---
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
library_name: transformers
model_name: SmolLM2-FT-legal-india
tags:
- generated_from_trainer
- smol
- leagal-india
- indian law
- trl
- sft
licence: license
datasets:
- vishnun0027/Indian-Law
language:
- en
---

# Legal Language Model
This is a fine-tuned version of the **SmolLM2-135M-Instruct** model, trained on legal texts from the **Indian-Law** dataset by [vishnun0027](https://huggingface.co/datasets/vishnun0027/Indian-Law) on Hugging Face.

## 🤗 Model Availability
**The model is publicly available on Hugging Face:** [saicharan1010/SmolLM2-FT-legal-india](https://huggingface.co/saicharan1010/SmolLM2-FT-legal-india)

## Model Information
- **Base Model**: HuggingFaceTB/SmolLM2-135M-Instruct
- **Dataset**: Indian-Law (25,600 instruction-response pairs after cleaning)
- **Training**: Fine-tuned using SFT (Supervised Fine-Tuning)

## Training Details
- **Training Steps**: 1,000 steps
- **Batch Size**: 16
- **Learning Rate**: 5e-5
- **Final Training Loss**: 1.086

## Performance Evaluation
Evaluation on 1,280 test samples showed improved legal reasoning compared to the base model:
- **BLEU Score**: 0.126 (compared to base model's 0.121)
- **ROUGE-L F-Score**: 0.304

## Usage


### Using Transformers Library

```python
from transformers import pipeline, AutoTokenizer
# Load tokenizer and create pipeline
tokenizer = AutoTokenizer.from_pretrained("saicharan1010/SmolLM2-FT-legal-india")
pipe = pipeline("text-generation", model="saicharan1010/SmolLM2-FT-legal-india")
# Format with chat template
prompt = "Can a Vakalatnama be revoked or withdrawn in India?"
messages = [{"role": "user", "content": prompt}]
formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)
# Generate response
response = pipe(formatted_prompt, max_new_tokens=200)
print(response[0]['generated_text'])
```

This model is specifically optimized for legal language tasks in the Indian context. It shows improved understanding of Indian legal terminology and concepts compared to the base model.
## Training procedure

 
This model was trained with SFT.

### Framework versions

- TRL: 0.13.0
- Transformers: 4.48.1
- Pytorch: 2.6.0.dev20241224+cu126
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```