---

license: other

base_model: ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-Merged-FP16

library_name: gguf

pipeline_tag: text-generation

tags:

• gguf
• llama.cpp
• llama-cpp
• minicpm
• minicpm5
• minicpm5-1b
• tool-calling
• function-calling
• function-call
• tool-use
• agentic
• xml-tool-calling
• q4_k_m
• q8_0
• f16
• quantized
• vllm
• unsloth
• openbmb
• nemotron
• dpo

datasets:

• Team-ACE/ToolACE

model-index:

• name: MiniCPM5-1B-Agentic-Tooluse-v2

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: 0.9933

name: Parseable tool-call rate

- type: valid_name_rate

value: 0.9700

name: Valid available-tool name rate

- type: expected_name_rate

value: 0.9267

name: Expected tool-name rate

- type: args_exact_rate

value: 0.6533

name: Exact-arguments rate

- type: arg_key_overlap

value: 0.7517

name: Argument-key overlap

- type: no_schema_copy_rate

value: 1.0000

name: No-schema-copy rate

- type: no_repetition_rate

value: 1.0000

name: No-repetition rate

- type: stopped_cleanly_rate

value: 0.1500

name: Stopped-cleanly rate

---

MiniCPM5-1B Agentic Tooluse Nemotron DPO GGUF

Newly Updated and Better Checkpoint (Click Here)

Current GGUF exports of the July 2026 Nemotron SFT+DPO repaired

MiniCPM5-1B-Agentic-Tooluse-Merged-FP16.

These files were generated from the latest evaluated merged checkpoint. The previous warning that this repository had not been regenerated is no longer applicable.

Model Family

| Use case | Repository |

|---|---|

| Current F16, Q8_0, and Q4_K_M GGUF files | This repository |

| Standalone merged Hugging Face model | MiniCPM5-1B-Agentic-Tooluse-Merged-FP16 |

| PEFT/LoRA adapter | MiniCPM5-1B-Agentic-Tooluse-QLoRA-v2 |

Current Files

| File | Size | Recommended use |

|---|---:|---|

| MiniCPM5-1B-Agentic-Tooluse-Nemotron-DPO.F16.gguf | 2.17 GB | Highest-fidelity GGUF and reference conversion |

| MiniCPM5-1B-Agentic-Tooluse-Nemotron-DPO.Q8_0.gguf | 1.15 GB | High fidelity with lower memory use |

| MiniCPM5-1B-Agentic-Tooluse-Nemotron-DPO.Q4_K_M.gguf | 688 MB | Recommended local size/speed balance |

| CONVERSION_METADATA.json | Small | Exact source and converter revisions |

The pre-Nemotron exports are retained under legacy/ for reproducibility. Use the root-level Nemotron-DPO files for the current model.

Provenance

• Base model: openbmb/MiniCPM5-1B
• Current adapter: ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-QLoRA-v2
• Conversion source: ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-Merged-FP16
• Source revision: 67e1cad9b15ffc21d0877bf952d9ece2eeda5d35
• llama.cpp revision: 683f0c72e5b3c07fab90bfd9ec2ce8661d624228
• Quantizations: F16, Q8_0, Q4_K_M

The export pipeline checked each output's GGUF magic, format version, size, and successful completion by llama-quantize. Full task evaluation was performed on the source merged model, not independently on every quantization.

Tool-Call Format

The model emits XML-style calls:

<function name="tool_name"><param name="parameter">value</param></function>

A deployment runtime should:

1. Provide the available tool definitions in the training-compatible prompt format.
2. Decode deterministically for tool selection.
3. Stop after the first complete </function>.
4. Validate the function name and arguments against the supplied schema.
5. Execute the tool outside the model and provide the result in a new turn.

llama.cpp

Use a recent llama.cpp build:

llama-cli \
  -m MiniCPM5-1B-Agentic-Tooluse-Nemotron-DPO.Q4_K_M.gguf \
  -p '<user>Fix the calculator bug.</user>
<tools>{"name":"run_tests","description":"Run the test suite."}</tools>
<calls>' \
  -n 96 \
  --temp 0

The exact chat template and EOS handling matter. If a frontend supplies its own template, verify that it matches the source model's chat_template.jinja.

vLLM

As of July 2026, vLLM documents GGUF support as highly experimental and under-optimized. GGUF support also requires the out-of-tree plugin:

uv pip install vllm vllm-gguf-plugin

Serve Q4_K_M directly from the Hub:

vllm serve \
  ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-GGUF:Q4_K_M \
  --tokenizer ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-Merged-FP16 \
  --hf-config-path ewinregirgojr/MiniCPM5-1B-Agentic-Tooluse-Merged-FP16

The separate tokenizer/config arguments follow vLLM's recommendation to avoid unstable GGUF tokenizer conversion. The merged repository is used because it contains the matching tokenizer, configuration, and chat template.

