MiniCPM is a family of compact language models, including MiniCPM5-1B, designed to run locally on devices with limited resources. Developers use it for on-device assistants, reasoning, code, tool use, deployment, and fine-tuning, while the repository also includes a desktop-pet example. The catalogue entries support deployment and fine-tuning workflows for the models.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OpenBMB/MiniCPM --skill minicpm5-deploy-litertgit clone --depth 1 https://github.com/OpenBMB/MiniCPMWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/openbmb/minicpm/minicpm5-deploy-litert)<a href="https://agentmods.dev/skills/openbmb/minicpm/minicpm5-deploy-litert"><img src="https://agentmods.dev/badge/skills/openbmb/minicpm/minicpm5-deploy-litert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/openbmb/minicpm/minicpm5-deploy-litert"><img src="https://agentmods.dev/badge/skills/openbmb/minicpm/minicpm5-deploy-litert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00121 | $0.01532 |
| Opus 5 | $0.00060 | $0.00766 |
| Sonnet 5 | $0.00024 | $0.00306 |
| Haiku 4.5 | $0.00012 | $0.00153 |
Grade A, and why
minicpm5-deploy-litert scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl http://127.0.0.1:9379/v1/chat/completions -H "Content-Type: application/json" \ How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploy MiniCPM5-2B / MiniCPM5-1B with LiteRT-LM (Android / iOS / desktop / IoT)
LiteRT-LM, Google's on-device runtime built on LiteRT (formerly TensorFlow Lite). One .litertlm bundle, CPU or GPU, through a CLI, an OpenAI-compatible server, Python, Kotlin and Swift. The bundles are hosted in litert-community. Human-readable reference: docs/deployment/litert.md.
Required input
| Var | Example | Default |
|---|---|---|
LITERT_REPO |
litert-community/MiniCPM5-2B or litert-community/MiniCPM5-1B |
litert-community/MiniCPM5-2B |
FILE |
MiniCPM5-2B_int4.litertlm (1.55 GB, the phone file) or MiniCPM5-2B_int8.litertlm (2.60 GB, reasoning that has to finish); 1B: minicpm_wi4b32_wi8_afp32.litertlm (CPU) or minicpm_wi4b32_wi8_afp32_gpu_opt.litertlm (GPU) |
MiniCPM5-2B_int4.litertlm |
BACKEND |
cpu or gpu |
cpu |
THINKING |
true (reason first) or false (direct answer) |
false |
PROMPT |
1+1=? |
1+1=? |
Steps
1. Install (once)
uv tool install litert-lm
2A. Run a pre-converted bundle (desktop CLI)
litert-lm run --from-huggingface-repo="${LITERT_REPO}" "${FILE}" \
--backend "${BACKEND}" --thinking "${THINKING:-false}" --prompt "${PROMPT}"
The first run downloads the file into ~/.litert-lm/cache/huggingface/<repo>/; later runs skip the download. With thinking on, the reasoning prints between [thought] and [/thought] and the answer follows; --thinking-budget 2048 caps the reasoning (4096 for math); THINKING=true makes the model reason first. Sampling as OpenBMB recommends: --top-k 40 --top-p 0.95 --temperature 1.0 (the CLI's default top-k is 1 = greedy, so temperature alone changes nothing).
2B. Android
- No code: the AI Edge Gallery app, Model manager → + → Import from HF → paste the file's Hugging Face link.
- Your own app:
implementation("com.google.ai.edge.litertlm:litertlm-android:0.17.0")from Google Maven, thelibOpenCL.so<uses-native-library>entries in the manifest for the GPU, then:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 80 lines · 121 tokens per session scan A a3dea7288140
minicpm5-deploy-litert is a skill published in the GitHub repository OpenBMB/MiniCPM (10,780 stars, last pushed yesterday), licensed Apache-2.0. It adds 121 tokens to every session and 1,532 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-10.
Other skills, from other repositories
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Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
spark-memory-thermal-ops
Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
llama-cpp
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
amc-run-rtsp-calibration
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
amc-run-video-calibration
Calibrates pre-recorded cam.mp4 datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to amc-run-rtsp-calibration.