Instructions to use meetara-lab/meetara-gemma4-e2b-it-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 meetara-lab/meetara-gemma4-e2b-it-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 meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M

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 meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M

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 meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M

Use Docker

docker model run hf.co/meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use meetara-lab/meetara-gemma4-e2b-it-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meetara-lab/meetara-gemma4-e2b-it-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meetara-lab/meetara-gemma4-e2b-it-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M

Ollama
How to use meetara-lab/meetara-gemma4-e2b-it-gguf with Ollama:
```
ollama run hf.co/meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M
```

Unsloth Studio

How to use meetara-lab/meetara-gemma4-e2b-it-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 meetara-lab/meetara-gemma4-e2b-it-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 meetara-lab/meetara-gemma4-e2b-it-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for meetara-lab/meetara-gemma4-e2b-it-gguf to start chatting

How to use meetara-lab/meetara-gemma4-e2b-it-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M

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": "meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use meetara-lab/meetara-gemma4-e2b-it-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 meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M

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 meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use meetara-lab/meetara-gemma4-e2b-it-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M

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 "meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M" \
  --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 meetara-lab/meetara-gemma4-e2b-it-gguf with Docker Model Runner:
```
docker model run hf.co/meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M
```

Lemonade

How to use meetara-lab/meetara-gemma4-e2b-it-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull meetara-lab/meetara-gemma4-e2b-it-gguf:Q4_K_M

Run and chat with the model

lemonade run user.meetara-gemma4-e2b-it-gguf-Q4_K_M

List all available models

lemonade list

meetara-gemma4-e2b-it (meeTARA Gemma 4 distribution)

This is a meeTARA-branded export of the official open-weights checkpoint unknown (Gemma 4, instruction-tuned). Gemma-native turn formatting is preserved, and meeTARA default system behavior is injected only when the caller does not provide a system message, so multimodal formatting, tool calling, and thinking modes stay compatible with Transformers and current llama.cpp converters.

me²TARA = Mental & Emotional Empathetic Technology for Adaptive Response & Assistance.

meeTARA welcome and response structure (optional)

This folder includes meetara_default_system_en.txt: the same welcome + structured-response intent as the v3 "simple" ChatML block, in plain text for you to pass as a system (or developer) message in your app or API. The same default content is also mirrored into Gemma-native template metadata so behavior remains present when no explicit system message is provided.

Why not the v3 "enhanced" ChatML template?

convert_to_gguf_meetara_v3_enhanced.py injects a large Qwen-style ChatML chat template. Gemma 4 expects Google's <|turn|> template and special tokens; mixing formats would produce broken prompts. For Gemma, use this script for GGUF packaging; use meetara_default_system_en.txt plus your app, or a Gemma-native fine-tune, if you need meeTARA behavior inside the stack.

GGUF build

Built with repo llama.cpp:

python llama.cpp/convert_hf_to_gguf.py <this_folder> --outfile meetara-gemma4-e2b-it-Q4_K_M.gguf
# optional vision/audio projector:
python llama.cpp/convert_hf_to_gguf.py <this_folder> --mmproj --outfile meetara-vl-gemma4-e2b-it.gguf

License

Apache 2.0 (same as upstream Gemma 4).

Credits

Weights and tokenizer: Google DeepMind (unknown). Packaging: meeTARA project.

Downloads last month: 334

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

View +1 variant

meetara-lab
/

meetara-gemma4-e2b-it-gguf

meetara-gemma4-e2b-it (meeTARA Gemma 4 distribution)

meeTARA welcome and response structure (optional)

Why not the v3 "enhanced" ChatML template?

GGUF build

License

Credits

Space using meetara-lab/meetara-gemma4-e2b-it-gguf 1