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[Docs] Update MiniCPM-V-4.6 documentation and deployment configuration (#24991)

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Yihao Wang
2026-05-11 11:10:19 -07:00
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docs_new/cookbook/autoregressive/OpenBMB/MiniCPM-V-4_6.mdx
+66 -28
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@@ -5,14 +5,15 @@ metatags:
tag: NEW
---
<Note>
The public MiniCPM-V 4.6 release weights are not yet on HuggingFace; benchmark numbers below were captured during SGLang port verification on an internal test checkpoint and will be re-run once the public weights drop. The License field is also pending verification against the public model card.
</Note>
## 1. Model Introduction
MiniCPM-V 4.6 is the next-generation multimodal model from [OpenBMB](https://huggingface.co/openbmb), the team behind the MiniCPM-V series. The model combines a **Qwen3.5-style hybrid LLM backbone** (Gated Delta Net + full attention) with a **NaViT-packed vision encoder** that handles arbitrary aspect ratios and high-resolution slicing natively, plus end-to-end video support.
OpenBMB ships two variants on HuggingFace:
- [`openbmb/MiniCPM-V-4.6`](https://huggingface.co/openbmb/MiniCPM-V-4.6) — base instruct model. Use this for general multimodal serving; thinking mode is still available per-request via `chat_template_kwargs.enable_thinking=true`.
- [`openbmb/MiniCPM-V-4.6-Thinking`](https://huggingface.co/openbmb/MiniCPM-V-4.6-Thinking) — thinking-tuned variant with stronger chain-of-thought behavior. Pair with the same `--reasoning-parser qwen3` flag.
**Key Features:**
- **Hybrid LLM backbone**: Qwen3.5-style mix of Gated Delta Net (linear-attention) layers and full-attention layers, providing long-context efficiency without giving up modeling power.
@@ -20,9 +21,9 @@ MiniCPM-V 4.6 is the next-generation multimodal model from [OpenBMB](https://hug
- **High-resolution slicing**: Source image plus a configurable grid of slice tiles (up to 9 tiles in the open test variant) lets the model reason over fine detail in 1280×720+ images.
- **Video**: Frame-by-frame multi-modal data items routed through the same vision encoder; any number of frames per request.
- **Reasoning Parser**: switchable thinking mode (Qwen3.5 lineage), exposed via `chat_template_kwargs.enable_thinking` per request and SGLang's `--reasoning-parser qwen3` on the server side.
- **Tool Calling**: Qwen 2.5–style `<tool_call>` JSON format, surfaced as OpenAI-compatible `message.tool_calls` via SGLang's `--tool-call-parser qwen`. Composes with thinking mode and with image / video inputs.
- **Tool Calling**: Qwen3.5-style `<tool_call><function=…><parameter=…>…</parameter></function></tool_call>` XML format, surfaced as OpenAI-compatible `message.tool_calls` via SGLang's `--tool-call-parser qwen3_coder`. Composes with thinking mode and with image / video inputs.
**License:** TODO — verify on HuggingFace model card.
**License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
## 2. SGLang Installation
@@ -31,11 +32,9 @@ Pull the nightly Docker image (rolling tag, tracks `main`):
```bash
# CUDA 13 (Hopper / Blackwell, default)
docker pull lmsysorg/sglang:dev
docker pull lmsysorg/sglang:dev-minicpm-v-4-6
# CUDA 12 (Ampere or older drivers)
docker pull lmsysorg/sglang:dev-cu12
docker pull lmsysorg/sglang:dev-cu12-minicpm-v-4-6
```
For the general SGLang installation guide (PyPI, source, Docker) see the [official SGLang installation guide](../../../docs/get-started/install).
@@ -44,7 +43,7 @@ For the general SGLang installation guide (PyPI, source, Docker) see the [offici
### 3.1 Basic Configuration
**Interactive Command Generator**: Use the configuration selector below to generate the appropriate deployment command. The `Reasoning Parser` and `Tool Call Parser` toggles add `--reasoning-parser qwen3` and `--tool-call-parser qwen` respectively; see §4.4 for usage details.
