Instructions to use IFM/MegaMath-Llama-3.2-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IFM/MegaMath-Llama-3.2-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IFM/MegaMath-Llama-3.2-1B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("IFM/MegaMath-Llama-3.2-1B") model = AutoModelForCausalLM.from_pretrained("IFM/MegaMath-Llama-3.2-1B", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IFM/MegaMath-Llama-3.2-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IFM/MegaMath-Llama-3.2-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IFM/MegaMath-Llama-3.2-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IFM/MegaMath-Llama-3.2-1B
- SGLang
How to use IFM/MegaMath-Llama-3.2-1B 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 "IFM/MegaMath-Llama-3.2-1B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IFM/MegaMath-Llama-3.2-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "IFM/MegaMath-Llama-3.2-1B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IFM/MegaMath-Llama-3.2-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IFM/MegaMath-Llama-3.2-1B with Docker Model Runner:
docker model run hf.co/IFM/MegaMath-Llama-3.2-1B
| license: llama3.2 | |
| datasets: | |
| - LLM360/MegaMath | |
| language: | |
| - en | |
| pipeline_tag: text-generation | |
| library_name: transformers | |
| tags: | |
| - math | |
| - code | |
| - cot | |
| - pal | |
| # MegaMath-Llama-3.2-1B | |
| [Arxiv](https://arxiv.org/abs/2504.02807) | [Datasets](https://huggingface.co/datasets/LLM360/MegaMath) | |
| A proof-of-concept model train on [MegaMath](https://huggingface.co/datasets/LLM360/MegaMath) dataset, capable of both Chain-of-Thought and Program-Aided-Language problem solving. | |
|  | |
| ## Performance | |
|  | |
| ## Citation | |
| If you find our work useful, please cite | |
| ```bibtex | |
| @article{zhou2025megamath, | |
| title = {MegaMath: Pushing the Limits of Open Math Corpora}, | |
| author = {Zhou, Fan and Wang, Zengzhi and Ranjan, Nikhil and Cheng, Zhoujun and Tang, Liping and He, Guowei and Liu, Zhengzhong and Xing, Eric P.}, | |
| journal = {arXiv preprint arXiv:2504.02807}, | |
| year = {2025}, | |
| note = {Preprint} | |
| } | |
| ``` |