README.md

---
library_name: transformers
tags:
- trl
- sft
datasets:
- entfane/Mixture-Of-Thoughts-Math-No-COT
language:
- en
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
pipeline_tag: text-generation
---

<img src="https://huggingface.co/entfane/math_genious-7B/resolve/main/math-genious.png" width="400" height="400"/>

# Math Genius 7B

This model is a Math Chain-of-Thought fine-tuned version of Mistral 7B v0.3 Instruct model.


### Fine-tuning dataset

Model was fine-tuned on [entfane/Mixture-Of-Thoughts-Math-No-COT](https://huggingface.co/datasets/entfane/Mixture-Of-Thoughts-Math-No-COT) math dataset.

### Inference

```python
!pip install transformers accelerate

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "entfane/math-genius-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
messages = [
    {"role": "user", "content": "What's the derivative of 2x^2?"}
]
input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
encoded_input = tokenizer(input, return_tensors = "pt").to(model.device)
output = model.generate(**encoded_input, max_new_tokens=1024)
print(tokenizer.decode(output[0], skip_special_tokens=False))
```

### Evaluation
#### MathQA
The model was evaluated on a randomly sampled subset of 1,000 records from the test split of the [Math-QA](https://huggingface.co/datasets/rvv-karma/Math-QA) dataset.
Math Genius 7B achieved an accuracy of 93.1% in producing the correct final answer under the pass@1 evaluation metric.

#### AIME
Math Genius 7B was evaluated on [90 problems from AIME 22, AIME 23, and AIME 24](https://huggingface.co/datasets/AI-MO/aimo-validation-aime).
The model has successfully solved 3/90 of the problems.