**Interactive Command Generator**: Use the configuration selector below to generate the appropriate deployment command. The `Variant` toggle switches between `openbmb/MiniCPM-V-4.6` (base) and `openbmb/MiniCPM-V-4.6-Thinking`. The `Reasoning Parser` and `Tool Call Parser` toggles add `--reasoning-parser qwen3` and `--tool-call-parser qwen3_coder` respectively; see §4.4 for usage details.
import { MiniCPMV46Deployment } from '/src/snippets/autoregressive/minicpm-v-4_6-deployment.jsx'
@@ -70,10 +69,12 @@ import { MiniCPMV46Deployment } from '/src/snippets/autoregressive/minicpm-v-4_6
Deploy the model on an H200:
```bash Command
sglang serve --model-path openbmb/MiniCPM-V-4_6 \
sglang serve --model-path openbmb/MiniCPM-V-4.6 \
--trust-remote-code \
--dtype bfloat16 \
--mem-fraction-static 0.15 \
--mamba-scheduler-strategy extra_buffer \
--page-size 64 \
--host 0.0.0.0 --port 30000
```
@@ -88,7 +89,7 @@ client = OpenAI(
)
response = client.chat.completions.create(
model="openbmb/MiniCPM-V-4_6",
model="openbmb/MiniCPM-V-4.6",
messages=[
{
"role": "user",
@@ -127,7 +128,7 @@ from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="openbmb/MiniCPM-V-4_6",
model="openbmb/MiniCPM-V-4.6",
messages=[
{
"role": "user",
@@ -163,7 +164,7 @@ from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="openbmb/MiniCPM-V-4_6",
model="openbmb/MiniCPM-V-4.6",
messages=[
{
"role": "user",
@@ -204,7 +205,7 @@ from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="openbmb/MiniCPM-V-4_6",
model="openbmb/MiniCPM-V-4.6",
messages=[{"role": "user", "content": "Reply with the single word 'hi'. No explanation."}],
max_tokens=200,
)
@@ -221,7 +222,7 @@ content : hi
```python Example (instruct mode)
response = client.chat.completions.create(
model="openbmb/MiniCPM-V-4_6",
model="openbmb/MiniCPM-V-4.6",
messages=[{"role": "user", "content": "Reply with the single word 'hi'. No explanation."}],
max_tokens=200,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
@@ -239,10 +240,10 @@ content : hi
#### 4.4.2 Tool Calling
Pass `--tool-call-parser qwen` to the server (toggle "Tool Call Parser" on in §3.1) so SGLang extracts `<tool_call>` blocks from the model output into the OpenAI-style `message.tool_calls` field (with `finish_reason="tool_calls"`). The model speaks the Qwen 2.5 tool-call format (`<tool_call>\n{...}\n</tool_call>`); the `qwen` parser is the right one. Tool calls compose with both reasoning modes and with image / video inputs.
Pass `--tool-call-parser qwen3_coder` to the server (toggle "Tool Call Parser" on in §3.1) so SGLang extracts `<tool_call>` blocks from the model output into the OpenAI-style `message.tool_calls` field (with `finish_reason="tool_calls"`). The model speaks the Qwen3.5 XML tool-call format (`<tool_call><function=name><parameter=k>v</parameter></function></tool_call>`); the `qwen3_coder` parser is the right one. Tool calls compose with both reasoning modes and with image / video inputs.
<Warning>
Do **not** use `--tool-call-parser qwen3_coder` for MiniCPM-V 4.6 — even though the Qwen3.5 cookbooks use it. `qwen3_coder` expects an XML-style inner format (`<function=name><parameter=k>v</parameter></function>`), but 4.6 emits Qwen2.5-style JSON (`{"name":..., "arguments":...}`) inside the same `<tool_call>` wrapper. The result is `finish_reason="tool_calls"` but an empty `tool_calls` array, with the raw markup left in `content` — broken in both directions.
Do **not** use `--tool-call-parser qwen` for MiniCPM-V 4.6 — that parser expects the older Qwen2.5 JSON format `<tool_call>{"name":..., "arguments":...}</tool_call>`, but both public 4.6 variants emit the Qwen3.5-style XML format with nested `<function=…>` and `<parameter=…>` tags. With `qwen` the outer `<tool_call>` markers match but the inner JSON parse fails, so `tool_calls` returns empty and the raw markup is left in `content`.
</Warning>
@@ -270,7 +271,7 @@ tools = [
]
response = client.chat.completions.create(
model="openbmb/MiniCPM-V-4_6",
model="openbmb/MiniCPM-V-4.6",
messages=[{"role": "user", "content": "What is the weather in San Francisco? Use the tool."}],
tools=tools,
max_tokens=200,
@@ -292,10 +293,6 @@ To get the final natural-language answer, feed the tool's result back as a `tool
## 5. Benchmark
<Note>
**TODO — re-run all benchmarks once the official MiniCPM-V 4.6 release weights are public.** Numbers in this section were captured during SGLang port verification and should not be interpreted as representative of the public release.
