--- title: Youtu-LLM-2B-Base canonical_url: "https://www.modelscope.cn/models/Tencent-YouTu-Research/Youtu-LLM-2B-Base" md_url: "https://www.modelscope.cn/models/Tencent-YouTu-Research/Youtu-LLM-2B-Base.md" repository: Tencent-YouTu-Research/Youtu-LLM-2B-Base chinese_name: Youtu-LLM-2B-Base last_updated: 2026-07-22 license: other pipeline_tag: text-generation tasks: - text-generation model_type: - youtu architectures: - YoutuForCausalLM parameters: 2.0B tensor_type: - BF16 library_name: - safetensors - pytorch frameworks: - Pytorch downloads: 230 stars: 2 --- # Youtu-LLM-2B-Base > Youtu-LLM-2B-Base - Tencent-YouTu-Research 在 ModelScope 开源的模型。📃 License • 💻 Code • 📑 Technical Report • 📊 Benchmarks Tencent-YouTu-Research/Youtu-LLM-2B-Base 是 ModelScope 魔搭社区上的 2.0B 参数text-generation模型，采用 other 许可。 - **Repository**: Tencent-YouTu-Research/Youtu-LLM-2B-Base - **License**: other - **Tasks**: text-generation - **Parameters**: 2.0B - **Downloads**: 230 - **Stars**: 2 - **Last updated**: 2026-07-22 Source: https://www.modelscope.cn/models/Tencent-YouTu-Research/Youtu-LLM-2B-Base ---
# Youtu-LLM Logo [📃 License](LICENSE.txt) • [💻 Code](https://github.com/TencentCloudADP/youtu-tip/tree/master/youtu-llm) • [📑 Technical Report](https://arxiv.org/abs/2512.24618) • [📊 Benchmarks](#benchmarks)
## 🎯 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](https://huggingface.co/tencent/Youtu-LLM-2B-Base) and [Instruct](https://huggingface.co/tencent/Youtu-LLM-2B) - 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](https://huggingface.co/tencent/Youtu-LLM-2B-Base)| | Youtu-LLM-2B | Instruct model of Youtu-LLM-2B | 🤗 [Model](https://huggingface.co/tencent/Youtu-LLM-2B)| | Youtu-LLM-2B-GGUF | Instruct model of Youtu-LLM-2B, in GGUF format | 🤗 [Model](https://huggingface.co/tencent/Youtu-LLM-2B-GGUF)| ## 📰 News - [2026.01.28] You can now directly use Youtu-LLM with [Transformers>=5.1.0](https://github.com/huggingface/transformers/releases/tag/v5.1.0). - [2026.01.07] You can now fine-tuning Youtu-LLM with [ModelScope](https://mp.weixin.qq.com/s/JJtQWSYEjnE7GnPkaJ7UNA). - [2026.01.04] You can now fine-tuning Youtu-LLM with [LlamaFactory](https://github.com/hiyouga/LlamaFactory/pull/9707). > **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](https://huggingface.co/tencent/Youtu-LLM-2B-Base/commit/16ac4cd8ea7edb31386c2470d07c0dbce311ac4b). ## 📊 Performance Comparisons ### Base Model # Comparison between Youtu-LLM-2B-Base and baselines #### General Benchmarks | Type | Benchmark (Metric) | # Shots | Qwen3-1.7B-Base | SmoLM3-3B-Base | Gemma3-4B-Base | Qwen3-4B-Base | Llama3.1-8B | Youtu-LLM-2B-Base | | :--- | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | Commonsense | MMLU-Pro (EM) | 5 | 34.9% | 35.3% | 29.4% | 46.1% | 36.2% | **48.4%** | | | MLQA-Zh (EM) | 3 | 38.1% | 38.0% | 40.3% | **47.2%** | 43.0% | 43.5% | | | MMLU-ProX-Zh (EM) | 5 | 32.5% | 26.7% | 24.2% | **45.2%** | 25.4% | 40.7% | | STEM | GSM8K (EM) | 8 | 68.2% | 67.3% | 38.5% | **80.8%** | 47.8% | 77.6% | | | MGSM-Zh (EM) | 8 | 57.1% | 40.7% | 33.0% | **69.7%** | 35.9% | 68.9% | | | MATH (EM) | 4 | 28.1% | 40.8% | 24.4% | **44.8%** | 21.5% | 44.4% | | | BBH (EM) | 3 | 53.0% | 59.8% | 51.6% | **70.8%** | 62.9% | 59.8% | | | GPQA-MC (Acc. Norm) | 5 | 30.4% | 26.6% | 28.6% | **37.8%** | 30.1% | 33.3% | | | HLE-MC (Acc. Norm) | 3 | 10.7% | 3.1% | 8.0% | 15.0% | 11.5% | **17.4%** | | Coding | MBPP (Pass@1) | 3 | 55.6% | 51.0% | 45.8% | **67.5%** | 49.4% | 66.6% | | | MBPP+ (Pass@1) | 3 | 71.0% | 66.1% | 61.9% | 80.8% | 62.7% | **81.8%** | | | HumanEval (Pass@1) | 0 | 49.9% | 34.8% | 36.6% | 57.6% | 36.0% | **64.6%** | | | HumanEval+ (Pass@1) | 0 | 41.3% | 28.1% | 28.1% | 49.9% | 28.1% | **57.3%** | | | LiveCodeBench v6 (Pass@1) | 3 | 5.1% | 2.9% | 2.9% | 6.9% | 3.4% | **9.7%** | | | CRUXEval (Pass@1) | 1 | 40.6% | 42.1% | 39.7% | 54.8% | 42.3% | **55.9%** | | | RepoBench (EM) | 3 | 21.0% | 21.8% | 23.0% | **25.3%** | 25.2% | 22.7% | | Long Context | LongBench v2 (Acc.) | 3 | 28.0% | **28.8%** | 26.6% | 25.8% | 27.8% | 27.2% | | | NIAH (Acc.) | / | 79.8% | 75.0% | 99.5% | 83.0% | **99.8%** | 98.8% | #### Agentic Benchmarks We takes [APTBench](https://github.com/TencentYoutuResearch/APTBench/) for evaluating the agentic capabilities of base model. | Category | Qwen3-1.7B-Base | SmoLM3-3B-Base | Gemma3-4B-Base | Qwen3-4B-Base | Llama3.1-8B | Youtu-LLM-2B-Base | | :--- | :---: | :---: | :---: | :---: | :---: | :---: | | Code | 25.1% | 24.3% | 32.8% | **41.9%** | 23.6% | 37.9% | | Deep Research | 28.5% | 27.2% | 36.4% | **40.5%** | 30.0% | 38.6% | | Math | 59.9% | 60.7% | 59.8% | **70.5%** | 60.1% | 68.0% | | Tool | 56.7% | 59.1% | 61.7% | **65.8%** | 64.1% | 64.2% | ## 📚 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}, } ```