README.md

---
tags:
- gguf
- llama.cpp
- ollama
- unsloth
- indian-law
- legal-ai
- phi-3
- qlora
- kanoonu-ai
license: apache-2.0
datasets:
- tejasgowda05/Indian-Kanoonu-Dataset
language:
- en
base_model:
- tejasgowda05/Kanoonu-AI-Phi3-Finetuned
---

<div align="center">

# ⚖️ Kanoonu AI — Phi-3 GGUF (Q4_K_M)

**Quantized GGUF version of Kanoonu AI — ready for local deployment**

[![LoRA Adapter](https://img.shields.io/badge/🤗_LoRA-Kanoonu--AI--Phi3--Finetuned-blue)](https://huggingface.co/tejasgowda05/Kanoonu-AI-Phi3-Finetuned)
[![GGUF](https://img.shields.io/badge/🤗_GGUF-Kanoonu--AI--Phi3--GGUF-green)](https://huggingface.co/tejasgowda05/Kanoonu-AI-Phi3-GGUF)
[![Dataset](https://img.shields.io/badge/🤗_Dataset-Indian--Kanoonu--Dataset-orange)](https://huggingface.co/datasets/tejasgowda05/Indian-Kanoonu-Dataset)
[![License](https://img.shields.io/badge/License-Apache_2.0-yellow)](https://opensource.org/licenses/Apache-2.0)

</div>

---

## 📖 Overview

This is the **GGUF quantized version** of [`tejasgowda05/Kanoonu-AI-Phi3-Finetuned`](https://huggingface.co/tejasgowda05/Kanoonu-AI-Phi3-Finetuned) — a Phi-3-mini model fine-tuned on 23,370 Indian law Q&A pairs covering the Indian Penal Code (IPC), Code of Criminal Procedure (CrPC), Constitution of India, and other statutes.

The GGUF format allows this model to run **locally on CPU or GPU** without requiring a high-end machine, making Indian legal information accessible to everyone.

---

## 📦 Available Files

| File | Quantization | Size | Use Case |
|------|-------------|------|----------|
| `phi-3-mini-4k-instruct.Q4_K_M.gguf` | Q4_K_M | ~2.2 GB | ✅ Recommended — best balance of size and quality |

### What is Q4_K_M?
Q4_K_M is a 4-bit quantization method that compresses the model to ~2.2GB with negligible quality loss compared to the full precision version. It runs comfortably on most modern laptops.

---

## 🚀 Quick Start

### Option 1 — Ollama (Easiest)
```bash
# Step 1 — Install Ollama from https://ollama.com/download

# Step 2 — Pull and run directly
ollama run hf.co/tejasgowda05/Kanoonu-AI-Phi3-GGUF:Q4_K_M
```



### Option 2 — llama.cpp CLI

```bash
llama-cli -hf tejasgowda05/Kanoonu-AI-Phi3-GGUF --jinja
```

### Option 3 — llama-cpp-python

```python
from llama_cpp import Llama

llm = Llama(
    model_path = "./kanoonu_model/phi-3-mini-4k-instruct.Q4_K_M.gguf",
    n_ctx      = 2048,
    n_threads  = 4,
)

response = llm(
    "<|system|>\nYou are Kanoonu AI, an expert Indian legal assistant.\n<|end|>\n"
    "<|user|>\nWhat is an FIR and how is it filed in India?<|end|>\n"
    "<|assistant|>\n",
    max_tokens = 200,
    stop       = ["<|end|>", "<|endoftext|>"],
)

print(response["choices"][0]["text"])
```

### Option 4 — Python with ctransformers

```python
from ctransformers import AutoModelForCausalLM

llm = AutoModelForCausalLM.from_pretrained(
    "tejasgowda05/Kanoonu-AI-Phi3-GGUF",
    model_file = "phi-3-mini-4k-instruct.Q4_K_M.gguf",
    model_type = "mistral",
)

print(llm("What are the fundamental rights in the Indian Constitution?"))
```

---

## 💻 Hardware Requirements

| Setup | Minimum RAM | Performance |
|-------|-------------|-------------|
| CPU only | 8 GB RAM | Slow (~1-2 tokens/sec) |
| CPU + 8GB RAM | 8 GB RAM | Moderate (~3-5 tokens/sec) |
| GPU (4GB VRAM) | 4 GB VRAM | Fast (~15-20 tokens/sec) |
| GPU (8GB VRAM) | 8 GB VRAM | Very Fast (~30+ tokens/sec) |

---

## 🏗️ How This Was Created

```
microsoft/Phi-3-mini-4k-instruct  (3.8B base model)
         ↓
   QLoRA Fine-tuning
   (24,607 Indian law Q&A pairs)
         ↓
tejasgowda05/Kanoonu-AI-Phi3-Finetuned  (LoRA adapters)
         ↓
   Merge LoRA → Convert to GGUF → Quantize Q4_K_M
   (via Unsloth)
         ↓
tejasgowda05/Kanoonu-AI-Phi3-GGUF  ← you are here
```

| Training Metric | Value |
|----------------|-------|
| Final Train Loss | 0.3478 |
| Best Eval Loss | 0.6568 |
| Training Examples | 23,370 |
| Training Time | ~270 minutes |

---

## 🔗 Related Resources

| Resource | Link |
|----------|------|
| 🤗 LoRA Adapter | [tejasgowda05/Kanoonu-AI-Phi3-Finetuned](https://huggingface.co/tejasgowda05/Kanoonu-AI-Phi3-Finetuned) |
| 🤗 GGUF Model (this repo) | [tejasgowda05/Kanoonu-AI-Phi3-GGUF](https://huggingface.co/tejasgowda05/Kanoonu-AI-Phi3-GGUF) |
| 🤗 Formatted Dataset | [tejasgowda05/Indian-Kanoonu-Dataset](https://huggingface.co/datasets/tejasgowda05/Indian-Kanoonu-Dataset) |
| 📦 Base Model | [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) |
| 📦 Original Dataset | [viber1/indian-law-dataset](https://huggingface.co/datasets/viber1/indian-law-dataset) |

---

## ⚠️ Limitations & Disclaimer

- This model is intended for **educational and informational purposes only**
- It is **not a substitute for professional legal advice**
- Always consult a qualified lawyer for legal matters
- The model may occasionally produce inaccurate or outdated legal information

---

## 👤 Author

**Tejas Gowda N** — [`tejasgowda05`](https://huggingface.co/tejasgowda05)

Built as part of the **Kanoonu AI** project — making Indian legal information accessible through conversational AI.

---

## 📄 License

Apache 2.0 — inherited from `microsoft/Phi-3-mini-4k-instruct` and the original dataset.

---

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

*Trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)*