---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
Indian Legal Assistant: A LLaMA-based Model for Indian Legal Text Generation
This repository contains information and code for the Indian Legal Assistant, a LLaMA-based model finetuned on Indian legal texts. The model is designed to assist with various legal tasks and queries related to Indian law.

Table of Contents
Model Description
Model Details
Installation
Usage
Evaluation
Contributing
License
Model Description
The Indian Legal Assistant is a text generation model specifically trained to understand and generate text related to Indian law. It is suitable for various legal tasks such as:

Legal question answering
Case summarization
Legal document analysis
Statute interpretation
Model Details
Model Name: Indian_Legal_Assistant
Developer: varma007ut
Model Size: 8.03B parameters
Architecture: LLaMA
Language: English
License: Apache 2.0
Hugging Face Repository: varma007ut/Indian_Legal_Assistant
Installation
To use this model, install the required libraries:

bash
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pip install transformers torch
# For GGUF support
pip install llama-cpp-python
Usage
There are several ways to use the Indian Legal Assistant model:

1. Using Hugging Face Pipeline
python
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from transformers import pipeline

pipe = pipeline("text-generation", model="varma007ut/Indian_Legal_Assistant")

prompt = "Summarize the key points of the Indian Contract Act, 1872:"
result = pipe(prompt, max_length=200)
print(result[0]['generated_text'])
2. Using Hugging Face Transformers Directly
python
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from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("varma007ut/Indian_Legal_Assistant")
model = AutoModelForCausalLM.from_pretrained("varma007ut/Indian_Legal_Assistant")

prompt = "What are the fundamental rights in the Indian Constitution?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0]))
3. Using GGUF Format with llama-cpp-python
python
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from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="varma007ut/Indian_Legal_Assistant",
    filename="ggml-model-q4_0.gguf",  # Replace with the actual GGUF filename if different
)

response = llm.create_chat_completion(
    messages=[
        {"role": "user", "content": "Explain the concept of judicial review in India."}
    ]
)

print(response['choices'][0]['message']['content'])
4. Using Inference Endpoints
This model supports Hugging Face Inference Endpoints. You can deploy the model and use it via API calls. Refer to the Hugging Face documentation for more information on setting up and using Inference Endpoints.