LegalEase_IndianConsumerLaw

Model Details

Model Description

LegalEase_IndianConsumerLaw is a fine-tuned model designed to assist in answering legal questions related to Indian consumer laws. It is based on Mistral-7B and optimized for structured legal Q&A, consumer rights explanations, and legal text interpretation.

• Developed by: Gulfarogh Azam
• Model type: Causal Language Model (LLM)
• Language(s): English (with a focus on Indian legal terminology)
• Finetuned from: mistralai/Mistral-7B-v0.1
• Trained on: The Consumer Protection Act, 2019, India.

Model Sources

• Repository: Hugging Face Model Repo

Uses

Direct Use

• Legal professionals, researchers, and individuals can query the model for Indian consumer law-related information.
• Useful for understanding legal provisions and consumer rights.

Downstream Use

• Can be integrated into legal chatbots or AI-powered legal assistance tools.

Out-of-Scope Use

• Not a replacement for legal advice from a qualified lawyer.
• May not cover state-specific regulations.
• Should not be used for non-legal inquiries.

Bias, Risks, and Limitations

Bias

• The model has been trained on a limited dataset, which may introduce biases.
• Responses may not always align with the latest amendments in law.

Risks

• May generate incorrect or outdated legal interpretations.
• Does not account for regional variations in legal practice.

Recommendations

Users should cross-check critical legal information with official sources before making any decisions.

How to Use the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "gazam/LegalEase_IndianConsumerLaw"

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(repo_id)
tokenizer = AutoTokenizer.from_pretrained(repo_id)

# Generate a response
input_text = "What are my rights under the Consumer Protection Act?"
inputs = tokenizer(input_text, return_tensors="pt")
output = model.generate(**inputs, max_length=256)

print(tokenizer.decode(output[0], skip_special_tokens=True))