---
language: "en"
license: "mit"
tags:
  - cfr49
  - compliance
  - transportation
  - legal
  - regulatory
  - fine-tune
  - text-generation
library_name: "transformers"
datasets:
  - "custom-cfr49-dataset"
base_model: "meta-llama/Llama-3-1B" 
pipeline_tag: "text-generation" 
---



# **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**:

```python
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))