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tencent/Youtu-LLM-2B-Base

sourceHugging Faceotherupdated 7mo agoView on Hugging Face
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Model Card

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<img src="assets/youtu-llm-logo.png" alt="Youtu-LLM Logo" height="100px">

๐Ÿ“ƒ License โ€ข ๐Ÿ’ป Code โ€ข ๐Ÿ“‘ Technical Report โ€ข ๐Ÿ“Š Benchmarks

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๐ŸŽฏ 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:

  • โ€”Type: Autoregressive Causal Language Models with Dense MLA
  • โ€”Release versions: Base and Instruct
  • โ€”Number of Parameters: 1.96B
  • โ€”Number of Layers: 32
  • โ€”Number of Attention Heads (MLA): 16 for Q/K/V
  • โ€”MLA Rank: 1,536 for Q, 512 for K/V
  • โ€”MLA Dim: 128 for QK Nope, 64 for QK Rope, and 128 for V
  • โ€”Context Length: 131,072
  • โ€”Vocabulary Size: 128,256

๐Ÿค— Model Download

Model NameDescriptionDownload
Youtu-LLM-2B-BaseBase model of Youtu-LLM-2B๐Ÿค— Model
Youtu-LLM-2BInstruct model of Youtu-LLM-2B๐Ÿค— Model
Youtu-LLM-2B-GGUFInstruct model of Youtu-LLM-2B, in GGUF format๐Ÿค— Model

๐Ÿ“ฐ News

  • โ€”[2026.01.28] You can now directly use Youtu-LLM with Transformers>=5.1.0.
  • โ€”[2026.01.07] You can now fine-tuning Youtu-LLM with ModelScope.
  • โ€”[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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๐Ÿ“Š Performance Comparisons

Base Model

<img src="assets/generalagenticbase.png" alt="Comparison between Youtu-LLM-2B-Base and baselines" height="260px">

General Benchmarks
TypeBenchmark (Metric)# ShotsQwen3-1.7B-BaseSmoLM3-3B-BaseGemma3-4B-BaseQwen3-4B-BaseLlama3.1-8BYoutu-LLM-2B-Base
CommonsenseMMLU-Pro (EM)534.9%35.3%29.4%<u>46.1%</u>36.2%48.4%
MLQA-Zh (EM)338.1%38.0%40.3%47.2%43.0%<u>43.5%</u>
MMLU-ProX-Zh (EM)532.5%26.7%24.2%45.2%25.4%<u>40.7%</u>
STEMGSM8K (EM)868.2%67.3%38.5%80.8%47.8%<u>77.6%</u>
MGSM-Zh (EM)857.1%40.7%33.0%69.7%35.9%<u>68.9%</u>
MATH (EM)428.1%40.8%24.4%44.8%21.5%<u>44.4%</u>
BBH (EM)353.0%59.8%51.6%70.8%<u>62.9%</u>59.8%
GPQA-MC (Acc. Norm)530.4%26.6%28.6%37.8%30.1%<u>33.3%</u>
HLE-MC (Acc. Norm)310.7%3.1%8.0%<u>15.0%</u>11.5%17.4%
CodingMBPP (Pass@1)355.6%51.0%45.8%67.5%49.4%<u>66.6%</u>
MBPP+ (Pass@1)371.0%66.1%61.9%<u>80.8%</u>62.7%81.8%
HumanEval (Pass@1)049.9%34.8%36.6%<u>57.6%</u>36.0%64.6%
HumanEval+ (Pass@1)041.3%28.1%28.1%<u>49.9%</u>28.1%57.3%
LiveCodeBench v6 (Pass@1)35.1%2.9%2.9%<u>6.9%</u>3.4%9.7%
CRUXEval (Pass@1)140.6%42.1%39.7%<u>54.8%</u>42.3%55.9%
RepoBench (EM)321.0%21.8%23.0%25.3%<u>25.2%</u>22.7%
Long ContextLongBench v2 (Acc.)3<u>28.0%</u>28.8%26.6%25.8%27.8%27.2%
NIAH (Acc.)/79.8%75.0%<u>99.5%</u>83.0%99.8%98.8%
Agentic Benchmarks

We takes APTBench for evaluating the agentic capabilities of base model.

CategoryQwen3-1.7B-BaseSmoLM3-3B-BaseGemma3-4B-BaseQwen3-4B-BaseLlama3.1-8BYoutu-LLM-2B-Base
Code25.1%24.3%32.8%41.9%23.6%<u>37.9%</u>
Deep Research28.5%27.2%36.4%40.5%30.0%<u>38.6%</u>
Math59.9%60.7%59.8%70.5%60.1%<u>68.0%</u>
Tool56.7%59.1%61.7%65.8%64.1%<u>64.2%</u>

๐Ÿ“š Citation

If you find our work useful in your research, please consider citing the following paper:

bibtex
@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}, 
}