Online Tutorial | 2B Outperforms 4B, SOTA Among Same-Level Open-Source Models, OpenBMB Launches MiniCPM5-2B With 131K Long Context and Tool Calling

From Q&A and coding to tool calling, the capabilities of AI assistants continue to expand. How to balance reasoning, long-text processing, and agent applications under limited resources has become a key issue for on-device deployment.

MiniCPM5-2B offers a lightweight option. As the second model in the MiniCPM5 series launched by OpenBMB, it adopts the standard LlamaForCausalLM architecture, targeting on-device deployment, local inference, and resource-constrained scenarios, supporting developers in building practical AI applications around small-parameter models.

According to official evaluations, MiniCPM5-2B achieves an average score of 53.9 within the listed comparison range, reaching a leading level among 2B-class open-source models, with some metrics surpassing 4B-class models. Its capabilities cover code reasoning, mathematical reasoning, long-context understanding, tool calling, and agent tasks, making it suitable for programming assistance, problem-solving, and knowledge Q&A scenarios. For long-text processing needs, MiniCPM5-2B natively supports a context window of up to 131,072 tokens, providing ample space for inputting lengthy documents, code, and conversation records.

Beyond text generation, MiniCPM5-2B also supports XML-style tool calling, serving as a model foundation for programming agents, search agents, and other tool-calling workflows.

Currently, the HyperAI tutorial section has launched the practical tutorial "MiniCPM5-2B: On-Device 2B-Class SOTA Large Model Inference," supporting online experience and one-click deployment~

Run Online:

https://go.hyper.ai/q0Pc2

More Online Tutorials:

https://hyper.ai/notebooks

Demo page

Demo Run

1. After entering the hyper.ai homepage, select the "Tutorials" page, or click "View More Tutorials," choose "MiniCPM5-2B: On-Device 2B-Class SOTA Large Model Inference," and click "Run This Tutorial."

2. After the page redirects, click "Clone" in the top-right corner to clone the tutorial into your own container.

Note: The top-right corner of the page supports language switching, currently offering both Chinese and English. This tutorial article uses English as an example for the steps.

3. Select "NVIDIA RTX 5090" and the "vllm" image, then click "Continue job execution."

4. Wait for resource allocation. Once the status changes to "Running," click "Open Workspace" to enter the Jupyter Workspace.

Results Display

1. After the page redirects, click the README file on the left, then click "Run" at the top after entering.

2. After the run completes, open a new terminal and execute the following command to start Open WebUI.

3. After startup, click the API address on the right to access the Open WebUI interface in your browser and start chatting with the local model.