Unsloth + TRL SFTTrainer |
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| Prompt format | ChatML |
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---
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## 📊 Training Data
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| Dataset | Link |
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|---|---|
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| 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) |
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6 question types per section: `definitional_topic` · `definitional_section` · `scenario` · `elements` · `exceptions` · `consequence`
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---
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## 👤 Author
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**GSMS-B** — Bugatha Ganasyam Mani Sankar
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---
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## ⚠️ Disclaimer
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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.
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---
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*Fine-tuned using [Unsloth](https://github.com/unslothai/unsloth) for training efficiency.*
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