Instructions to use CompressedGemma/Gemma-4-E4B-Opus-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use CompressedGemma/Gemma-4-E4B-Opus-distilled with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="CompressedGemma/Gemma-4-E4B-Opus-distilled", filename="Gemma-4-E4B-Opus.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use CompressedGemma/Gemma-4-E4B-Opus-distilled with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf CompressedGemma/Gemma-4-E4B-Opus-distilled # Run inference directly in the terminal: llama-cli -hf CompressedGemma/Gemma-4-E4B-Opus-distilled
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf CompressedGemma/Gemma-4-E4B-Opus-distilled # Run inference directly in the terminal: llama-cli -hf CompressedGemma/Gemma-4-E4B-Opus-distilled
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 CompressedGemma/Gemma-4-E4B-Opus-distilled # Run inference directly in the terminal: ./llama-cli -hf CompressedGemma/Gemma-4-E4B-Opus-distilled
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 CompressedGemma/Gemma-4-E4B-Opus-distilled # Run inference directly in the terminal: ./build/bin/llama-cli -hf CompressedGemma/Gemma-4-E4B-Opus-distilled
Use Docker
docker model run hf.co/CompressedGemma/Gemma-4-E4B-Opus-distilled
- LM Studio
- Jan
- vLLM
How to use CompressedGemma/Gemma-4-E4B-Opus-distilled with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CompressedGemma/Gemma-4-E4B-Opus-distilled" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CompressedGemma/Gemma-4-E4B-Opus-distilled", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CompressedGemma/Gemma-4-E4B-Opus-distilled
- Ollama
How to use CompressedGemma/Gemma-4-E4B-Opus-distilled with Ollama:
ollama run hf.co/CompressedGemma/Gemma-4-E4B-Opus-distilled
- Unsloth Studio new
How to use CompressedGemma/Gemma-4-E4B-Opus-distilled 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 CompressedGemma/Gemma-4-E4B-Opus-distilled 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 CompressedGemma/Gemma-4-E4B-Opus-distilled to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for CompressedGemma/Gemma-4-E4B-Opus-distilled to start chatting
- Pi new
How to use CompressedGemma/Gemma-4-E4B-Opus-distilled with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf CompressedGemma/Gemma-4-E4B-Opus-distilled
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": "CompressedGemma/Gemma-4-E4B-Opus-distilled" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use CompressedGemma/Gemma-4-E4B-Opus-distilled with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf CompressedGemma/Gemma-4-E4B-Opus-distilled
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 CompressedGemma/Gemma-4-E4B-Opus-distilled
Run Hermes
hermes
- Docker Model Runner
How to use CompressedGemma/Gemma-4-E4B-Opus-distilled with Docker Model Runner:
docker model run hf.co/CompressedGemma/Gemma-4-E4B-Opus-distilled
- Lemonade
How to use CompressedGemma/Gemma-4-E4B-Opus-distilled with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull CompressedGemma/Gemma-4-E4B-Opus-distilled
Run and chat with the model
lemonade run user.Gemma-4-E4B-Opus-distilled-{{QUANT_TAG}}List all available models
lemonade list
Developer note:
This is release candidate 1, it is substantially more powerful than base E4B gemma.
I'm debating whether it is wise to release the tools I used to do this
How I obtained a representation of Claude's Neurons
I started by analyzing the distribution of activations in each model. For each layer, I looked at:
- Where do activations cluster in the hidden state space?
- What input magnitudes are typical?
Though this was a lot harder for Claude since Anthropic does not tell how many layers Claude has.
Comparison with Base Model
| Aspect | Gemma-4-E4B-IT | Gemma-4-E4B-Opus |
|---|---|---|
| Base weights | ✓ | ✓ (preserved) |
| Neuron adapters | ✗ | ✓ (fused) |
| gate/up | Standard | Unmodified |
| down | Standard | Contoured |
| Context | 8192 | 8192 |
| Quantization | BF16 | Q8_0 (~8.0GB) |
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We're not able to determine the quantization variants.