Jan-nano-gguf
6.4K
141
Q4
license:apache-2.0
by
Menlo
Language Model
OTHER
New
6K downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
Unknown
Mobile
Laptop
Server
Quick Summary
Jan Nano is a fine-tuned language model built on top of the Qwen3 architecture.
Code Examples
Recommended Sampling Parametersbibtex
@misc{dao2025jannanotechnicalreport,
title={Jan-nano Technical Report},
author={Alan Dao and Dinh Bach Vu},
year={2025},
eprint={2506.22760},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.22760},
}Recommended Sampling Parametersbibtex
@misc{dao2025jannanotechnicalreport,
title={Jan-nano Technical Report},
author={Alan Dao and Dinh Bach Vu},
year={2025},
eprint={2506.22760},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.22760},
}Recommended Sampling Parametersbibtex
@misc{dao2025jannanotechnicalreport,
title={Jan-nano Technical Report},
author={Alan Dao and Dinh Bach Vu},
year={2025},
eprint={2506.22760},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.22760},
}Recommended Sampling Parametersbibtex
@misc{dao2025jannanotechnicalreport,
title={Jan-nano Technical Report},
author={Alan Dao and Dinh Bach Vu},
year={2025},
eprint={2506.22760},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.22760},
}Recommended Sampling Parametersbibtex
@misc{dao2025jannanotechnicalreport,
title={Jan-nano Technical Report},
author={Alan Dao and Dinh Bach Vu},
year={2025},
eprint={2506.22760},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.22760},
}Recommended Sampling Parametersbibtex
@misc{dao2025jannanotechnicalreport,
title={Jan-nano Technical Report},
author={Alan Dao and Dinh Bach Vu},
year={2025},
eprint={2506.22760},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.22760},
}Recommended Sampling Parametersbibtex
@misc{dao2025jannanotechnicalreport,
title={Jan-nano Technical Report},
author={Alan Dao and Dinh Bach Vu},
year={2025},
eprint={2506.22760},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.22760},
}Recommended Sampling Parametersbibtex
@misc{dao2025jannanotechnicalreport,
title={Jan-nano Technical Report},
author={Alan Dao and Dinh Bach Vu},
year={2025},
eprint={2506.22760},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.22760},
}Recommended Sampling Parametersbibtex
@misc{dao2025jannanotechnicalreport,
title={Jan-nano Technical Report},
author={Alan Dao and Dinh Bach Vu},
year={2025},
eprint={2506.22760},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.22760},
}Deploy This Model
Production-ready deployment in minutes
Together.ai
Instant API access to this model
Production-ready inference API. Start free, scale to millions.
Try Free APIReplicate
One-click model deployment
Run models in the cloud with simple API. No DevOps required.
Deploy NowDisclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.