Instructions to use ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF 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 ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-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 ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-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 ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-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": "ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M

Ollama
How to use ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF with Ollama:
```
ollama run hf.co/ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M
```

Unsloth Studio

How to use ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-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 ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-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 ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF to start chatting

How to use ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-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": "ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-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 ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF: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 "ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF: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 ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF with Docker Model Runner:
```
docker model run hf.co/ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M
```

Lemonade

How to use ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-v3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM5-1B-Agentic-Tooluse-v3-GGUF-Q4_K_M

List all available models

lemonade list

ewinregirgojr commited on 2 days ago

Commit

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SEO/GEO: rewrite intro with keyword-rich quotable authoritative content

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@@ -74,12 +74,21 @@ pipeline_tag: text-generation
 ---
-# MiniCPM5-1B-Agentic-Tooluse-v3-GGUF
-**GGUF quantizations of a 1B-parameter agentic tool-calling / function-calling model**, ready to run locally with [llama.cpp](https://github.com/ggerganov/llama.cpp), [Ollama](https://ollama.com/), [LM Studio](https://lmstudio.ai/), koboldcpp, text-generation-webui, or any other GGUF-compatible runtime — fully offline, private, and CPU-friendly.
-Searching for a **local function-calling model**, a **small LLM you can run on CPU or a phone**, a **GGUF model for AI agents**, or a **fast, private alternative to cloud-hosted function calling**? This is built specifically for that.
 ## Why this model
 MiniCPM5-1B-Agentic-Tooluse-v3 is a compact **1B-parameter** model fine-tuned specifically for agentic tool/function calling: it parses a tool schema plus a user request and reliably emits a structured, correctly-named, correctly-valued function call — the core capability behind LangChain agents, MCP servers, ReAct loops, home-automation assistants, and any app that needs an LLM to reliably drive external APIs and tools.

 ---
+# MiniCPM5-1B-Agentic-Tooluse-v3-GGUF — Local Function-Calling LLM (llama.cpp / Ollama / LM Studio)
+**MiniCPM5-1B-Agentic-Tooluse-v3** is a **1-billion-parameter open-weight function-calling model** you can run entirely offline on a CPU — no GPU, no cloud API, no data leaving your machine. It is quantized to GGUF format and works out of the box with [llama.cpp](https://github.com/ggerganov/llama.cpp), [Ollama](https://ollama.com/), [LM Studio](https://lmstudio.ai/), koboldcpp, and text-generation-webui.
+If you are looking for a **local LLM for tool calling**, a **small function-calling model for Raspberry Pi or a laptop**, a **private offline AI agent backbone**, or a **free alternative to GPT-4o / Claude function calling** that runs on your own hardware, this is it.
+> **74.67% exact-argument accuracy** on a held-out 300-example benchmark — trained with QLoRA supervised fine-tuning followed by GRPO reinforcement learning, rewarding exact function-name and argument-value correctness. No GPU required at Q4_K_M.
+## Why this model
+MiniCPM5-1B-Agentic-Tooluse-v3 is fine-tuned specifically to parse a tool schema and a natural-language user request, then emit a structured, correctly-named, correctly-valued function call — the exact skill that powers LangChain agents, LlamaIndex pipelines, AutoGen, CrewAI, MCP tool servers, ReAct loops, and home-automation assistants.
+Unlike most small open tool-calling models that stop at supervised fine-tuning, this model goes further with **GRPO reinforcement learning** on top of the SFT checkpoint, specifically rewarding the two hardest parts of tool calling: choosing the right function name and getting every argument value exactly right.
+**Compared to GPT-4o / Claude for function calling:** this model is 100% free, runs locally, keeps all data private, has zero per-call cost, and is fine-tunable — it trades some absolute accuracy for massive gains in cost, latency, and privacy.
 ## Why this model
 MiniCPM5-1B-Agentic-Tooluse-v3 is a compact **1B-parameter** model fine-tuned specifically for agentic tool/function calling: it parses a tool schema plus a user request and reliably emits a structured, correctly-named, correctly-valued function call — the core capability behind LangChain agents, MCP servers, ReAct loops, home-automation assistants, and any app that needs an LLM to reliably drive external APIs and tools.