---
license: apache-2.0
datasets:
- Akul/alpaca_physics_dataset
base_model:
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
pipeline_tag: text-generation
library_name: mlx
---



Model Name: TinyLlama-Physics

Model Type: Fine-Tuned Llama Model

Base Model: TinyLlama-1.1B-Chat-v1.0

# Model Overview
TinyLlama-Physics is a fine-tuned version of the TinyLlama-1.1B-Chat-v1.0 model, which has been adapted to understand and respond to physics-related questions. This model is designed to answer questions and provide explanations on a variety of topics within the field of physics, including classical mechanics, electromagnetism, thermodynamics, quantum mechanics, and more.

The model was fine-tuned using the MLX library on a dataset of physics-related content to enhance its ability to understand complex scientific concepts and generate accurate, informative responses.

## Key Features
Fine-tuned on physics concepts, making it ideal for academic and educational purposes.
Capable of answering a variety of physics-related questions, from basic to intermediate topics.
Built on the TinyLlama-1.1B-Chat-v1.0 base, which provides a solid foundation for conversational AI.
Model Usage
TinyLlama-Physics can be used to generate responses to physics-related questions in real-time. It leverages the mlx_lm library to load the fine-tuned model and tokenizer for generating accurate and context-aware responses.

## Limitations
The model may not always produce perfect answers, and it may struggle with highly specialized or advanced physics topics.
There are known errors in some of the answers, and further fine-tuning could help improve its accuracy.

### Example Code
This example demonstrates how to use the TinyLlama-Physics model for answering physics-related questions.

```python
# This is an example Python code
from mlx_lm import load, generate

model, tokenizer = load(path_or_hf_repo="sid22669/TinyLlama-Physics")

def generate_prompt(question):
    return f"""### Question:
{question}

### Response:
"""

prompt = generate_prompt("Who is the father of Physics?")
response = generate(model, tokenizer, prompt=prompt)

print(response)
```

## How to Use the Model
Install the required dependencies, including mlx_lm, mlx and transformers libraries.
Load the model from Hugging Face using the load() function with the model's name.
Use the generate() function to pass a physics-related question to the model and receive a generated response.


## Model Fine-Tuning
This model was fine-tuned using the MLX library, with additional custom configurations and datasets focused on physics topics.

## Additional Information
Fine-Tuning Process: The model was fine-tuned using 6 num layers on the TinyLlama base model, with a focus on making it more capable of understanding and responding to questions about physics.
Expected Results: You can expect relatively accurate answers to basic physics questions, though more advanced topics may require additional fine-tuning for better accuracy. Sometimes the model might provide redundant information too.

## How to Cite
If you use this model in your research or projects, please cite it as follows:

@misc{TinyLlama-Physics,
  author = {Siddharth},
  title = {TinyLlama-Physics: A Fine-Tuned Physics Model},
  year = {2025},
  url = {https://huggingface.co/sid22669/TinyLlama-Physics}
}

### Example Use Case
You can use this model in a physics chatbot, a virtual tutor for learning physics, or even in automated question-answering systems focused on educational content.

### More Information
For more details about the fine-tuning process, the datasets used, and potential improvements, feel free to reach out via GitHub or contact the model author directly.