Instructions to use Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Cactus-Compute/gemma-4-e2b-it-hybrid-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 Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Cactus-Compute/gemma-4-e2b-it-hybrid-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 Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Cactus-Compute/gemma-4-e2b-it-hybrid-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 Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Cactus-Compute/gemma-4-e2b-it-hybrid-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 Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF with Ollama:
ollama run hf.co/Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF:Q4_K_M
- Unsloth Studio
How to use Cactus-Compute/gemma-4-e2b-it-hybrid-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 Cactus-Compute/gemma-4-e2b-it-hybrid-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 Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF to start chatting
- Pi
How to use Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Cactus-Compute/gemma-4-e2b-it-hybrid-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": "Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Cactus-Compute/gemma-4-e2b-it-hybrid-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 Cactus-Compute/gemma-4-e2b-it-hybrid-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 Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Cactus-Compute/gemma-4-e2b-it-hybrid-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 "Cactus-Compute/gemma-4-e2b-it-hybrid-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 Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF with Docker Model Runner:
docker model run hf.co/Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF:Q4_K_M
- Lemonade
How to use Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.gemma-4-e2b-it-hybrid-GGUF-Q4_K_M
List all available models
lemonade list
Add files using upload-large-folder tool
Browse files- .gitattributes +2 -0
- README.md +56 -0
- gemma-4-e2b-it-hybrid-Q4_K_M.gguf +3 -0
- gemma-4-e2b-it-hybrid-f16.gguf +3 -0
.gitattributes
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---
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license: gemma
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base_model: Cactus-Compute/gemma-4-e2b-it-hybrid
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tags:
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- gguf
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- gemma4
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- hybrid
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- handoff
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---
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# gemma-4-e2b-it-hybrid — GGUF
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GGUF builds of [Cactus-Compute/gemma-4-e2b-it-hybrid](https://huggingface.co/Cactus-Compute/gemma-4-e2b-it-hybrid):
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Gemma 4 E2B instruction-tuned with a built-in **handoff probe** — every
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generation returns a `confidence` score so your app can answer locally when
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the model is likely right and escalate to a bigger model when it isn't.
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## Requirements
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The `gemma-4-e2b-it-hybrid` architecture is not yet in upstream llama.cpp.
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Run these files with a build that includes the Cactus patch series (see the
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[cactus-hybrid-demo](https://github.com/cactus-compute/cactus-hybrid-demo)
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repo, `integrations/llama.cpp/` — `apply.sh` builds it in ~3 minutes, prebuilt
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binaries planned). On unpatched llama.cpp the files fail to load with
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"unknown model architecture" by design.
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```bash
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llama-server -hf Cactus-Compute/gemma-4-e2b-it-hybrid-GGUF --jinja
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```
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Chat-completions responses (and the final SSE chunk when streaming) carry a
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top-level `"confidence"` field. Default handoff threshold: **0.85**; see the
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calibration note below for quantized builds.
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## Files
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| file | quant | size | notes |
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|---|---|---|---|
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| `gemma-4-e2b-it-hybrid-f16.gguf` | F16 | 9.31 GB | closest to the bf16 reference |
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| `gemma-4-e2b-it-hybrid-Q4_K_M.gguf` | Q4_K_M | 3.43 GB | recommended for consumer hardware |
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The probe head (11 `probe.*` tensors) is stored in F32 in **all** quants —
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only the trunk is quantized.
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## Calibration note
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Quantized trunks shift the layer-28 activations the probe reads, moving
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confidences downward relative to the bf16 reference (measured mean drift:
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F16 −0.07, Q4_K_M −0.10; easy-vs-hard ordering fully preserved). If you use
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aggressive thresholds, calibrate per quant; the 0.85 default remains
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conservative (it hands off more, never less).
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## License
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Gemma is provided under and subject to the Gemma Terms of Use. This
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derivative includes the Cactus handoff probe head.
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version https://git-lfs.github.com/spec/v1
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oid sha256:3ff0a132ece77af068ee044994f22b78ec35cc26a30c220858abe61c0f95852a
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size 3428140704
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gemma-4-e2b-it-hybrid-f16.gguf
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size 9311565504
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