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MiniCPM5 2B MLX

MiniCPM5 2B MLX is an Apache-2.0 licensed, text-generation model from OpenBMB, designed as a dense 2B Transformer for on-device, local deployment, and resource-constrained scenarios. According to the model card, it is the second model in the MiniCPM5 series and supports English and Chinese. Its stated focus areas include coding, mathematics, long-context understanding, tool use, and agentic tasks, and it is presented as achieving SOTA performance within a comparison set of similarly sized open-source models while remaining competitive with 4B-class models overall.

EstimatedRepository opened Sep 5, 2026Source checked 9/24/2026Version: 8a9ad753
LOCALRENTED GPUOPEN WEIGHTS
Freshness 86 out of 100. Repository age: 40 days old, Sep 5, 2026. Measures age, not quality.
Popularity 73 out of 100. 554,313 downloads in 30 days; 55 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

At a glance

Parameters
2.52B
Architecture
llama
Context length
131,072
License
apache-2.0
Disk space
1.3 GB
Software
llama.cpp, SGLang, vLLM
View the model source
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Best for

  • Local text generation
  • On-device deployment in resource-constrained scenarios
  • Coding tasks
  • Mathematics tasks
  • Long-context understanding and tool/agentic tasks

Tradeoffs

  • The supplied facts do not specify supported languages beyond English and Chinese.
  • No numerical hardware requirements are provided.
  • No performance benchmarks or testing results are included in the supplied facts.
  • No compatibility details beyond the MLX designation and transformers library are provided.
  • No license limitations or deployment restrictions are stated beyond the Apache-2.0 identifier.

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

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