---
library_name: transformers
tags: [retrieval-augmented-generation, finetuning, llm, huggingface]
---

# Model Card for Finetuned Llama 3.2 (ROS Query System)

This model is a finetuned version of Llama 3.2 specifically designed to answer questions related to the Robot Operating System (ROS). It was finetuned on Kaggle using domain-specific data scraped from GitHub repositories and Medium articles. The model powers a Retrieval-Augmented Generation (RAG) pipeline in our AI final project.

---

## Model Details

### Model Description

- **Developed by:** Krish Murjani (netid: km6520) & Shresth Kapoor (netid: sk11677)
- **Project Name:** CS-GY-6613 AI Final Project: ROS Query System
- **Finetuned From:** `sentence-transformers/all-MiniLM-L6-v2`
- **Language(s):** English
- **License:** Apache 2.0

---

### Model Sources

- **Repository:** [GitHub Repository](https://github.com/krishmurjani/cs-gy-6613-final-project)

---

## Uses

### Direct Use

The model is used in a Retrieval-Augmented Generation (RAG) pipeline for answering questions related to the Robot Operating System (ROS). It integrates with a vector search engine (Qdrant) and MongoDB for efficient retrieval and query response generation.

### Downstream Use

The model can be extended for other technical domains through additional finetuning or plug-in integration into larger AI systems.

### Out-of-Scope Use

The model is not designed for tasks outside of technical documentation retrieval and answering ROS-related queries.

---

## Bias, Risks, and Limitations

- **Bias:** The model may reflect biases inherent in the scraped ROS documentation and articles.
- **Limitations:** Responses are limited to the scraped and finetuned dataset. It may not generalize to broader queries.

### Recommendations

- Use the model for educational and research purposes in robotics and ROS-specific domains.
- Avoid using the model in high-stakes applications where critical decisions rely on the accuracy of generated responses.

---

## How to Get Started with the Model

```python
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("your-model-id")
tokenizer = AutoTokenizer.from_pretrained("your-model-id")

input_text = "How can I navigate to a specific pose using ROS?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model(**inputs)
print(outputs)
```

## Training Details

### Training Data

- **Sources:** 
  - GitHub repositories related to the Robot Operating System (ROS).
  - Medium articles discussing ROS topics.

### Training Procedure

- **Preprocessing:** 
  - Data cleaning, text chunking, and embedding using Sentence-BERT (`all-MiniLM-L6-v2`).
  - Used ClearML orchestrator for ETL and finetuning pipelines.

- **Training Framework:** 
  - Hugging Face Transformers, PyTorch

- **Training Regime:** 
  - fp16 mixed precision (for efficiency and memory optimization)

---

## Evaluation

### Testing Data

- **Dataset:** 
  - Internal evaluation dataset created from project-specific queries and generated question-answer pairs.

### Factors & Metrics

- **Metrics:** 
  - Query relevance, answer accuracy, and completeness.

- **Evaluation Results:** 
  - Achieved high relevance and precision for domain-specific questions related to ROS.

---

## Environmental Impact

- **Hardware Type:** 
  - NVIDIA Tesla T4 (Kaggle)

- **Hours Used:** 
  - Approximately 15-20 hours of training

- **Compute Region:** 
  - US Central (Kaggle Cloud)

- **Carbon Emitted:** 
  - Estimated using the [Machine Learning Impact Calculator](https://mlco2.github.io/impact#compute).

---

## Technical Specifications

- **Model Architecture:** 
  - Transformer-based language model (Llama 3.2)

- **Compute Infrastructure:** 
  - Kaggle Cloud with NVIDIA Tesla T4 GPUs

- **Frameworks:** 
  - Hugging Face Transformers, PyTorch, ClearML

---

## Citation

```bibtex
@misc{kapoor2024rosquery,
  title={ROS Query System: A Retrieval-Augmented Generation Pipeline},
  author={Shresth Kapoor and Krish Murjani},
  year={2024},
  note={CS-GY-6613 AI Final Project, NYU Tandon School of Engineering}
}
```

## Model Card Authors
- Krish Murjani ([krishmurjani](https://huggingface.co/krishmurjani))
- Shresth Kapoor ([shresthkapoor7](https://huggingface.co/shresthkapoor7))
	

## Model Card Contact

For any inquiries, please contact us through our ([GitHub Repository](https://github.com/krishmurjani/cs-gy-6613-final-project)).