CFR 49 Fine-Tuned LLM Work in Progress
A specialized language model for federal transportation regulations and compliance.
Overview
This model is a fine-tuned LLM based on Llama 3.2 - 1b, specifically trained on Title 49 of the Code of Federal Regulations (CFR 49). It is designed to assist in transportation law, safety regulations, and compliance requirements by providing accurate and contextual responses.
⚠️ Disclaimer: This model is for informational purposes only and should not be used as a substitute for legal advice. Always verify information with official federal sources.
Features
✅ Regulatory Compliance – Provides structured responses based on CFR 49.
✅ Legal Text Understanding – Trained on transportation regulations for precise interpretations.
✅ Efficient Query Handling – Optimized for answering legal and compliance-related questions.
Model Details
- Model Name:
cfr-49-llm - Base Model:
[Llama 3.2-1B] - Fine-Tuned On: CFR 49 legal texts, transportation compliance documentation.
- Training Method: Supervised fine-tuning on regulatory documents.
- Intended Use: Legal research, compliance checks, transportation law queries.
- Limitations: May not fully capture recent amendments or complex legal interpretations.
Installation & Usage
You can use this model with Hugging Face Transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "one1cat/FineTunes_LLM_CFR_49"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example query
input_text = "What are the labeling requirements for hazardous materials under CFR 49?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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