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--- |
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license: apache-2.0 |
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datasets: |
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- Akul/alpaca_physics_dataset |
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base_model: |
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- TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
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pipeline_tag: text-generation |
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library_name: mlx |
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--- |
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Model Name: TinyLlama-Physics |
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Model Type: Fine-Tuned Llama Model |
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Base Model: TinyLlama-1.1B-Chat-v1.0 |
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# Model Overview |
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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. |
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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. |
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## Key Features |
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Fine-tuned on physics concepts, making it ideal for academic and educational purposes. |
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Capable of answering a variety of physics-related questions, from basic to intermediate topics. |
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Built on the TinyLlama-1.1B-Chat-v1.0 base, which provides a solid foundation for conversational AI. |
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Model Usage |
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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. |
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## Limitations |
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The model may not always produce perfect answers, and it may struggle with highly specialized or advanced physics topics. |
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There are known errors in some of the answers, and further fine-tuning could help improve its accuracy. |
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### Example Code |
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This example demonstrates how to use the TinyLlama-Physics model for answering physics-related questions. |
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```python |
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# This is an example Python code |
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from mlx_lm import load, generate |
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model, tokenizer = load(path_or_hf_repo="sid22669/TinyLlama-Physics") |
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def generate_prompt(question): |
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return f"""### Question: |
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{question} |
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### Response: |
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""" |
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prompt = generate_prompt("Who is the father of Physics?") |
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response = generate(model, tokenizer, prompt=prompt) |
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print(response) |
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``` |
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## How to Use the Model |
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Install the required dependencies, including mlx_lm, mlx and transformers libraries. |
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Load the model from Hugging Face using the load() function with the model's name. |
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Use the generate() function to pass a physics-related question to the model and receive a generated response. |
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## Model Fine-Tuning |
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This model was fine-tuned using the MLX library, with additional custom configurations and datasets focused on physics topics. |
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## Additional Information |
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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. |
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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. |
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## How to Cite |
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If you use this model in your research or projects, please cite it as follows: |
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@misc{TinyLlama-Physics, |
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author = {Siddharth}, |
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title = {TinyLlama-Physics: A Fine-Tuned Physics Model}, |
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year = {2025}, |
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url = {https://huggingface.co/sid22669/TinyLlama-Physics} |
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} |
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### Example Use Case |
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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. |
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### More Information |
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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. |
