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

MiniCPM5-1B-Agentic-Tooluse-v3-GGUF / README.md

ewinregirgojr

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

9d8e3b1 verified 2 days ago

10.5 kB

license: apache-2.0
base_model: openbmb/MiniCPM5-1B
tags:
  - gguf
  - llama.cpp
  - llama-cpp
  - ollama
  - lm-studio
  - minicpm
  - minicpm5
  - minicpm5-1b
  - tool-calling
  - function-calling
  - tool-use
  - agentic
  - agentic-ai
  - ai-agent
  - xml-tool-calling
  - json-function-calling
  - quantized
  - quantization
  - q4_k_m
  - q8_0
  - f16
  - gguf-my-repo
  - small-language-model
  - slm
  - edge-ai
  - on-device
  - local-llm
  - offline-ai
  - privacy
  - openbmb
language:
  - en
pipeline_tag: text-generation

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, Ollama, LM Studio, 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.

Unlike most small open tool-calling models, this one went through a two-stage pipeline: QLoRA supervised fine-tuning followed by GRPO reinforcement learning, specifically rewarding exact function-name and exact argument-value correctness.

Results

Evaluated on a held-out 300-example test slice drawn from a seeded shuffle of ToolACE (see Split integrity). The base-model column is the same model with the same prompt and no adapter.

The published weights are SFT + GRPO (see GRPO / RLVR). The SFT column is kept because every negative result below is measured against it.

metric	v2 (previous release)	SFT retrain (pre-GRPO)	v3 = SFT + GRPO (published)
`parseable` — output is a well-formed call	0.9933	1.0000	1.0000
`valid_name` — name exists among the offered tools	0.9700	0.9867	0.9867
`expected_name` — name matches gold	0.9067	0.9567	0.9533
`args_exact` — every argument value matches gold	0.6133	0.7367	0.7467
`arg_key_overlap` — F1 over argument keys	0.8757	0.9422	0.9388
mean of 5	0.8718	0.9245	0.9251

Column meanings, to avoid the ambiguity the word "baseline" invites: v2 (previous release) = the previously published SFT adapter. An earlier draft of this card mislabeled this column "base model (untrained)" -- that was wrong; it is NOT the raw base model. The real untrained openbmb/MiniCPM5-1B, measured on this same test slice, scores parseable 0.9333, valid_name 0.9133, expected_name 0.8867, args_exact 0.6300, arg_key_overlap 0.8920. SFT retrain = a fresh SFT pass from v2, prior to GRPO. v3 = what this repo currently serves. Every "did it improve?" decision in this card is judged against v2, not against the untrained base model — beating an untrained model is not evidence of anything.

GRPO buys +0.0100 on args_exact, the metric that matters here, and gives back 0.0034 (one test example each) on expected_name and arg_key_overlap. That trade is reported rather than hidden: the mean moves only +0.0006, so this is a targeted gain on the hardest metric, not a broad improvement.

Full 8-metric benchmark (held-out test set, n=300)

This table mirrors the evaluation format from v2 and shows Base, v2, and v3 side-by-side across all 8 metrics using a single consistent harness and held-out test slice:

Metric	Base MiniCPM5-1B	v2 (previous release)	v3 (this model)	Delta (v2 → v3)
parseable_rate	0.0133	0.9933	1.0000	+0.0067
valid_name_rate	0.0133	0.9700	0.9867	+0.0167
expected_name_rate	0.0133	0.9267	0.9533	+0.0267
args_exact_rate	0.1500	0.6533	0.7467	+0.0934
arg_key_overlap	0.0033	0.7517	0.9388	+0.1871
no_schema_copy_rate	1.0000	1.0000	0.9967	-0.0033
no_repetition_rate	0.9967	1.0000	0.3400	-0.6600
stopped_cleanly_rate	0.0000	0.1500	0.0000	-0.1500

What the additional metrics mean:

no_schema_copy_rate — the model did not copy the tool schema's own field description verbatim into an argument value.
no_repetition_rate — the completion did not contain a duplicated function-call block or degenerate repeated-phrase loop. This model has a known weakness here: it often continues generating filler content after the tool call completes. Use a parser that extracts the first completed <function>...</function> block.
stopped_cleanly_rate — the model naturally stopped immediately after the completed </function> tag with no trailing tokens. Use a parser that treats the first completed <function>...</function> block as the action boundary — do not rely on natural end-of-generation.

Available quantizations

File	Quant	Size	Best for
`MiniCPM5-1B-Agentic-Tooluse-v3.F16.gguf`	F16	~2.02 GB	Maximum quality, GPU or high-RAM CPU inference
`MiniCPM5-1B-Agentic-Tooluse-v3.Q8_0.gguf`	Q8_0	~1.07 GB	Near-lossless quality, recommended default for most users
`MiniCPM5-1B-Agentic-Tooluse-v3.Q4_K_M.gguf`	Q4_K_M	~656 MB	Smallest, fastest — best for edge devices, phones, and CPU-only/low-RAM machines

Quickstart

llama.cpp:


./llama-cli -m MiniCPM5-1B-Agentic-Tooluse-v3.Q8_0.gguf -p "Your prompt with tool schema here"

llama-server (OpenAI-compatible API, works with most agent frameworks):


./llama-server -m MiniCPM5-1B-Agentic-Tooluse-v3.Q4_K_M.gguf --port 8080

Ollama:


# Create a Modelfile:

#   FROM ./MiniCPM5-1B-Agentic-Tooluse-v3.Q8_0.gguf

ollama create minicpm5-tooluse-v3 -f Modelfile

ollama run minicpm5-tooluse-v3

LM Studio: just download one of the .gguf files above directly through the LM Studio search/download UI.

Ideal use cases

Fully local / offline / private AI agents (no data leaves your machine)
Home automation and smart-home voice assistants
Mobile, browser-extension, and embedded/IoT tool-calling agents
Cost-sensitive, high-volume backend services that can't afford large-model API costs per call
Drop-in function-calling backbone for LangChain, LlamaIndex, AutoGen, CrewAI, and MCP-based agent stacks
Hobbyist and researcher experimentation with small-model agentic reasoning

FAQ

Which quant should I use? Q8_0 for the best quality-to-size tradeoff on most machines; Q4_K_M if you need the smallest possible footprint or are running on a phone/Raspberry Pi-class device; F16 if you have plenty of RAM/VRAM and want maximum fidelity.

Do I need a GPU? No — that's the point of this model. All three quantizations run well on CPU; a GPU just makes it faster.

How was this trained? QLoRA supervised fine-tuning on tool-calling trajectories, followed by GRPO (Group Relative Policy Optimization) reinforcement-learning refinement targeting exact argument correctness.

Related repos

v3 model family (this release)

Format	Repository
LoRA adapter (PEFT, smallest download, fine-tune further)	MiniCPM5-1B-Agentic-Tooluse-QLoRA-v3
Merged full-weight FP16 (transformers / vLLM / SGLang serving)	MiniCPM5-1B-Agentic-Tooluse-v3-Merged-FP16
GGUF quantizations (llama.cpp / Ollama / LM Studio, CPU-friendly)	MiniCPM5-1B-Agentic-Tooluse-v3-GGUF

Previous releases

Format	Repository
v2 LoRA adapter	MiniCPM5-1B-Agentic-Tooluse-QLoRA-v2
v2 Merged FP16	MiniCPM5-1B-Agentic-Tooluse-Merged-FP16
v2 GGUF	MiniCPM5-1B-Agentic-Tooluse-GGUF

Base model

Built on MiniCPM5-1B by OpenBMB, fine-tuned for agentic tool/function calling and refined with GRPO reinforcement learning.