GSMS-B/Indian-Legal-Llama-3.2-3B-GGUF

⚖️🦙 Indian Legal Llama 3.2 — 3B · GGUF

<p align="center"> <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/Type-GGUF%20Quantized-F59E0B?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/Runtime-Ollama%20%7C%20llama.cpp-10B981?style=for-the-badge" alt="Runtime"/> <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 — optimised for CPU inference via Ollama or llama.cpp. No GPU required. For full-precision PyTorch usage, see the Merged Model.

📖 Model Description

Indian Legal Llama 3.2 — 3B (GGUF) is a quantized, CPU-friendly version of the domain-adapted Llama 3.2 3B model, fine-tuned via QLoRA on `unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit` and covering all 1,059 sections of India's three 2023 criminal justice reform acts:

Trained on 6,354 instruction-format QA pairs — 6 question types per section covering definitions, scenarios, legal elements, exceptions, and consequences.

🔗 Model Family — Llama 3.2 3B

🚀 Quick Start

🖥️ Run with Ollama (easiest)

ollama run hf.co/GSMS-B/Indian-Legal-Llama-3.2-3B-GGUF

🐍 Run with llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="GSMS-B/Indian-Legal-Llama-3.2-3B-GGUF",
    filename="*Q4_K_M.gguf",   # recommended quant
    n_ctx=2048,
    verbose=False
)

response = llm.create_chat_completion(
    messages=[
        {"role": "system",  "content": "You are an expert legal assistant specializing in Indian criminal law — BNS, BNSS, and BSA 2023."},
        {"role": "user",    "content": "What is a Zero FIR under BNSS 2023?"}
    ],
    max_tokens=300,
    temperature=0.1
)
print(response["choices"][0]["message"]["content"])

🦜 LangChain integration

from langchain_community.llms import LlamaCpp

llm = LlamaCpp(
    model_path="path/to/Indian-Legal-Llama-3.2-3B.Q4_K_M.gguf",
    n_ctx=2048,
    temperature=0.1,
    verbose=False
)
print(llm.invoke("Explain the presumption of innocence under BSA 2023."))

🎯 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

❌ 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

📊 Training Dataset

6 question types per section: definitional_topic · definitional_section · scenario · elements · exceptions · consequence

👤 Author

GSMS-B — Bugatha Ganasyam Mani Sankar 🤗 Hugging Face Profile

⚠️ 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.

⚡ Fine-tuned using [Unsloth](https://github.com/unslothai/unsloth) for training efficiency. Quantized to GGUF for broad CPU compatibility.