</Note>
**Common Test Environment (all benchmarks below):**
- Hardware: 1× NVIDIA H200 (141 GB), single GPU (no TP / DP)
@@ -306,7 +303,7 @@ To get the final natural-language answer, feed the tool's result back as a `tool
```bash Command
CUDA_VISIBLE_DEVICES=0 python -m sglang.launch_server \
--model-path openbmb/MiniCPM-V-4_6 \
--model-path openbmb/MiniCPM-V-4.6 \
--trust-remote-code \
--dtype bfloat16 \
--mem-fraction-static 0.5 \
@@ -322,13 +319,54 @@ CUDA_VISIBLE_DEVICES=0 python -m sglang.launch_server \
#### 5.1.1 MMMU Benchmark
- Benchmark Command
```bash Command
python3 benchmark/mmmu/bench_sglang.py --port 30000 --concurrency 48 --max-new-tokens 2048
```
- Test Result
Numbers will be filled in once the official MiniCPM-V 4.6 release weights are public.
```
{'Accounting': {'acc': 0.767, 'num': 30},
'Agriculture': {'acc': 0.533, 'num': 30},
'Architecture_and_Engineering': {'acc': 0.4, 'num': 30},
'Art': {'acc': 0.6, 'num': 30},
'Art_Theory': {'acc': 0.667, 'num': 30},
'Basic_Medical_Science': {'acc': 0.533, 'num': 30},
'Biology': {'acc': 0.333, 'num': 30},
'Chemistry': {'acc': 0.333, 'num': 30},
'Clinical_Medicine': {'acc': 0.467, 'num': 30},
'Computer_Science': {'acc': 0.333, 'num': 30},
'Design': {'acc': 0.533, 'num': 30},
'Diagnostics_and_Laboratory_Medicine': {'acc': 0.333, 'num': 30},
'Economics': {'acc': 0.633, 'num': 30},
'Electronics': {'acc': 0.5, 'num': 30},
'Energy_and_Power': {'acc': 0.633, 'num': 30},
'Finance': {'acc': 0.533, 'num': 30},
'Geography': {'acc': 0.367, 'num': 30},
'History': {'acc': 0.533, 'num': 30},
'Literature': {'acc': 0.7, 'num': 30},
'Manage': {'acc': 0.367, 'num': 30},
'Marketing': {'acc': 0.733, 'num': 30},
'Materials': {'acc': 0.367, 'num': 30},
'Math': {'acc': 0.567, 'num': 30},
'Mechanical_Engineering': {'acc': 0.333, 'num': 30},
'Music': {'acc': 0.267, 'num': 30},
'Overall': {'acc': 0.527, 'num': 900},
'Overall-Art and Design': {'acc': 0.517, 'num': 120},
'Overall-Business': {'acc': 0.607, 'num': 150},
'Overall-Health and Medicine': {'acc': 0.553, 'num': 150},
'Overall-Humanities and Social Science': {'acc': 0.617, 'num': 120},
'Overall-Science': {'acc': 0.473, 'num': 150},
'Overall-Tech and Engineering': {'acc': 0.443, 'num': 210},
'Pharmacy': {'acc': 0.667, 'num': 30},
'Physics': {'acc': 0.767, 'num': 30},
'Psychology': {'acc': 0.567, 'num': 30},
'Public_Health': {'acc': 0.767, 'num': 30},
'Sociology': {'acc': 0.667, 'num': 30}}
eval out saved to ./val_sglang.json
Overall accuracy: 0.527
```
### 5.2 Speed Benchmark
@@ -339,7 +377,7 @@ We use SGLang's built-in `bench_serving` tool with random text prompts (1000 inp
```bash Command
python3 -m sglang.bench_serving \
--backend sglang \
--model openbmb/MiniCPM-V-4_6 \
--model openbmb/MiniCPM-V-4.6 \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
@@ -393,7 +431,7 @@ Max ITL (ms): 5.79
```bash Command
python3 -m sglang.bench_serving \
--backend sglang \
--model openbmb/MiniCPM-V-4_6 \
--model openbmb/MiniCPM-V-4.6 \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
@@ -453,7 +491,7 @@ python3 -m sglang.bench_serving \
--backend sglang-oai-chat \
--host 127.0.0.1 \
--port 30000 \
--model openbmb/MiniCPM-V-4_6 \
--model openbmb/MiniCPM-V-4.6 \
--dataset-name image \
--image-count 1 \
--image-resolution 720p \
@@ -512,7 +550,7 @@ python3 -m sglang.bench_serving \
--backend sglang-oai-chat \
--host 127.0.0.1 \
--port 30000 \
--model openbmb/MiniCPM-V-4_6 \
--model openbmb/MiniCPM-V-4.6 \
--dataset-name image \
--image-count 1 \
--image-resolution 720p \
docs_new/src/snippets/autoregressive/minicpm-v-4_6-deployment.jsx
+23 -17
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@@ -1,23 +1,18 @@
export const MiniCPMV46Deployment = () => {
// STATUS: Preview / pending upstream merge.
