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

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	---
	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)

	<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](https://huggingface.co/GSMS-B/Indian-Legal-Qwen2.5-3B) · For lightweight adapter loading see the [Adapter](https://huggingface.co/GSMS-B/Indian-Legal-Qwen2.5-3B-Adapter).

	---

	## 📖 Model Description

	Indian Legal Qwen 2.5 — 3B (GGUF) is a quantized, CPU-friendly version of [`GSMS-B/Indian-Legal-Qwen2.5-3B`](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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
	```bash
	ollama run hf.co/GSMS-B/Indian-Legal-Qwen2.5-3B-GGUF
	```

	### ⚙️ Run with llama.cpp
	```bash
	./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
	```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](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`

	---

	## 👤 Author

	GSMS-B — Bugatha Ganasyam Mani Sankar
	🤗 [Hugging Face Profile](https://huggingface.co/GSMS-B)

	---

	## ⚠️ 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.