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MiniCPM5 2B GGUF

MiniCPM5 2B GGUF is a GGUF-formatted text-generation model from OpenBMB, licensed under Apache-2.0. It is described as a dense 2B Transformer, the second model in the MiniCPM5 series after MiniCPM5-1B, and is intended for on-device, local deployment, and resource-constrained scenarios. The model card lists English and Chinese language support and tags for long-context, tool-calling, on-device, and edge-ai use. Training data references include the Ultra-FineWeb, UltraX-Preview, Ultra-FineWeb-L3, UltraData-Math, UltraData-Code, UltraData-SFT-2605, UltraData-SFT-Agent-2609, and UltraData-RL-2609 datasets.

EstimatedRepository opened Sep 5, 2026Source checked 9/27/2026Version: 2079a22f
LOCALRENTED GPUOPEN WEIGHTS
Freshness 86 out of 100. Repository age: 40 days old, Sep 5, 2026. Measures age, not quality.
Popularity 71 out of 100. 209,975 downloads in 30 days; 372 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source
Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source
Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method
WORKFLOWS FOR THIS MODEL 2

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At a glance

Architecture
unknown
License
apache-2.0
Disk space
1.5 GB
Software
llama.cpp, SGLang, vLLM
View the model source
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Best for

  • On-device or local text generation
  • Resource-constrained deployment scenarios
  • Long-context understanding tasks
  • Tool-calling tasks
  • Agentic tasks

Tradeoffs

  • Only English and Chinese language support is listed
  • No numerical hardware requirements or compatibility details are provided
  • No performance benchmarks or comparisons beyond model card claims are supplied
  • Not described as supporting languages beyond English and Chinese
  • No inference, testing, or purchase guidance is provided

Ways to run it

llama.cpp · Windows, macOS, Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

llama-server -m MiniCPM5-2B-F16.gguf -a MiniCPM5-2B --port 8080 -ngl 99 -c 8192 --jinja
Official instructions

SGLang · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

python -m sglang.launch_server --model-path openbmb/MiniCPM5-2B --port 30000
Official instructions

vLLM · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

vllm serve openbmb/MiniCPM5-2B --port 8000
Official instructions

Explore its uses

GGUFTEXT-GENERATION

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MiniCPM5 2B GGUF: hardware, VRAM & setup | YouRunAI