Instructions to use GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M

Ollama
How to use GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF with Ollama:
```
ollama run hf.co/GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M
```

Lemonade

How to use GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Indian-Legal-Qwen2.5-3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Indian-Legal-Qwen2.5-3B-GGUF / README.md

GSMS-B

Update README.md

b1be624 verified 3 months ago

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metadata

language:
  - en
license: apache-2.0
base_model: unsloth/Qwen2.5-3B-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 — 3B (GGUF)

🟡 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.

📖 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:

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

🔗 Model Family — Qwen 2.5 3B

Variant	Repo	Best For
🟢 Merged	GSMS-B/Indian-Legal-Qwen2.5-3B	Out-of-the-box inference, Gradio / API deployment
🔵 LoRA Adapter	GSMS-B/Indian-Legal-Qwen2.5-3B-Adapter	Lightweight loading on top of base model
🟡 GGUF (this repo)	`GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF`	CPU inference via Ollama / llama.cpp

🚀 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"])

🎯 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	Run 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/Qwen2.5-3B-Instruct-bnb-4bit`
🔧 Fine-tuning method	QLoRA
🎛️ LoRA rank	16
🎛️ LoRA alpha	16
🧩 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)	2
📈 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)

📊 Training Dataset

📂 Dataset	🔗 Link
Indian Legal QA — BNS + BNSS + BSA 2023	GSMS-B/Indian-Legal-QA-BNS-BNSS-BSA

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 for training efficiency.