tencent/Youtu-LLM-2B-Base
432.3k
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<img src="assets/youtu-llm-logo.png" alt="Youtu-LLM Logo" height="100px">
๐ License โข ๐ป Code โข ๐ Technical Report โข ๐ Benchmarks
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๐ฏ Brief Introduction
Youtu-LLM is a new, small, yet powerful LLM, contains only 1.96B parameters, supports 128k long context, and has native agentic talents. On general evaluations, Youtu-LLM significantly outperforms SOTA LLMs of similar size in terms of Commonsense, STEM, Coding and Long Context capabilities; in agent-related testing, Youtu-LLM surpasses larger-sized leaders and is truly capable of completing multiple end2end agent tasks.
Youtu-LLM has the following features:
- Type: Autoregressive Causal Language Models with Dense MLA
- Release versions: Base and Instruct
- Number of Parameters: 1.96B
- Number of Layers: 32
- Number of Attention Heads (MLA): 16 for Q/K/V
- MLA Rank: 1,536 for Q, 512 for K/V
- MLA Dim: 128 for QK Nope, 64 for QK Rope, and 128 for V
- Context Length: 131,072
- Vocabulary Size: 128,256
๐ค Model Download
๐ฐ News
- [2026.01.28] You can now directly use Youtu-LLM with Transformers>=5.1.0.
- [2026.01.07] You can now fine-tuning Youtu-LLM with ModelScope.
- [2026.01.04] You can now fine-tuning Youtu-LLM with LlamaFactory.
Note: If you wish to use Youtu-LLM-2B-Base based on earlier versions of transformers (>=4.56.0,<=4.57.1), please make sure to download the model repository before this commit.
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๐ Performance Comparisons
Base Model
<img src="assets/generalagenticbase.png" alt="Comparison between Youtu-LLM-2B-Base and baselines" height="260px">
General Benchmarks
Agentic Benchmarks
We takes APTBench for evaluating the agentic capabilities of base model.
๐ Citation
If you find our work useful in your research, please consider citing the following paper:
@article{youtu-llm,
title={Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models},
author={Tencent Youtu Lab},
year={2025},
eprint={2512.24618},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2512.24618},
}