Indian-Legal-Qwen2.5-3B-GGUF

⚖️🐉 Indian Legal Qwen 2.5 — 3B (GGUF)

<p align="center"> <img src="https://img.shields.io/badge/Base%20Model-Qwen%202.5%203B-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 · For lightweight adapter loading see the Adapter.

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📖 Model Description

Indian Legal Qwen 2.5 — 3B (GGUF) is a quantized, CPU-friendly version of GSMS-B/Indian-Legal-Qwen2.5-3B, 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:

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. This GGUF build lets you run the model without a GPU, using llama.cpp or Ollama.

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🔗 Model Family — Qwen 2.5 3B

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🚀 Quick Start

💻 Run with Ollama

ollama run hf.co/GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF

⚙️ Run with llama.cpp

./llama-cli \
  -hf GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF \
  -p "What is a Zero FIR under BNSS 2023?" \
  -n 300 \
  --temp 0.1

🐍 Run with llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="GSMS-B/Indian-Legal-Qwen2.5-3B-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"])

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🎯 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

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🏋️ Training Details

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📊 Training Dataset

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

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👤 Author

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

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⚠️ 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.

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⚡ Fine-tuned using Unsloth for training efficiency.