Feature Extraction
Transformers
Safetensors
English
phagen
biology
dna
genomics
bacteriophage
diffusion
masked-diffusion
custom-code
custom_code
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
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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
2.57 kB
| # 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. | |