メニュー
Run MiniCPM5-1B via Ollama on macOS / Linux laptop using the released GGUF. Use when the user wants "ollama run", "ollama pull", a Modelfile-driven setup, or one-line laptop deployment.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | minicpm5-deploy-ollama |
| description | Run MiniCPM5-1B via Ollama on macOS / Linux laptop using the released GGUF. Use when the user wants "ollama run", "ollama pull", a Modelfile-driven setup, or one-line laptop deployment. |
One-binary, no-Python laptop deployment. Consumes the released GGUF.
| Var | Example | Default |
|---|---|---|
GGUF_REPO | openbmb/MiniCPM5-1B-GGUF | required |
QUANT | Q4_K_M (657 MB, recommended) / Q8_0 / F16 | Q4_K_M |
MODEL_NAME | minicpm5-1b | minicpm5-1b |
brew install ollama # macOS
# or:
curl -fsSL https://ollama.com/install.sh | sh # Linux
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama serve &
mkdir -p ~/${MODEL_NAME} && cd ~/${MODEL_NAME}
huggingface-cli download ${GGUF_REPO} MiniCPM5-1B-${QUANT}.gguf --local-dir .
cat > Modelfile <<EOF
FROM ./MiniCPM5-1B-${QUANT}.gguf
# MiniCPM5 chat template (matches release tokenizer)
TEMPLATE """{{- if .Messages -}}
{{- range .Messages -}}
<|im_start|>{{ .Role }}
{{ .Content }}<|im_end|>
{{ end -}}
<|im_start|>assistant
{{ end -}}"""
PARAMETER stop "<|im_end|>"
PARAMETER stop "</s>"
# Defaults tuned for nothink mode
PARAMETER temperature 0.7
PARAMETER top_p 0.95
PARAMETER num_ctx 8192
EOF
ollama create ${MODEL_NAME} -f Modelfile
ollama run ${MODEL_NAME}
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "minicpm5-1b",
"messages": [{"role": "user", "content": "1+1=?"}],
"temperature": 0.7, "top_p": 0.95, "max_tokens": 64
}'
Expected: "2" in the reply.
Default Modelfile is nothink. For think:
ollama run ${MODEL_NAME} --temperature 0.9 --top-p 0.95
Or bake it into a separate model tag by flipping temperature 0.7 to temperature 0.9 (top_p stays 0.95) and ollama create ${MODEL_NAME}-think -f Modelfile.think.
To force the auto-injected <think>\n prefix, use raw mode:
curl http://localhost:11434/api/generate -d '{
"model": "minicpm5-1b",
"raw": true,
"prompt": "<|im_start|>user\n鸡兔同笼…<|im_end|>\n<|im_start|>assistant\n<think>\n",
"options": {"temperature": 0.9, "top_p": 0.95}
}'
Error: invalid file magic: corrupted download. Re-run huggingface-cli download.minicpm5-deploy-mlx (Q4 build)minicpm5-deploy-lmstudiominicpm5-deploy-vllmRun MiniCPM5-1B with llama.cpp using the released GGUF artifacts (F16 / Q8_0 / Q4_K_M). Use when the user wants CPU-only / consumer-GPU / cross-platform native deployment, asks for "llama.cpp", "llama-cli", "llama-server", "GGUF", or has no Python available.
Fine-tune MiniCPM5-1B into a LoRA adapter and convert it to a GGUF adapter that loads directly into llama.cpp / llama-server and the MiniCPM Desk Pet app's custom-LoRA upload. Use when the user wants "GGUF LoRA", "convert LoRA to GGUF", "convert_lora_to_gguf", a custom persona/skin for the desktop pet, "桌宠自定义 LoRA", "上传 LoRA 到桌宠", or asks how to take a trained adapter and run it on a GGUF base.
Pick the right fine-tuning framework for a MiniCPM5-1B base checkpoint and route to a framework-specific cookbook skill. Use when the user wants to SFT / LoRA / DPO / continue-pretrain MiniCPM5 and has not yet committed to a specific framework, or when they say "fine-tune MiniCPM5", "train MiniCPM5", "MiniCPM5 微调", "LoRA MiniCPM5", "继续训练 MiniCPM5".
Run MiniCPM5-1B in LM Studio (desktop GUI) using either the GGUF runtime (cross-platform) or the MLX runtime (Apple Silicon, faster). Includes OpenAI-compatible local server. Use when the user mentions "LM Studio", desktop GUI inference, "lms" CLI, or wants a no-code chat UI for MiniCPM5.
Run MiniCPM5-1B natively on Apple Silicon with Apple's MLX framework. Use when the user has an Apple Silicon Mac and asks for "MLX", "mlx_lm", "mlx_lm.convert", "mlx_lm.generate", or wants the fastest path on Apple Silicon.
Serve MiniCPM5-1B via SGLang as an OpenAI-compatible HTTP server with RadixAttention prefix cache and built-in MiniCPM5 tool-call parsing. Use when the user asks for "SGLang", "RadixAttention", "prefix cache", batch evaluation, tool calling, or wants a high-concurrency NVIDIA-GPU server alternative to vLLM.