Add MiniCPM5-2B cookbook (#38295)
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
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zijiexia
co-authored by
Claude Opus 5
Claude Opus 5
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title: MiniCPM-V 4.6
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metatags:
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description: "Deploy OpenBMB MiniCPM-V 4.6 (Qwen3.5-style hybrid GDN backbone + NaViT vision encoder) on NVIDIA GPUs with SGLang — multimodal text + image + video, slicing for high-resolution images."
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tag: NEW
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---
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## 1. Model Introduction
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---
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title: MiniCPM5-2B
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description: "Deploy OpenBMB MiniCPM5-2B with SGLang — a 2.5B dense Llama-architecture on-device model with 131K context, thinking mode, XML tool calling and DSpark speculative decoding on H200, RTX PRO 6000, RTX 5090 and DGX Spark."
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tag: NEW
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---
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## Deployment
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<a id="install" />
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<Accordion title="Install SGLang">
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For all methods and hardware platforms, see the [official SGLang installation guide](../../../docs/get-started/install). The two paths below match the **Python / Docker** toggle in the command panel.
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<Tabs>
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<Tab title="Python (pip / uv)">
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```bash Command
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pip install --upgrade pip
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pip install uv
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uv pip install --prerelease=allow "sglang>=0.5.12"
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```
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Then run the **Python** output of the command panel below in that environment.
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</Tab>
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<Tab title="Docker">
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```bash Command
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docker pull lmsysorg/sglang:dev
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```
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For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). Substitute the inner `sglang serve ...` with what the command generator below produces.
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</Tab>
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</Tabs>
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</Accordion>
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Pick your card to generate the launch command. MiniCPM5-2B is a 2.5B dense model and runs single-GPU at TP=1 on every supported card, so the page ships one operating point per card plus an optional **DSpark** speculative-decoding row.
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import { Deployment } from "/src/snippets/_deployment.jsx";
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import { config } from "/src/snippets/configs/openbmb/minicpm5-2b.jsx";
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import { benchmarks } from "/src/snippets/configs/openbmb/minicpm5-2b-benchmarks.jsx";
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<Deployment config={config} benchmarks={benchmarks} />
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<Note>
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Speed numbers exist for the RTX 5090 and DGX Spark cells — a single card at
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random 1024/1024, recorded as Mean. The H200 and RTX PRO 6000 cells are
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pending measurement, and no accuracy numbers have been taken on any platform
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yet. The DSpark overlay carries no speed numbers on any card.
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</Note>
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## Playground
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The Playground is where you experiment with **SGLang features beyond the recipes above**. The Deploy panel emits this model's documented launch recipes; the Playground lets you turn on additional knobs on top of whichever cell the Deploy panel is currently showing.
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import { Playground } from "/src/snippets/_playground.jsx";
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<Playground config={config} />
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## 1. Model Introduction
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**MiniCPM5-2B** is the second model in OpenBMB's MiniCPM5 series, following MiniCPM5-1B. It is a dense 2B-class Transformer built for on-device assistants, local deployment, coding agents and tool-use workflows — scenarios where a compact model with a small deployment footprint is preferred. OpenBMB reports 2B-class open-source SOTA within its comparison set (average 53.9), with its clearest advantages in code reasoning, math reasoning, long-context understanding, tool use and agentic tasks.
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The checkpoint uses the standard `LlamaForCausalLM` architecture — no custom kernels and no model-code fork — so SGLang loads it through the stock Llama path. It is 2,516,756,480 parameters (1,981,982,720 non-embedding) over 42 layers with GQA (16 query heads, 2 KV heads), and a native context length of 131,072 tokens. Post-training runs SFT → RL → On-Policy Distillation, which merges 16 RL expert models into the single released checkpoint. Weights are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) license.
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<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
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<colgroup>
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<col style={{width: "34%"}} />
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<col style={{width: "22%"}} />
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<col style={{width: "44%"}} />
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</colgroup>
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<thead>
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<tr style={{borderBottom: "2px solid #d55816"}}>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Checkpoint</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Precision</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Role on this page</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><a href="https://huggingface.co/openbmb/MiniCPM5-2B">openbmb/MiniCPM5-2B</a></td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>BF16</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>The served model in every cell above (final release, post-trained with RL + OPD).</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.05)"}}><a href="https://huggingface.co/openbmb/MiniCPM5-2B-DSpark">openbmb/MiniCPM5-2B-DSpark</a></td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>BF16 draft</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Draft model loaded by the <strong>DSPARK</strong> row of the Deploy panel; not served on its own.</td>
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</tr>
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</tbody>
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</table>
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OpenBMB also publishes SFT-only, mid-training and base checkpoints, plus GGUF, MLX and GPTQ-Int4 exports for llama.cpp / Ollama / LM Studio / Apple Silicon. Those target other runtimes and are not part of the SGLang matrix above.
