File size: 2,907 Bytes
db2a9b9 a52da4d db2a9b9 d539efa db2a9b9 d539efa a52da4d db2a9b9 a52da4d db2a9b9 a52da4d db2a9b9 d539efa a52da4d d539efa a52da4d db2a9b9 d539efa a52da4d d539efa a52da4d d539efa a52da4d d539efa a52da4d d539efa a52da4d d539efa a52da4d d539efa a52da4d d539efa a52da4d d539efa a52da4d d539efa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | ---
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])) |