MiniCPM5-SFT

About

What MiniCPM5-SFT 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) - MiniCPM5-1B-SFT · ModelScope · BF16 SFT-only checkpoint (before RL / OPD) 👈 you are here - 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

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

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