Instructions to use hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8",
	filename="MiniCPM5-1B-Agentic-v8-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8: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 hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8: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 hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M

Use Docker

docker model run hf.co/hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M

LM Studio
Jan
Ollama
How to use hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 with Ollama:
```
ollama run hf.co/hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M
```

Unsloth Studio

How to use hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 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 hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 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 hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 to start chatting

How to use hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8: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": "hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8: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 hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 with Docker Model Runner:
```
docker model run hf.co/hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M
```

Lemonade

How to use hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8:Q4_K_M

Run and chat with the model

lemonade run user.minicpm5-1B-GLM-5.2-Agentic-v8-Q4_K_M

List all available models

lemonade list

MiniCPM5-1B-Agentic-v8

Created by GLM-5.2. Model 8 of 8 in the agentic post-training series.

Variant: 4-way Soup (best overall)

Evaluation

Metric	Score
Real-World Tasks	33.3% (4/12)
Unique Tasks Solved	8/12
Consistent (5/5)	rw_http_server
Often (4/5)	rw_venv_setup

GGUFs available: f16, q8_0, q5_k_m, q4_k_m, q3_k_m, q2_k

Quantization Recommendations

This is a 1B model — heavier quantization degrades output quality significantly.

Quant	Quality	Size	Recommendation
f16	Full	~2.1GB	Best quality
q8_0	Excellent	~1.1GB	Recommended — near-identical to f16
q5_k_m	Good	~0.8GB	Reasoning OK, response may degrade on longer outputs
q4_k_m	Fair	~0.7GB	Reasoning OK, response degrades into repetition
q3_k_m	Poor	~0.6GB	Not recommended
q2_k	Poor	~0.5GB	Not recommended

For production use, prefer q8_0 or f16. The model uses reasoning tokens; lower quantizations break the transition from reasoning to response.

Chat Template

The GGUF chat template defaults to enable_thinking=true, so the model will always produce reasoning followed by response. If your inference engine supports enable_thinking=false, you can skip reasoning for faster responses.

Downloads last month: 1,686

Safetensors

Model size

1B params

Tensor type

BF16

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Model tree for hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8

Base model

openbmb/MiniCPM5-1B

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