Instructions to use YusenWithR/PhaGen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YusenWithR/PhaGen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="YusenWithR/PhaGen", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("YusenWithR/PhaGen", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download NOTICE.md from YusenWithR/PhaGen: direct link, hf CLI and curl.
- Browser
- Download file 2.57 kB
-
https://huggingface.co/YusenWithR/PhaGen/resolve/main/NOTICE.md
- Command line
-
hf download hf://YusenWithR/PhaGen/NOTICE.md
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curl -L -o NOTICE.md https://huggingface.co/YusenWithR/PhaGen/resolve/main/NOTICE.md
Attribution and license scope
PhaGen Copyright 2026 The PhaGen authors
Licensed under the Apache License, Version 2.0 (see LICENSE).
Third-party code in this repository
modeling_phagen.py
Adapts the MEGABYTE implementation by Phil Wang (https://github.com/lucidrains/MEGABYTE-pytorch). The multi-stage hierarchical transformer, its helper utilities and the per-stage start tokens come from that implementation. The DNA configuration, the masked-diffusion integration and the Transformers model class are added here.
MIT License
Copyright (c) 2023 Phil Wang
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
generation_utils.py
Adapts the masked-diffusion generation utilities from Open-dLLM (https://github.com/pengzhangzhi/Open-dLLM), licensed under the Apache License, Version 2.0. The mixin in that file records further lineage from the Dream model's generation utilities.
Training framework
The paper model was trained with VeOmni (https://github.com/ByteDance-Seed/VeOmni, Apache-2.0). The training framework is not part of this model repository.
Scope of the Apache-2.0 grant
LICENSE covers the code and configuration authored for this repository, and applies
alongside the upstream terms above for the adapted files. It makes no license claim over
the biological datasets used for training or evaluation, which keep their own terms, and
it does not relicense any third-party source.
Weights
The weights are the frozen paper artifact, released for research use together with the code in this repository.