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MiniCPM5 1B Base

MiniCPM5 1B Base is the pre-training-only base checkpoint in the MiniCPM5 series, released under the apache-2.0 license. It is a dense causal language model using a standard LlamaForCausalLM architecture, with 1,080,632,832 total parameters and 679,552,512 non-embedding parameters. It has 24 layers, GQA with 16 query heads and 2 KV heads, and a context length of 131,072. The model targets English and Chinese, supports text-generation, and is associated with long-context, tool-calling, on-device, and edge-ai tags. Pretraining data includes openbmb/Ultra-FineWeb, Ultra-FineWeb-L3, UltraData-Math, and UltraData-SFT-2605. The official model card also notes a built-in think chat template switchable via enable_thinking for the released MiniCPM5-1B model, but this base checkpoint is pre-training only.

EstimatedRepository opened May 21, 2026Source checked 9/24/2026Version: 15617069

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. 6,353 downloads in 30 days; 22 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

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Best for

Base model for further fine-tuning or post-training
English and Chinese text generation
Long-context language modeling up to 131,072 tokens
Research on compact causal language models
On-device or edge-oriented local LLM experimentation

Tradeoffs

It is a base checkpoint, not post-trained for chat or instruction following
No performance, compatibility, or hardware requirements are specified in the source facts
Only English and Chinese are listed as languages
It is a text-generation model only; no multimodal capabilities are stated
The model card describes features for MiniCPM5-1B, but this specific Base checkpoint is pre-training only

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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