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.

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:

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

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.

Base model architecture

MiniCPM5-1B uses a standard LlamaForCausalLM architecture:

Thinking mode

MiniCPM5-1B has a built-in <think>...</think> chat template. The same checkpoint can act as a fast assistant or a deliberate chain-of-thought reasoner — controlled by a single flag:

# Fast mode — recommended for tool calling (thinking OFF)
prompt = tokenizer.apply_chat_template(
    messages, tools=tools, add_generation_prompt=True,
    enable_thinking=False,
    tokenize=False,
)

# Reasoning mode (thinking ON — NOT recommended for tool calling)
prompt = tokenizer.apply_chat_template(
    messages, tools=tools, add_generation_prompt=True,
    enable_thinking=True,
    tokenize=False,
)

Important: always use enable_thinking=False for tool/function calling. With thinking ON the model spends its token budget inside <think>...</think> and may not reach a completed function call. All benchmark numbers in this card use thinking OFF.

Citation

If you use this model, please cite the base model paper:

@article{minicpm4,
  title   = {MiniCPM4: Ultra-Efficient LLMs on End Devices},
  author  = {MiniCPM Team},
  journal = {arXiv preprint arXiv:2506.07900},
  year    = {2025}
}

And the ToolACE dataset used for fine-tuning:

@article{toolace,
  title   = {ToolACE: Winning the Points of LLM Function Calling},
  author  = {Liu, Ying and others},
  journal = {arXiv preprint arXiv:2409.00920},
  year    = {2024}
}

ModelScope

The base model is also available on ModelScope (for users in China and East Asia):

• OpenBMB/MiniCPM5-1B on ModelScope

(The fine-tuned adapter/GGUF builds are currently HuggingFace-only.)

Related repos

v3 model family (this release)

Previous releases

Base model

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

Limitations