openbmb/MiniCPM5-2B-SFT

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Highlights

We are releasing MiniCPM5-2B, the second model in the MiniCPM5 series, following MiniCPM5-1B. It is a dense 2B Transformer that scales up the same training recipe, built for on-device, local deployment, and resource-constrained scenarios, reaching 2B-class open-source SOTA.

🏆 2B-class open-source SOTA: compared with strong open-source models of similar size, MiniCPM5-2B achieves SOTA performance within this comparison set. It remains competitive with 4B-class models overall, while showing its advantages over models of comparable size in coding, mathematics, long-context understanding, tool use, and agentic tasks.

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Capability Radar by Dimension

20%

40%

60%

80%

100%

Code Reasoning

Math Reasoning

Instruction Following

General Knowledge

Long Context

Tool Use

Coding Agent

Search Agent

General Agent

MiniCPM5-2B avg 53.9

Qwen3.5-4B avg 51.1

granite-4.2-3B avg 42.7

LFM2.5-2.6B avg 33.2 each axis: max = 100%

📂 Open High-Quality Data: Alongside the model, we are releasing the high-quality training datasets behind it as part of the UltraData family: UltraX, a high-quality web pre-training dataset; UltraData-Code, featuring L0–L3 tiered code data management to drive a significant leap in coding capabilities; UltraData-SFT-Agent-2609, comprising 500K agent training samples to enhance comprehensive on-device agent capabilities; and UltraData-RL-2609, with 80K+ high-quality RL training samples covering mathematics, code, general knowledge, and long-context reasoning.

Model List

Use this directory to choose the model format that matches your runtime:

MiniCPM5-2B

• [MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) · ModelScope · BF16 final release (post-trained with RL + OPD)
• [MiniCPM5-2B-SFT](https://huggingface.co/openbmb/MiniCPM5-2B-SFT) · ModelScope · BF16 SFT-only checkpoint (before RL / OPD) 👈 you are here
• [MiniCPM5-2B-Midtrain](https://huggingface.co/openbmb/MiniCPM5-2B-Midtrain) · ModelScope · BF16 mid-training checkpoint (before SFT)
• [MiniCPM5-2B-Base](https://huggingface.co/openbmb/MiniCPM5-2B-Base) · ModelScope · BF16 base checkpoint (pre-training only)
• [MiniCPM5-2B-GGUF](https://huggingface.co/openbmb/MiniCPM5-2B-GGUF) · ModelScope · GGUF for llama.cpp / Ollama / LM Studio
• [MiniCPM5-2B-MLX](https://huggingface.co/openbmb/MiniCPM5-2B-MLX) · ModelScope · MLX / 4bit for Apple Silicon
• [MiniCPM5-2B-GPTQ](https://huggingface.co/openbmb/MiniCPM5-2B-GPTQ) · ModelScope · GPTQ / 4bit quantized model
• [MiniCPM5-2B-DSpark](https://huggingface.co/openbmb/MiniCPM5-2B-DSpark) · ModelScope · DSpark draft model for inference acceleration

MiniCPM5-1B

• [MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B) · ModelScope · BF16 final release (post-trained with RL + OPD)
• [MiniCPM5-1B-SFT](https://huggingface.co/openbmb/MiniCPM5-1B-SFT) · ModelScope · BF16 SFT-only checkpoint (before RL / OPD)
• [MiniCPM5-1B-Base](https://huggingface.co/openbmb/MiniCPM5-1B-Base) · ModelScope · BF16 base checkpoint (pre-training only)
• [MiniCPM5-1B-GGUF](https://huggingface.co/openbmb/MiniCPM5-1B-GGUF) · ModelScope · GGUF for llama.cpp / Ollama / LM Studio
• [MiniCPM5-1B-MLX](https://huggingface.co/openbmb/MiniCPM5-1B-MLX) · ModelScope · MLX / 4bit for Apple Silicon

Model Information

MiniCPM5-2B has the following features:

• Type: Causal Language Model
• Architecture: Standard LlamaForCausalLM
• Number of Parameters: 2,516,756,480
• Number of Non-Embedding Parameters: 1,981,982,720
• Number of Layers: 42
• Number of Attention Heads (GQA): 16 for Q and 2 for KV
• Context Length: 131,072

Introduction

MiniCPM5-2B is the second model 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...