---

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

datasets:

• Team-ACE/ToolACE

model-index:

• name: MiniCPM5-1B-Agentic-Tooluse-v3

results:

- task:

type: text-generation

name: Tool calling

dataset:

name: External ToolACE-derived first-call evaluation (held-out 300 examples)

type: Team-ACE/ToolACE

metrics:

- type: parseable_rate

value: 1.0000

name: Parseable tool-call rate

- type: valid_name_rate

value: 0.9867

name: Valid available-tool name rate

- type: expected_name_rate

value: 0.9533

name: Expected tool-name rate

- type: args_exact_rate

value: 0.7467

name: Exact-arguments rate

- type: arg_key_overlap

value: 0.9388

name: Argument-key overlap

- type: no_schema_copy_rate

value: 0.9967

name: No-schema-copy rate

- type: no_repetition_rate

value: 0.3400

name: No-repetition rate

- type: stopped_cleanly_rate

value: 0.0000

name: Stopped-cleanly rate

---

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 |

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.

Base model architecture

MiniCPM5-1B uses a standard LlamaForCausalLM architecture:

| Property | Value |

|---|---|

| Parameters (total) | 1,080,632,832 |

| Parameters (non-embedding) | 679,552,512 |

| Architecture | LlamaForCausalLM |

| Layers | 24 |

| Attention heads (GQA) | 16 Q / 2 KV |

| Context length | 131,072 tokens |

| Training | SFT → RL (GRPO) fine-tune on openbmb/MiniCPM5-1B |

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)

| 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.

Limitations