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**Recommended generation:** `temperature=1.0`, `top_p=0.95` (informational — SGLang reads the checkpoint's `generation_config.json`; do not hardcode these in client code).
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**Resources:** [HuggingFace](https://huggingface.co/openbmb/MiniCPM5-2B) · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-2B) · [GitHub](https://github.com/OpenBMB/MiniCPM) · [Tech report](https://arxiv.org/pdf/2506.07900).
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## 2. Advanced Usage
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### 2.1 Thinking Mode
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Thinking is controlled by the chat template's `enable_thinking` flag, passed per request through `chat_template_kwargs`. The `qwen3` reasoning parser — already in every generated command, and toggleable from the **Parsers** card in the [Playground above](#playground) — splits the `<think>` segment into `message.reasoning_content` and leaves the final answer in `message.content`. Drop the flag and the thinking text stays inline in `content`, closing tag and all.
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<Accordion title="Thinking On / Off (Python)">
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```python Example
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
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# Thinking on (chat-template default).
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resp = client.chat.completions.create(
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model="openbmb/MiniCPM5-2B",
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messages=[{"role": "user", "content": "What is 15% of 240?"}],
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extra_body={"chat_template_kwargs": {"enable_thinking": True}},
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)
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msg = resp.choices[0].message
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print("Reasoning:", getattr(msg, "reasoning_content", None))
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print("Answer:", msg.content)
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# Thinking off — a direct answer, no deliberation segment.
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resp = client.chat.completions.create(
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model="openbmb/MiniCPM5-2B",
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messages=[{"role": "user", "content": "What is 15% of 240?"}],
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extra_body={"chat_template_kwargs": {"enable_thinking": False}},
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)
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print("Without thinking:", resp.choices[0].message.content)
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```
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</Accordion>
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<Accordion title="Example Output">
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```text Output
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Reasoning: We are asked: "What is 15% of 240?" This is a simple percentage calculation. To find 15% of 240, we multiply 240 by 15% (which is 0.15). So: 240 × 0.15 = 36. Alternatively, we can think of it as (15/100) × 240 = (15 × 240)/100 = 3600/100 = 36. So the answer is 36.
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We need to respond in a helpful way. The user might be testing or seeking quick answer. Let's provide a clear response with explanation if needed, but since it's straightforward, we can just give the answer directly or briefly explain.
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Answer:
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15% of 240 is **36**.
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To calculate:
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\( 240 \times 0.15 = 36 \)
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Reasoning: None
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Answer: To find 15% of 240, multiply 240 by 15% (which is 0.15):
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\[
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240 \times 0.15 = 36
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\]
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So, 15% of 240 is **36**.
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```
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</Accordion>
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### 2.2 Tool Calling
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MiniCPM5-2B emits XML-style tool calls (`<function name="..."><param name="...">...</param></function>`), and SGLang's built-in `minicpm5` detector converts them to OpenAI-compatible `tool_calls`, parallel calls included. The parser is already in every command the Deploy panel generates; the **Tool Call Parser** chip in the Playground's **Parsers** card is an opt-out, not an opt-in.
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<Accordion title="Tool Calling (Python)">
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```python Example
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
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tools = [{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "Get the current weather for a city.",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {"type": "string", "description": "City name"},
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"date": {"type": "string", "description": "YYYY-MM-DD"},
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},
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"required": ["city"],
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},
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},
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}]
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resp = client.chat.completions.create(
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model="openbmb/MiniCPM5-2B",
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messages=[{"role": "user", "content": "What is the weather in Beijing today?"}],
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tools=tools,
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)
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msg = resp.choices[0].message
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# With the qwen3 reasoning parser on, the thinking segment lands in
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# `reasoning_content` and `content` may be empty on a tool-call turn.
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print("Reasoning:", getattr(msg, "reasoning_content", None))
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print("Content:", msg.content)
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for call in (msg.tool_calls or []):
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print("Tool:", call.function.name, call.function.arguments)
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```
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</Accordion>
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<Accordion title="Example Output">
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```text Output
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Reasoning:
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The user is asking about the weather in Beijing today. I have access to a get_weather function that can help with this. Let me call it to get the current weather for Beijing.
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Content:
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Tool: get_weather {"city": "Beijing"}
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```
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</Accordion>
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