Instructions to use devopsforflops/delia-functiongemma-270m-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 devopsforflops/delia-functiongemma-270m-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 devopsforflops/delia-functiongemma-270m-gguf:F16
# Run inference directly in the terminal:
llama cli -hf devopsforflops/delia-functiongemma-270m-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf devopsforflops/delia-functiongemma-270m-gguf:F16
# Run inference directly in the terminal:
llama cli -hf devopsforflops/delia-functiongemma-270m-gguf:F16

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 devopsforflops/delia-functiongemma-270m-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf devopsforflops/delia-functiongemma-270m-gguf:F16

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 devopsforflops/delia-functiongemma-270m-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf devopsforflops/delia-functiongemma-270m-gguf:F16

Use Docker

docker model run hf.co/devopsforflops/delia-functiongemma-270m-gguf:F16

LM Studio
Jan
Ollama
How to use devopsforflops/delia-functiongemma-270m-gguf with Ollama:
```
ollama run hf.co/devopsforflops/delia-functiongemma-270m-gguf:F16
```

Unsloth Studio

How to use devopsforflops/delia-functiongemma-270m-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 devopsforflops/delia-functiongemma-270m-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 devopsforflops/delia-functiongemma-270m-gguf to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use devopsforflops/delia-functiongemma-270m-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devopsforflops/delia-functiongemma-270m-gguf:F16

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": "devopsforflops/delia-functiongemma-270m-gguf:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use devopsforflops/delia-functiongemma-270m-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devopsforflops/delia-functiongemma-270m-gguf:F16

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 "devopsforflops/delia-functiongemma-270m-gguf:F16" \
  --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 devopsforflops/delia-functiongemma-270m-gguf with Docker Model Runner:
```
docker model run hf.co/devopsforflops/delia-functiongemma-270m-gguf:F16
```

Lemonade

How to use devopsforflops/delia-functiongemma-270m-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull devopsforflops/delia-functiongemma-270m-gguf:F16

Run and chat with the model

lemonade run user.delia-functiongemma-270m-gguf-F16

List all available models

lemonade list

Hermes Agent

How to use devopsforflops/delia-functiongemma-270m-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 devopsforflops/delia-functiongemma-270m-gguf:F16

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 devopsforflops/delia-functiongemma-270m-gguf:F16

Run Hermes

hermes

Atomic Chat

Delia FunctionGemma 270M - GGUF

This is the GGUF version of delia-functiongemma-270m, fine-tuned for Delia MCP tool orchestration.

Quick Start with Ollama

# Download the GGUF file
wget https://huggingface.co/devopsforflops/delia-functiongemma-270m-gguf/resolve/main/functiongemma-delia-f16.gguf

# Create Modelfile
cat > Modelfile << 'MODELFILE'
FROM ./functiongemma-delia-f16.gguf

TEMPLATE """{{ if .System }}<start_of_turn>developer
{{ .System }}
<end_of_turn>
{{ end }}<start_of_turn>user
{{ .Prompt }}
<end_of_turn>
<start_of_turn>model
"""

PARAMETER stop <end_of_turn>
PARAMETER stop <start_of_turn>
PARAMETER temperature 0.1
PARAMETER num_ctx 2048
MODELFILE

# Import to Ollama
ollama create functiongemma-delia -f Modelfile

# Test it
ollama run functiongemma-delia "Hello!"

Model Details

Property	Value
Base Model	google/functiongemma-270m-it
Architecture	Gemma3
Parameters	268M
Quantization	F16 (full precision)
File Size	~518 MB
Context Length	2048 tokens

Training

Fine-tuned using LoRA on Delia MCP tool calling examples:

LoRA rank: 16
LoRA alpha: 64
Epochs: 20
Dataset: 27 training examples from Delia test suite

Use with Delia

Add to your Delia settings.json:

{
  "model_dispatcher": {
    "name": "functiongemma-delia",
    "num_ctx": 2048
  }
}

Important: The model name must contain "functiongemma" for Delia to apply the correct prompt formatting.

Related Models

delia-functiongemma-270m - Full merged HuggingFace model
delia-functiongemma-270m-lora - LoRA adapter only

License

Apache 2.0

Downloads last month: 14

GGUF

Model size

0.3B params

Architecture

gemma3

Hardware compatibility

16-bit

Inference Providers NEW

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Model tree for devopsforflops/delia-functiongemma-270m-gguf

Base model

google/functiongemma-270m-it

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(54)

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