//
// Only **H200 + BF16 + TP=1** is actually tested. Other NVIDIA platforms
// below are listed in chronological generation order for convenience
// but are **not yet verified**:
// NVIDIA platforms listed in chronological generation order:
// - A100 (Ampere, sm_80): FA3 falls back to flashinfer.
// - H100 (Hopper, sm_90a): same arch family as H200, same kernels.
// - H200 (Hopper, sm_90a): TESTED; default.
// - B200 (Blackwell, sm_100a): sglang auto-picks trtllm_mha; added
// - H100 / H200 (Hopper, sm_90a): same kernel family.
// - B200 (Blackwell, sm_100a): sglang auto-picks trtllm_mha; pinned
// explicitly here for safety.
// B300 / GB300 (sm_103a) require the CUDA-13 image variant (`-cu130`)
// and are not exposed in this preview generator.
// and are not exposed in this generator.
//
// mem-fraction-static values below are conservative estimates for the
// released model size; re-tune once parameter count is published.
// mem-fraction-static values are conservative defaults; re-tune for
// your workload.
//
// Required flags (any hardware):
// --trust-remote-code tokenizer / preprocessor loading
// --dtype bfloat16 released ckpt config.json has torch_dtype:None;
// --dtype bfloat16 released ckpt config.json has no torch_dtype;
// without forcing bf16 the GDN causal_conv1d
// triton kernel fails on bf16/fp16 branch merge.
const options = {
@@ -31,12 +26,20 @@ export const MiniCPMV46Deployment = () => {
{ id: 'b200', label: 'B200', default: false },
],
},
variant: {
name: 'variant',
title: 'Variant',
items: [
{ id: 'base', label: 'Base', subtitle: 'MiniCPM-V-4.6', default: true },
{ id: 'thinking', label: 'Thinking', subtitle: 'MiniCPM-V-4.6-Thinking', default: false },
],
},
reasoning: {
name: 'reasoning',
title: 'Reasoning Parser',
items: [
{ id: 'enabled', label: 'enabled', default: true },
{ id: 'disabled', label: 'disabled', default: false },
{ id: 'enabled', label: 'enabled', default: false },
{ id: 'disabled', label: 'disabled', default: true },
],
},
toolcall: {
@@ -67,15 +70,18 @@ export const MiniCPMV46Deployment = () => {
};
const generateCommand = (values) => {
const { hardware, reasoning, toolcall, mambaCache } = values;
const { variant, hardware, reasoning, toolcall, mambaCache } = values;
const hwConfig = modelConfigs[hardware];
if (!hwConfig) return `# Error: Unknown hardware platform`;
const { tp, mem } = hwConfig;
const isBlackwell = hardware === 'b200';
const modelPath = variant === 'thinking'
? 'openbmb/MiniCPM-V-4.6-Thinking'
: 'openbmb/MiniCPM-V-4.6';
let cmd = `sglang serve --model-path openbmb/MiniCPM-V-4_6`;
let cmd = `sglang serve --model-path ${modelPath}`;
if (tp > 1) {
cmd += ` \\\n --tp ${tp}`;
}
@@ -89,7 +95,7 @@ export const MiniCPMV46Deployment = () => {
cmd += ` \\\n --reasoning-parser qwen3`;
}
if (toolcall === 'enabled') {
cmd += ` \\\n --tool-call-parser qwen`;
cmd += ` \\\n --tool-call-parser qwen3_coder`;
}
if (mambaCache === 'v2') {
cmd += ` \\\n --mamba-scheduler-strategy extra_buffer`;