🎯 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

🤗 Model Download

Model Name	Description	Download
Youtu-LLM-2B-Base	Base model of Youtu-LLM-2B	🤗 Model
Youtu-LLM-2B	Instruct model of Youtu-LLM-2B	🤗 Model
Youtu-LLM-2B-GGUF	Instruct model of Youtu-LLM-2B, in GGUF format	🤗 Model

📊 Performance Comparisons

Instruct Model

General Benchmarks

Benchmark	DeepSeek-R1-Distill-Qwen-1.5B	Qwen3-1.7B	SmolLM3-3B	Qwen3-4B	DeepSeek-R1-Distill-Llama-8B	Youtu-LLM-2B
Commonsense Knowledge Reasoning
MMLU-Redux	53.0%	74.1%	75.6%	83.8%	78.1%	75.8%
MMLU-Pro	36.5%	54.9%	53.0%	69.1%	57.5%	61.6%
Instruction Following & Text Reasoning
IFEval	29.4%	70.4%	60.4%	83.6%	34.6%	81.2%
DROP	41.3%	72.5%	72.0%	82.9%	73.1%	86.7%
MUSR	43.8%	56.6%	54.1%	60.5%	59.7%	57.4%
STEM
MATH-500	84.8%	89.8%	91.8%	95.0%	90.8%	93.7%
AIME 24	30.2%	44.2%	46.7%	73.3%	52.5%	65.4%
AIME 25	23.1%	37.1%	34.2%	64.2%	34.4%	49.8%
GPQA-Diamond	33.6%	36.9%	43.8%	55.2%	45.5%	48.0%
BBH	31.0%	69.1%	76.3%	87.8%	77.8%	77.5%
Coding
HumanEval	64.0%	84.8%	79.9%	95.4%	88.1%	95.9%
HumanEval+	59.5%	76.2%	74.7%	87.8%	82.5%	89.0%
MBPP	51.5%	80.5%	66.7%	92.3%	73.9%	85.0%
MBPP+	44.2%	67.7%	56.7%	77.6%	61.0%	71.7%
LiveCodeBench v6	19.8%	30.7%	30.8%	48.5%	36.8%	43.7%

Agentic Benchmarks

Benchmark	Qwen3-1.7B	SmolLM3-3B	Qwen3-4B	Youtu-LLM-2B
Deep Research
GAIA	11.4%	11.7%	25.5%	33.9%
xbench	11.7%	13.9%	18.4%	19.5%
Code
SWE-Bench-Verified	0.6%	7.2%	5.7%	17.7%
EnConda-Bench	10.8%	3.5%	16.1%	21.5%
Tool
BFCL V3	55.5%	31.5%	61.7%	58.0%
τ²-Bench	2.6%	9.7%	10.9%	15.0%

🚀 Quick Start

This guide will help you quickly deploy and invoke the Youtu-LLM-2B model. This model supports "Reasoning Mode", enabling it to generate higher-quality responses through Chain of Thought (CoT).

Server Example

Enable Reasoning Mode (default):

./llama-server -m Youtu-LLM-2B-F16.gguf \
  --port 8080 \
  --host 0.0.0.0

Disable Reasoning Mode:

./llama-server -m Youtu-LLM-2B-F16.gguf \
  --port 8080 \
  --host 0.0.0.0 \
  --reasoning-budget 0

Key Configuration Details

Reasoning Mode Toggle

Controlled via the --reasoning-budget parameter:

Default (no flag): Enables Chain of Thought; ideal for complex logic and reasoning tasks. Response includes reasoning_content field.
--reasoning-budget 0: Disables reasoning; faster response time, suitable for simple conversations.

Recommended Decoding Parameters

Parameter	Reasoning Mode	Normal Mode
`temperature`	1.0 (Maintains creativity)	0.7 (More stable results)
`top_p`	0.95	0.8
`top_k`	20	20
`repetition_penalty`	1.05	-

Tip: When using Reasoning Mode, a higher temperature helps the model perform deeper, more divergent thinking.

📚 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}, 
}