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MiniCPM5 1B SFT

MiniCPM5 1B SFT is an Apache-2.0 licensed, text-generation model from openbmb/MiniCPM5-1B-SFT. It is an SFT-only checkpoint (before RL/OPD) in the MiniCPM5 series, supporting English and Chinese. The model is a dense 1B Transformer with a LlamaForCausalLM architecture, 1,080,632,832 parameters (679,552,512 non-embedding), 24 layers, GQA with 16 Q heads and 2 KV heads, and a 131,072 token context length. It includes a hybrid reasoning chat template with a `thinking` mode activated via `enable_thinking`, and is intended for on-device, local deployment, and resource-constrained scenarios.

EstimatedRepository opened May 21, 2026Source checked 9/24/2026Version: a60b37f1

LOCALRENTED GPUOPEN WEIGHTS

Freshness 57 out of 100. Repository age: 147 days old, May 21, 2026. Measures age, not quality.

Popularity 49 out of 100. 5,106 downloads in 30 days; 47 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: 1.08B
Architecture: llama
Context length: 131,072
License: apache-2.0
Disk space: 2 GB
Software: llama.cpp, SGLang, vLLM

View the model source

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Best for

Local assistants on edge or on-device environments
Coding agents
Tool-use workflows
Reasoning scenarios where a compact model is preferred
Long-context text generation with English and Chinese

Tradeoffs

It is an SFT-only checkpoint and does not include RL or OPD post-training.
No performance benchmarks or numerical hardware requirements are provided.
No evaluation results or testing claims are included.
The model card does not specify compatibility with specific inference backends or formats.
No purchasable recommendations or deployment guarantees are 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

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