Instructions to use AofaYu71/LatentSkill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AofaYu71/LatentSkill with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AofaYu71/LatentSkill", dtype="auto") - Notebooks
- Google Colab
- Kaggle
LatentSkill Checkpoints
This model repository contains the released checkpoints for LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents.
Code: https://github.com/yuaofan0-oss/LatentSkill
Paper: https://arxiv.org/abs/2606.06087
Dataset repository: https://huggingface.co/datasets/AofaYu71/LatentSkill
Contents
latentskill_pretrain_qwen3_8b/
pretrain.tar.gz
latentskill_sft_qwen3_8b/
train.tar.gz
The checkpoint archives are expected to be extracted under the code repository root:
checkpoints/latentskill_pretrain_qwen3_8b/pretrain/
checkpoints/latentskill_sft_qwen3_8b/train/
Download
From the root of the code repository:
hf download AofaYu71/LatentSkill \
--repo-type model \
--local-dir checkpoints \
--include "latentskill_pretrain_qwen3_8b/pretrain.tar.gz" \
"latentskill_sft_qwen3_8b/train.tar.gz"
tar -xzf checkpoints/latentskill_pretrain_qwen3_8b/pretrain.tar.gz \
-C checkpoints/latentskill_pretrain_qwen3_8b/
tar -xzf checkpoints/latentskill_sft_qwen3_8b/train.tar.gz \
-C checkpoints/latentskill_sft_qwen3_8b/
Intended Use
These checkpoints are intended for reproducing the LatentSkill training and evaluation pipeline described in the paper. They are used with the LatentSkill codebase and the Qwen3-8B backbone.
The checkpoints are not standalone conversational models. Please load them through the project code and follow the paths documented in the GitHub README.
Citation
@article{yu2026latentskillincontexttextualskills,
title={LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents},
author={Aofan Yu and Chenyu Zhou and Tianyi Xu and Zihan Guo and Rong Shan and Zhihui Fu and Jun Wang and Weiwen Liu and Yong Yu and Weinan Zhang and Jianghao Lin},
year={2026},
eprint={2606.06087},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2606.06087},
}