Instructions to use openbmb/MiniCPM5-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM5-1B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="openbmb/MiniCPM5-1B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("openbmb/MiniCPM5-1B-GGUF", dtype="auto")

llama-cpp-python

How to use openbmb/MiniCPM5-1B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="openbmb/MiniCPM5-1B-GGUF",
	filename="MiniCPM5-1B-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use openbmb/MiniCPM5-1B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/openbmb/MiniCPM5-1B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use openbmb/MiniCPM5-1B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM5-1B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM5-1B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM5-1B-GGUF:Q4_K_M

SGLang

How to use openbmb/MiniCPM5-1B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "openbmb/MiniCPM5-1B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM5-1B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "openbmb/MiniCPM5-1B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM5-1B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use openbmb/MiniCPM5-1B-GGUF with Ollama:
```
ollama run hf.co/openbmb/MiniCPM5-1B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use openbmb/MiniCPM5-1B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for openbmb/MiniCPM5-1B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for openbmb/MiniCPM5-1B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for openbmb/MiniCPM5-1B-GGUF to start chatting

Pi new

How to use openbmb/MiniCPM5-1B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "openbmb/MiniCPM5-1B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use openbmb/MiniCPM5-1B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default openbmb/MiniCPM5-1B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use openbmb/MiniCPM5-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM5-1B-GGUF:Q4_K_M
```

Lemonade

How to use openbmb/MiniCPM5-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull openbmb/MiniCPM5-1B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM5-1B-GGUF-Q4_K_M

List all available models

lemonade list

OpenBMB-LLM commited on 2 days ago

Commit

60a062a

verified ·

1 Parent(s): 84f0fc1

Update Chinese model card (README-cn.md)

Browse files

Files changed (1) hide show

README-cn.md +8 -1

README-cn.md CHANGED Viewed

@@ -190,7 +190,9 @@ python -m sglang.launch_server --model-path openbmb/MiniCPM5-1B --port 30000 \
 MiniCPM5-1B 使用**标准 `LlamaForCausalLM` 架构**，主流推理引擎可直接加载，**无需自定义算子，也无模型代码 fork**。逐步部署和微调说明请参考下方 GitHub cookbooks；Agent Skills 作为 GitHub 资源提供给使用 Cursor / Claude Code 类 coding agent 的用户。
-| 后端 / 框架 | 模型格式 / 适用场景 | Cookbook | Agent Skill |
 | --- | --- | --- | --- |
 | Transformers | BF16 / FP16，本地 Python 推理，GPU + CPU | [transformers.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/transformers.md) | [minicpm5-deploy-transformers](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-transformers/SKILL.md) |
 | vLLM | BF16 / FP16 OpenAI server | [vllm.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/vllm.md) | [minicpm5-deploy-vllm](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-vllm/SKILL.md) |
@@ -199,6 +201,11 @@ MiniCPM5-1B 使用**标准 `LlamaForCausalLM` 架构**，主流推理引擎可
 | Ollama | GGUF，本地端侧运行 | [ollama.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/ollama.md) | [minicpm5-deploy-ollama](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-ollama/SKILL.md) |
 | LM Studio | GGUF，Mac 桌面应用与 OpenAI server | [lmstudio.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/lmstudio.md) | [minicpm5-deploy-lmstudio](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-lmstudio/SKILL.md) |
 | MLX | MLX / 4bit，Apple Silicon 本地推理 | [mlx.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/mlx.md) | [minicpm5-deploy-mlx](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-mlx/SKILL.md) |
 | TRL + PEFT | LoRA / SFT 微调 | [trl.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/finetune/trl.md) | [minicpm5-finetune-trl](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-finetune-trl/SKILL.md) |
 | LLaMA-Factory | 微调 | [llamafactory.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/finetune/llamafactory.md) | [minicpm5-finetune-llamafactory](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-finetune-llamafactory/SKILL.md) |
 | ms-swift | 微调 | [ms_swift.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/finetune/ms_swift.md) | [minicpm5-finetune-ms-swift](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-finetune-ms-swift/SKILL.md) |

 MiniCPM5-1B 使用**标准 `LlamaForCausalLM` 架构**，主流推理引擎可直接加载，**无需自定义算子，也无模型代码 fork**。逐步部署和微调说明请参考下方 GitHub cookbooks；Agent Skills 作为 GitHub 资源提供给使用 Cursor / Claude Code 类 coding agent 的用户。
+### 部署
+| 后端 | 模型格式 / 适用场景 | Cookbook | Agent Skill |
 | --- | --- | --- | --- |
 | Transformers | BF16 / FP16，本地 Python 推理，GPU + CPU | [transformers.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/transformers.md) | [minicpm5-deploy-transformers](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-transformers/SKILL.md) |
 | vLLM | BF16 / FP16 OpenAI server | [vllm.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/vllm.md) | [minicpm5-deploy-vllm](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-vllm/SKILL.md) |
 | Ollama | GGUF，本地端侧运行 | [ollama.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/ollama.md) | [minicpm5-deploy-ollama](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-ollama/SKILL.md) |
 | LM Studio | GGUF，Mac 桌面应用与 OpenAI server | [lmstudio.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/lmstudio.md) | [minicpm5-deploy-lmstudio](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-lmstudio/SKILL.md) |
 | MLX | MLX / 4bit，Apple Silicon 本地推理 | [mlx.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/deployment/mlx.md) | [minicpm5-deploy-mlx](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-deploy-mlx/SKILL.md) |
+### 微调
+| 框架 | 适用场景 | Cookbook | Agent Skill |
+| --- | --- | --- | --- |
 | TRL + PEFT | LoRA / SFT 微调 | [trl.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/finetune/trl.md) | [minicpm5-finetune-trl](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-finetune-trl/SKILL.md) |
 | LLaMA-Factory | 微调 | [llamafactory.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/finetune/llamafactory.md) | [minicpm5-finetune-llamafactory](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-finetune-llamafactory/SKILL.md) |
 | ms-swift | 微调 | [ms_swift.md](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/docs/finetune/ms_swift.md) | [minicpm5-finetune-ms-swift](https://github.com/OpenBMB/MiniCPM/blob/minicpm5/skills/minicpm5-finetune-ms-swift/SKILL.md) |