MiniCPM5

About

What MiniCPM5 is

English | Highlights We are releasing MiniCPM5-1B, the first model in the MiniCPM5 series. It is a dense 1B Transformer built for on-device, local deployment, and resource-constrained scenarios, reaching 1B-class open-source SOTA. 🏆 1B-class open-source SOTA: compared with strong open-source models in the same size class, MiniCPM5-1B reaches SOTA within this comparison set. Its advantage is most visible in agentic tool use, code generation, and difficult reasoning. 🧠 Hybrid Reasoning: built-in <think chat template, switch via enablethinking. The same checkpoint serves as both a fast assistant and a deliberate reasoner. 🛠️ Deployment / Fine-tuning Resources: the MiniCPM GitHub repo provides single-page cookbooks and Agent Skills for major inference backends and fine-tuning frameworks. 🐱 Desktop Pet: a local-LLM desktop pet driven by MiniCPM5-1B. Model List Use this directory to choose the model format that matches your runtime: - MiniCPM5-1B · ModelScope · BF16 final release (post-trained with RL + OPD) 👈 you are here - MiniCPM5-1B-SFT · ModelScope · BF16 SFT-only checkpoint (before RL / OPD) - MiniCPM5-1B-Base · ModelScope · BF16 base checkpoint (pre-training only) - MiniCPM5-1B-GGUF · ModelScope · GGUF for llama.cpp / Ollama / LM Studio - MiniCPM5-1B-MLX · ModelScope · MLX / 4bit for Apple Silicon Model Information MiniCPM5-1B has the following features: - Type: Causal Language Model - Architecture: Standard LlamaForCausalLM - Number of Parameters: 1,080,632,832 - Number of Non-Embedding Parameters: 679,552,512 - Number of Layers: 24 - Number of Attention Heads (GQA): 16 for Q and 2 for KV - Context Length: 131,072 Introduction MiniCPM5-1B is the first checkpoint in the MiniCPM5 series. It is designed for local assistants, coding agents, tool-use workflows, and reasoning scenarios where a compact model is preferred. The model keeps a small deployment footprint while providing native long-context support and both Think / No Think chat modes through the same checkpoint. Evaluation Results We compare MiniCPM5-1B with strong open-source models in the same size class, including LFM2.5-1.2B-Thinking, Qwen3-0.6B/think and Qwen3.5-0.8B/think. These are capable baseline

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

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

Call it like any OpenAI endpoint

Once AxForge deploys minicpm5 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (minicpm5 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":"minicpm5","messages":[{"role":"user","content":"Hello"}]}'

Details

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