Instructions to use lucaelin/functiongemma-270m-cn-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lucaelin/functiongemma-270m-cn-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lucaelin/functiongemma-270m-cn-gguf",
	filename="functiongemma-270m-cn-bf16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use lucaelin/functiongemma-270m-cn-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lucaelin/functiongemma-270m-cn-gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf lucaelin/functiongemma-270m-cn-gguf:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lucaelin/functiongemma-270m-cn-gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf lucaelin/functiongemma-270m-cn-gguf:BF16

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 lucaelin/functiongemma-270m-cn-gguf:BF16
# Run inference directly in the terminal:
./llama-cli -hf lucaelin/functiongemma-270m-cn-gguf:BF16

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 lucaelin/functiongemma-270m-cn-gguf:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lucaelin/functiongemma-270m-cn-gguf:BF16

Use Docker

docker model run hf.co/lucaelin/functiongemma-270m-cn-gguf:BF16

LM Studio
Jan
Ollama
How to use lucaelin/functiongemma-270m-cn-gguf with Ollama:
```
ollama run hf.co/lucaelin/functiongemma-270m-cn-gguf:BF16
```

Unsloth Studio new

How to use lucaelin/functiongemma-270m-cn-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 lucaelin/functiongemma-270m-cn-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 lucaelin/functiongemma-270m-cn-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lucaelin/functiongemma-270m-cn-gguf to start chatting

Pi new

How to use lucaelin/functiongemma-270m-cn-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lucaelin/functiongemma-270m-cn-gguf:BF16

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": "lucaelin/functiongemma-270m-cn-gguf:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lucaelin/functiongemma-270m-cn-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lucaelin/functiongemma-270m-cn-gguf:BF16

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 lucaelin/functiongemma-270m-cn-gguf:BF16

Run Hermes

hermes

Docker Model Runner
How to use lucaelin/functiongemma-270m-cn-gguf with Docker Model Runner:
```
docker model run hf.co/lucaelin/functiongemma-270m-cn-gguf:BF16
```

Lemonade

How to use lucaelin/functiongemma-270m-cn-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lucaelin/functiongemma-270m-cn-gguf:BF16

Run and chat with the model

lemonade run user.functiongemma-270m-cn-gguf-BF16

List all available models

lemonade list

functiongemma-270m-cn-gguf

Quantized GGUF release for COVAS:NEXT fine-tuning experiments based on google/functiongemma-270m-it.

This release is for tool use only.

It was trained for tool_calling and tool_result_summarization, and it is not a general COVAS:NEXT model for event reactions or contextual Q&A.

Do not use this model as a general conversational ship AI. In the current state it performs poorly on event reaction and contextual question-answering benchmarks.

Files

functiongemma-270m-cn-f32.gguf: validated FP32 GGUF export.
functiongemma-270m-cn-f16.gguf: validated FP16 GGUF export.
functiongemma-270m-cn-bf16.gguf: validated BF16 GGUF export.
functiongemma-270m-cn-q8_0.gguf: validated Q8_0 GGUF export.

Source Run

Training output: mlx/output/sweeps/functiongemma_tc_trs_lr5e5_fullproj_fixed_2120_run1/
Dataset: mlx/data/functiongemma_tc_trs/
Objective: mixed tool_calling + tool_result_summarization
Hyperparameters: LR 5e-5, iters 2120, save interval 200, expanded LoRA target set

Intended Use

Best use: raw tool-call generation in a FunctionGemma-compatible prompt format.
Supported well enough: tool result summarization.
Not supported: event reactions, contextual QA, or broader conversational behavior.

Held-Out Tool Benchmark Snapshot

Evaluated on mlx/data/functiongemma_tc_trs/test.jsonl with the corrected q8 GGUF using the patched local llama-completion path.

Rows: 58
Tool calling attempted: 26/27
Tool calling made: 26/27
Tool calling name correct: 20/27
Tool calling args correct: 20/27
TRS nonempty: 31/31
TRS without tool markers: 31/31

Interpretation:

The final q8_0 GGUF matched the corrected mixed MLX reference on the held-out tool benchmark.
This validation is specific to tool use and tool-result summarization, not to open-ended ship-assistant behavior.

Judge Eval Caveat

The broader 46-case judge-scored benchmark showed that this model is not usable as a general response model:

Overall: 123/276 (44.6%)
Tool calling: 78/108 (72.2%)
Event reaction: 0/42 (0.0%)
Contextual QA: 0/60 (0.0%)
Tool result summarization: 45/66 (68.2%)

That is why this GGUF release should be treated as tool-use-specialized only.

Notes

Export required local FunctionGemma fixes in llama.cpp conversion/runtime handling.
The validated artifacts are the corrected *_fixed exports from the experiment log.
See docs/experiments.md and docs/functiongemma_issues.md in the source project for the full build and validation history.

Downloads last month: 452

GGUF

Model size

0.3B params

Architecture

gemma3

Hardware compatibility

8-bit

16-bit

32-bit

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Model tree for lucaelin/functiongemma-270m-cn-gguf

Base model

google/functiongemma-270m-it

Quantized

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