README.md

---
base_model: unsloth/gpt-oss-120b-unsloth-bnb-4bit
repo_name: Azmainadeeb/MathGPT
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
- mathematics
- olympiad-math
- reasoning
- chain-of-thought
license: apache-2.0
language:
- en
datasets:
- HuggingFaceH4/Multilingual-Thinking
- Goedel-LM/MathOlympiadBench
- hf-imo-colab/olympiads-ref-base-math-word
- alejopaullier/aimo-external-dataset
- imbishal7/math-olympiad-problems-and-solutions-aops
- baidalinadilzhan/problems-and-solutions-interantional-phos
- kishanvavdara/aimo-olympiadbench-math-dataset
---

# MathGPT (GPT-OSS-120B-Olympiad)

**MathGPT** is a high-performance reasoning model fine-tuned from **GPT-OSS 120B**. It is engineered specifically for solving complex mathematical theorems, competition-level problems (AIME/IMO), and advanced scientific reasoning.

- **Developed by:** Azmainadeeb
- **Model Type:** Causal Language Model (Fine-tuned for Mathematical Reasoning)
- **Base Model:** unsloth/gpt-oss-120b-unsloth-bnb-4bit
- **Training Framework:** Unsloth + TRL

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

## 🧩 Model Architecture
MathGPT leverages the **Mixture-of-Experts (MoE)** architecture of the GPT-OSS family, utilizing 117B total parameters with 5.1B active parameters per token. This allows the model to maintain state-of-the-art reasoning depth while remaining computationally efficient during inference.



## 📚 Training Data
The model was trained on a massive synthesis of reasoning-dense datasets to ensure "Chain of Thought" consistency:

### Primary Thinking Dataset
* **[Multilingual-Thinking](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking):** Instills the core "Thinking" trace and multi-step internal monologue.

### Olympiad & Competition Sets
* **OlympiadBench & MathOlympiadBench:** High-difficulty benchmark problems.
* **IMO Math Boxed:** Problems curated from the International Mathematical Olympiad.
* **AoPS (Art of Problem Solving):** Diverse competition-style math problems.
* **AIMO External Data:** Specific sets designed for the AI Mathematical Olympiad.

## 🚀 Quickstart Usage

```python
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Azmainadeeb/MathGPT",
    max_seq_length = 4096,
    load_in_4bit = True,
)

messages = [
    {"role": "user", "content": "Find all real numbers x such that 8^x + 2^x = 130."}
]

# Apply the template with reasoning_effort to trigger the "Thinking" mode
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt = True,
    reasoning_effort = "medium", # Options: low, medium, high
    return_tensors = "pt"
).to("cuda")

outputs = model.generate(inputs, max_new_tokens = 1024)
print(tokenizer.decode(outputs[0]))