Instructions to use Azmainadeeb/MathGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Azmainadeeb/MathGPT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Azmainadeeb/MathGPT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Azmainadeeb/MathGPT")
model = AutoModelForCausalLM.from_pretrained("Azmainadeeb/MathGPT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Azmainadeeb/MathGPT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Azmainadeeb/MathGPT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azmainadeeb/MathGPT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Azmainadeeb/MathGPT

SGLang

How to use Azmainadeeb/MathGPT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Azmainadeeb/MathGPT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azmainadeeb/MathGPT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Azmainadeeb/MathGPT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azmainadeeb/MathGPT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Azmainadeeb/MathGPT with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Azmainadeeb/MathGPT to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Azmainadeeb/MathGPT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Azmainadeeb/MathGPT to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Azmainadeeb/MathGPT",
    max_seq_length=2048,
)

Docker Model Runner
How to use Azmainadeeb/MathGPT with Docker Model Runner:
```
docker model run hf.co/Azmainadeeb/MathGPT
```

MathGPT / README.md

Azmainadeeb

Update README.md

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	---
	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]))