Model reference · open weights
MiniCPM4 is an open-weight language model from openbmb, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
👋 Join us on <a href="https://discord.gg/3cGQn9b3YM" target="blank"Discord</a and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="blank"WeChat</a What's New - [2025.06.06] MiniCPM4 series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report here.🔥🔥🔥 MiniCPM4 Series MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems. - MiniCPM4-8B: The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens. - MiniCPM4-0.5B: The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens. (<-- you are here) - MiniCPM4-8B-Eagle-FRSpec: Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B. - MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu: Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B. - MiniCPM4-8B-Eagle-vLLM: Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B. - MiniCPM4-8B-marlin-Eagle-vLLM: Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B. - BitCPM4-0.5B: Extreme ternary quantization applied to MiniCPM4-0.5B compresses model parameters into ternary values, achieving a 90% reduction in bit width. - BitCPM4-1B: Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width. - MiniCPM4-Survey: Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers. - MiniCPM4-MCP: Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements. Introduction MiniCPM 4 is an extremely efficient edge-side large model that has undergone efficient optimization across four dimension
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | openbmb |
|---|---|
| Type | Language models |
| Parameters (lead) | 434M |
| Context | 32k tokens |
| Variants | 2 |
| Runs with | transformers |
| Released | 2025-06-05 |
| Popularity | 42k downloads / month |
| Likes | 289 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
Using it via the API
Once AxForge deploys minicpm4 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (minicpm4 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"minicpm4","messages":[{"role":"user","content":"Hello"}]}'
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