JurisQwen is a specialized legal domain language model based on Qwen2.5-7B, fine-tuned on Indian legal datasets. This model is designed to assist with legal queries, document analysis, and providing information about Indian law.

Model Details

Model Description

Developed by: Prathamesh Devadiga
Base Model: Qwen2.5-7B by Qwen
Model Type: Language Model with LoRA fine-tuning
Language: English with focus on Indian legal terminology
License: Apache-2.0
Finetuned from model: Qwen/Qwen2.5-7B
Framework: PEFT 0.15.1 with Unsloth optimization

Training Dataset

The model was fine-tuned on the "viber1/indian-law-dataset" which contains instruction-response pairs focused on Indian legal knowledge and terminology.

Technical Specifications

Model Architecture

Base model: Qwen2.5-7B
Fine-tuning method: LoRA (Low-Rank Adaptation)
LoRA configuration:
- Rank (r): 32
- Alpha: 64
- Dropout: 0.05
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Training Procedure

Training Infrastructure: NVIDIA A100-40GB GPU
Quantization: 4-bit quantization using bitsandbytes
Mixed Precision: bfloat16
Attention Implementation: Flash Attention 2
Training Hyperparameters:
- Epochs: 3
- Batch size: 16
- Gradient accumulation steps: 2
- Learning rate: 2e-4
- Weight decay: 0.001
- Scheduler: Cosine with 10% warmup
- Optimizer: AdamW 8-bit
- Maximum sequence length: 4096
- TF32 enabled for A100

Deployment Infrastructure

Deployed using Modal cloud platform
GPU: NVIDIA A100-40GB
Persistent volume storage for model checkpoints

Usage

Setting Up the Environment

This model is deployed using Modal. To use it, you'll need to:

Install Modal:

pip install modal

Authenticate with Modal:

modal token new

Deploy the application:

python app.py

Running Fine-tuning

To run the fine-tuning process:

python

from app import app, finetune_qwen

# Deploy the app
app.deploy()

# Run fine-tuning
result = finetune_qwen.remote()
print(f"Fine-tuning result: {result}")

Inference

To run inference with the fine-tuned model:

python

from app import app, test_inference

# Example legal query
response = test_inference.remote("What are the key provisions of the Indian Contract Act?")
print(response)

Input Format

The model uses the following format for prompts:

<|im_start|>user
[Your legal question here]
<|im_end|>

The model will respond with:

<|im_start|>assistant
[Legal response]
<|im_end|>

Limitations and Biases

The model is specifically trained on Indian legal data and may not generalize well to other legal systems
Legal advice provided by the model should not be considered as professional legal counsel
The model may exhibit biases present in the training data
Performance on complex or novel legal scenarios not present in the training data may be limited

Recommendations

Users should validate important legal information with qualified legal professionals
Always cross-reference model outputs with authoritative legal sources
Be aware that legal interpretations may vary and the model provides one possible interpretation

Environmental Impact

Hardware: NVIDIA A100-40GB GPU
Training time: Approximately 3-5 hours
Cloud Provider: Modal

Citation

If you use this model in your research, please cite:

@software{JurisQwen,
  author = {Prathamesh Devadiga},
  title = {JurisQwen: Indian Legal Domain Fine-tuned Qwen2.5-7B Model},
  year = {2025},
  url = {https://github.com/devadigapratham/JurisQwen}
}

Acknowledgments

Qwen team for the original Qwen2.5-7B model
Unsloth for optimization tools
Modal for deployment infrastructure
Creator of the "viber1/indian-law-dataset"

devadigaprathamesh/JurisQwen

JurisQwen: Legal Domain Fine-tuned Qwen2.5-7B Model

Overview