---
license: mit
language:
- en
metrics:
- perplexity
- accuracy
base_model:
- microsoft/Phi-3-mini-4k-instruct
library_name: transformers
tags:
- legal
---

# 🧑‍⚖️ vakil-phi3-mini-4k-instruct-finetuned

## 📌 Overview
This repository hosts a fine‑tuned version of **microsoft/Phi‑3‑mini‑4k‑instruct (3.8B parameters)**, adapted specifically for **Indian legal knowledge tasks**.  
The model was instruction‑tuned using **LoRA (Low‑Rank Adaptation)** on curated datasets covering constitutional acts and statutory sections.  
The objective of this fine‑tuning was to enhance the model’s ability to deliver **accurate, contextual, and explainable outputs** for legal queries in the Indian domain.

---

## ⚙️ Training Details
- **Base Model:** microsoft/Phi‑3‑mini‑4k‑instruct  
- **Fine‑Tuning Method:** LoRA (parameter‑efficient fine‑tuning)  
- **Domain Data:** Indian constitutional acts, statutory sections, and related legal texts  
- **Training Infrastructure:** RunPod RTX A6000 GPU  
- **Training Duration:** 18 hours, 2 epochs  
- **Optimization Goal:** Reduce training loss and improve domain‑specific accuracy  

---

## 📊 Evaluation
- **Intrinsic Evaluation:**  
  - Reduced perplexity compared to the base model  
  - Improved accuracy on domain‑specific test sets  

- **Extrinsic Evaluation:**  
  - Better parsing of statutes and structured legal outputs  
  - Enhanced contextual reasoning in legal Q&A tasks  

- **Qualitative Observations:**  
  - More consistent responses when asked about constitutional provisions  
  - Improved ability to generate structured JSON outputs for legal sections  

---

## 🚀 Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ManjunathCode10x/vakil-phi3-mini-4k-instruct-finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

inputs = tokenizer("Explain Article 21 of the Indian Constitution:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```