Instructions to use turnercore/minicpm5-automaticity-v7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use turnercore/minicpm5-automaticity-v7 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="turnercore/minicpm5-automaticity-v7",
	filename="MiniCPM5_AUTOMATICITY_V7_Q4.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 turnercore/minicpm5-automaticity-v7 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 turnercore/minicpm5-automaticity-v7
# Run inference directly in the terminal:
llama cli -hf turnercore/minicpm5-automaticity-v7

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf turnercore/minicpm5-automaticity-v7
# Run inference directly in the terminal:
llama cli -hf turnercore/minicpm5-automaticity-v7

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 turnercore/minicpm5-automaticity-v7
# Run inference directly in the terminal:
./llama-cli -hf turnercore/minicpm5-automaticity-v7

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 turnercore/minicpm5-automaticity-v7
# Run inference directly in the terminal:
./build/bin/llama-cli -hf turnercore/minicpm5-automaticity-v7

Use Docker

docker model run hf.co/turnercore/minicpm5-automaticity-v7

LM Studio
Jan
Ollama
How to use turnercore/minicpm5-automaticity-v7 with Ollama:
```
ollama run hf.co/turnercore/minicpm5-automaticity-v7
```

Unsloth Studio

How to use turnercore/minicpm5-automaticity-v7 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 turnercore/minicpm5-automaticity-v7 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 turnercore/minicpm5-automaticity-v7 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for turnercore/minicpm5-automaticity-v7 to start chatting

How to use turnercore/minicpm5-automaticity-v7 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf turnercore/minicpm5-automaticity-v7

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": "turnercore/minicpm5-automaticity-v7"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use turnercore/minicpm5-automaticity-v7 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf turnercore/minicpm5-automaticity-v7

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 turnercore/minicpm5-automaticity-v7

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use turnercore/minicpm5-automaticity-v7 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf turnercore/minicpm5-automaticity-v7

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 "turnercore/minicpm5-automaticity-v7" \
  --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 turnercore/minicpm5-automaticity-v7 with Docker Model Runner:
```
docker model run hf.co/turnercore/minicpm5-automaticity-v7
```

Lemonade

How to use turnercore/minicpm5-automaticity-v7 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull turnercore/minicpm5-automaticity-v7

Run and chat with the model

lemonade run user.minicpm5-automaticity-v7-{{QUANT_TAG}}

List all available models

lemonade list

MiniCPM5 Automaticity V7

Public release of the MiniCPM5 automaticity V7 LoRA and merged GGUF exports.

The training data and benchmark repos are private because future rows/results may contain real tool-call traces. The included HTML report and benchmark dataset repo compare:

MiniCPM5_Base
MiniCPM5_Base + AUTOMATICITY_V7_LORA_ADAPTER
MiniCPM5_AUTOMATICITY_V7_Q4
MiniCPM5_AUTOMATICITY_V7_Q8
FunctionGemma_AUTOMATICITY_V7_Q8

MiniCPM tool-call fine-tuning targets MiniCPM XML function calls rather than JSON.

Automaticity benchmark v1

Frozen local benchmark: /home/turnercore/automaticity-benchmark-v1/automaticity-hard-v1.jsonl

Canonical benchmark repo: turnercore/automaticity-benchmark-v1 (private)

Run	Exact	Tool name	Arguments	No-op recall	p50 latency	p95 latency
FunctionGemma_AUTOMATICITY_V7_Q8	82/92 (89.1%)	96.7%	90.2%	94.7%	180 ms	568 ms
MiniCPM5_Base	78/92 (84.8%)	92.4%	87.0%	86.8%	701 ms	2,070 ms
MiniCPM5_Base + AUTOMATICITY_V7_LORA_ADAPTER	59/92 (64.1%)	67.4%	75.0%	21.1%	316 ms	478 ms
MiniCPM5_AUTOMATICITY_V7_Q8	60/92 (65.2%)	69.6%	75.0%	26.3%	151 ms	248 ms
MiniCPM5_AUTOMATICITY_V7_Q4	54/92 (58.7%)	64.1%	67.4%	13.2%	141 ms	226 ms

The V7 MiniCPM fine-tune is not promoted. It learned the XML output shape and is fast when baked to GGUF, but it overcalls no-op, negated, hypothetical, deferred, and incomplete prompts. AUTOMATICITY_V8 adds no-op hardening rows for the next run.

Downloads last month: 159

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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