Text Generation
Transformers
hyperlm_qwen3
hypergraph
large-language-models
qwen3
projector
hyper-align
hypergraph-as-language
Instructions to use MengqiLei/hyper-align with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MengqiLei/hyper-align with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MengqiLei/hyper-align")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MengqiLei/hyper-align", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MengqiLei/hyper-align with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MengqiLei/hyper-align" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MengqiLei/hyper-align", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MengqiLei/hyper-align
- SGLang
How to use MengqiLei/hyper-align 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 "MengqiLei/hyper-align" \ --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": "MengqiLei/hyper-align", "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 "MengqiLei/hyper-align" \ --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": "MengqiLei/hyper-align", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MengqiLei/hyper-align with Docker Model Runner:
docker model run hf.co/MengqiLei/hyper-align
| license: apache-2.0 | |
| library_name: transformers | |
| base_model: Qwen/Qwen3-8B | |
| tags: | |
| - hypergraph | |
| - large-language-models | |
| - qwen3 | |
| - projector | |
| - hyper-align | |
| - hypergraph-as-language | |
| - arxiv:2605.21858 | |
| inference: false | |
| # Hyper-Align | |
| This repository contains the released **Hyper-Align** projector checkpoint for the paper **Hypergraph as Language**. | |
| Hyper-Align is a hypergraph-native alignment framework that makes high-order association structures directly consumable by a frozen large language model. This checkpoint uses the **HIDT-O** hypergraph serialization protocol and the **HIP** projector to map hypergraph incidence information into the token space of **Qwen/Qwen3-8B**. | |
| > This repository does not include the base LLM or text encoder weights. Users must download those models from their official Hugging Face repositories. | |
| ## Files | |
| ```text | |
| config.json | |
| mm_projector.bin | |
| ``` | |
| - `config.json` defines the Hyper-Align wrapper and HIP projector configuration. | |
| - `mm_projector.bin` contains the trained projector weights. | |
| Keep both files in the same checkpoint directory. The evaluation code uses `config.json` to instantiate the Hyper-Align model wrapper and projector before loading `mm_projector.bin`. | |
| ## License | |
| The Hyper-Align code and released projector checkpoint are distributed under the Apache License 2.0. | |
| The base LLM and embedding model are not redistributed here. Users must comply with the licenses and terms of the corresponding upstream model repositories: | |
| - `Qwen/Qwen3-8B` | |
| - `Qwen/Qwen3-Embedding-0.6B` | |
| ## Citation | |
| If you use this checkpoint, please cite: | |
| ```bibtex | |
| @article{lei2026hypergraph, | |
| title={Hypergraph as Language}, | |
| author={Lei, Mengqi and Xie, Guohuan and Ying, Shihui and Du, Shaoyi and Yong, Jun-Hai and Li, Siqi and Gao, Yue}, | |
| journal={arXiv preprint arXiv:2605.21858}, | |
| year={2026} | |
| } | |
| ``` | |
| ## Links | |
| - Paper: https://arxiv.org/abs/2605.21858 | |
| - Code: https://github.com/Mengqi-Lei/Hypergraph-as-Language | |