README.md

---
license: mit
datasets:
- aryan27/geometry-cot
language:
- en
base_model:
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: transformers
---
# **MiniLLM Geometry Engine**

MiniLLM Geometry Engine is a fine-tuned version of the TinyLlama 1.1B
language model, optimized for generating geometry-related functions for
a geometry engine. Fine-tuned on a custom Geometry Chain-of-Thought
(CoT) dataset, this model excels at producing accurate and efficient
mathematical functions for geometric computations, stored in the Hugging
Face .safetensors format for secure and efficient model loading.

## **Model Overview**

-   **Base Model**: TinyLlama 1.1B

-   **Fine-Tuning Dataset**: Custom Geometry CoT dataset consisting of coordinate geometry problems and geometrical construction instruction

-   **Model Format**: .safetensors for secure and efficient weight storage

-   **Intended Use**: Generating geometry functions for educational tools.

-   **License**: MIT

-   **Contact**: Contact via Hugging Face profile

## **Important Note:**

-   Use the system_prompt.txt for the System Prompt, this provides the accurate results.

-   You can extract and edit the system prompt. I hope to add new functions later.

## **Capabilities**

MiniLLM Geometry Engine interprets geometry-related queries and outputs
functional Python code or mathematical expressions. For example, given a
prompt like \"Generate a function to calculate the area of a triangle,\"
it produces executable code or formulas with clear reasoning, leveraging
its CoT fine-tuning for logical accuracy. Supported tasks include area,
volume, distance, and intersection calculations.

## **Technical Details**

-   **Architecture**: Transformer-based, inherited from TinyLlama 1.1B

-   **Fine-Tuning Details**: Trained on a dataset of geometry problems with step-by-step solutions

-   **Output Format**: Python code or pseudocode compatible with geometry engine APIs

-   **Performance**: Improved accuracy on geometry tasks compared to the base TinyLlama model, with low latency

-   **Model Storage**: Uses .safetensors for secure and efficient loading with Hugging Face\'s safetensors library

## **Usage**

To load and use the model with Hugging Face\'s Transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

from safetensors.torch import load_file

\# Load tokenizer and model
```python
tokenizer = AutoTokenizer.from_pretrained("aryan27/geometry-model-hf")

model = AutoModelForCausalLM.from_pretrained("aryan27/geometry-model-hf")
```

\# Example prompt
```python
prompt = "Draw a point A at 3,4"

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(*inputs, max_length=200)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

Install required libraries:
```shell
pip install safetensors transformers
```
## **Training Details**

-   **Dataset**: Custom Geometry CoT dataset with 10,000 geometry problems and solutions

-   **Training Procedure**: Fine-tuned for 3 epochs on a single NVIDIA A100 GPU

-   **Hyperparameters**: Learning rate: 2e-5, batch size: 16

-   **Hardware**: NVIDIA A100 GPU

## **Limitations**

-   Supports very few functions since this was an experimental model, maybe I will add other functions later.

-   Limited to geometry-related tasks and may not generalize to other mathematical domains.

-   Extensive use of strict System Prompting. I aim to eliminate that also.

## **How to Contribute**

Submit issues or pull requests via the Hugging Face repository.
Contributions to the dataset or model improvements are welcome.

## **Acknowledgments**

Thanks to the TinyLlama team for the base model and Hugging Face for the
safetensors and transformers libraries.

MiniLLM Geometry Engine is a lightweight, efficient solution for
developers and researchers needing reliable geometry function
generation, with the security and performance benefits of the
.safetensors format.