For stable high-throughput vLLM deployment, prefer the merged safetensors repository when memory permits.

Source-Model Evaluation

External evaluation used 300 examples derived from the untouched

Team-ACE/ToolACE dataset. The same cases and deterministic first-call evaluator were used for the base and fine-tuned source model. This is not an official ToolACE leaderboard submission.

| Metric | Base MiniCPM5-1B | Nemotron repaired source | Delta |

|---|---:|---:|---:|

| Parseable tool call | 0.0133 | 0.9933 | +0.9800 |

| Valid available-tool name | 0.0133 | 0.9700 | +0.9567 |

| Expected tool name | 0.0133 | 0.9267 | +0.9133 |

| Exact arguments | 0.1500 | 0.6533 | +0.5033 |

| Argument-key overlap | 0.0033 | 0.7517 | +0.7484 |

| No schema copying | 1.0000 | 1.0000 | +0.0000 |

| No repetition | 0.9967 | 1.0000 | +0.0033 |

| Clean stop | 0.0000 | 0.1500 | +0.1500 |

These scores establish the behavior of the merged source checkpoint. Quantization can alter individual outputs, so evaluate the exact GGUF and runtime configuration used in production.

Source-Model Improvements and GGUF Scope

These GGUF files were converted from the repaired merged checkpoint, whose reported results improved substantially over base MiniCPM5-1B:

• Parseable calls: 1.33% -> 99.33%
• Valid available-tool names: 1.33% -> 97.00%
• Expected-tool selection: 1.33% -> 92.67%
• Exact arguments: 15.00% -> 65.33%
• Argument-key overlap: 0.33% -> 75.17%
• No repetition: 99.67% -> 100.00%
• Natural clean termination: 0.00% -> 15.00%

These are source FP16 metrics. The remaining gap to 100% is residual error after improvement, not evidence that the fine-tune made the base model worse.

Deployment Notes

• Q4_K_M and Q8_0 have not yet received an independent 300-case ToolACE run, so their exact scores should not be assumed identical to FP16.
• Schema validation and permission checks are universal tool-runtime safety requirements, not model-specific regressions.
• Valid-name and exact-argument behavior improved strongly in the source model; rare errors can still occur on unseen tools.
• MiniCPM5's parser-based serving path extracts the first completed XML call instead of relying solely on natural EOS.
• vLLM GGUF support is experimental; merged safetensors remains the more reliable vLLM format.
• llama.cpp behavior depends on build version and the frontend's chat-template handling.

Version History

The current Nemotron-DPO files are the July 2026 repaired exports. Legacy pre-Nemotron files are stored under legacy/ for reproducibility, and earlier repository states remain available from Hugging Face commit history.

<!-- MINICPM5_RUNTIME_CONTRACT_START -->

MiniCPM5 Tool-Call Runtime Contract

MiniCPM5-1B has native XML-style tool calling. OpenBMB recommends SGLang for tool calling with its built-in minicpm5 parser:

python -m sglang.launch_server \
  --model-path MODEL_PATH \
  --tool-call-parser minicpm5

The parser converts a completed <function ...>...</function> block into an OpenAI-compatible tool_calls response. A production agent should treat the first completed function block as the action boundary, validate it, execute the tool externally, and send the tool result in a new turn.

The reported stopped_cleanly_rate=0.15 is a strict natural-termination metric: only 15% of evaluated generations emitted a completed call and then naturally terminated at that boundary without runtime intervention. It does not mean that only 15% produced usable tool calls. On the same 300 cases, the repaired source model achieved:

• parseable_rate=0.9933
• valid_name_rate=0.9700
• expected_name_rate=0.9267
• no_repetition_rate=1.0000

Therefore, natural EOS-at-boundary remains weak, while first-call extraction and selection are much stronger. Use the official parser where supported and configure bounded generation or parser-side stopping after the first complete </function>.

<!-- MINICPM5_RUNTIME_CONTRACT_END -->

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

Latest v3 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 |

v2 family (this release)

| 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 |

Limitations