Instructions to use h2loop-ai/gemma-4-e2b-hexagon 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 h2loop-ai/gemma-4-e2b-hexagon 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 h2loop-ai/gemma-4-e2b-hexagon # Run inference directly in the terminal: llama cli -hf h2loop-ai/gemma-4-e2b-hexagon
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf h2loop-ai/gemma-4-e2b-hexagon # Run inference directly in the terminal: llama cli -hf h2loop-ai/gemma-4-e2b-hexagon
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 h2loop-ai/gemma-4-e2b-hexagon # Run inference directly in the terminal: ./llama-cli -hf h2loop-ai/gemma-4-e2b-hexagon
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 h2loop-ai/gemma-4-e2b-hexagon # Run inference directly in the terminal: ./build/bin/llama-cli -hf h2loop-ai/gemma-4-e2b-hexagon
Use Docker
docker model run hf.co/h2loop-ai/gemma-4-e2b-hexagon
- LM Studio
- Jan
- Ollama
How to use h2loop-ai/gemma-4-e2b-hexagon with Ollama:
ollama run hf.co/h2loop-ai/gemma-4-e2b-hexagon
- Unsloth Studio
How to use h2loop-ai/gemma-4-e2b-hexagon 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 h2loop-ai/gemma-4-e2b-hexagon 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 h2loop-ai/gemma-4-e2b-hexagon to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for h2loop-ai/gemma-4-e2b-hexagon to start chatting
- Pi
How to use h2loop-ai/gemma-4-e2b-hexagon with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf h2loop-ai/gemma-4-e2b-hexagon
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": "h2loop-ai/gemma-4-e2b-hexagon" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use h2loop-ai/gemma-4-e2b-hexagon with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf h2loop-ai/gemma-4-e2b-hexagon
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 "h2loop-ai/gemma-4-e2b-hexagon" \ --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 h2loop-ai/gemma-4-e2b-hexagon with Docker Model Runner:
docker model run hf.co/h2loop-ai/gemma-4-e2b-hexagon
- Lemonade
How to use h2loop-ai/gemma-4-e2b-hexagon with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull h2loop-ai/gemma-4-e2b-hexagon
Run and chat with the model
lemonade run user.gemma-4-e2b-hexagon-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use h2loop-ai/gemma-4-e2b-hexagon with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf h2loop-ai/gemma-4-e2b-hexagon
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 h2loop-ai/gemma-4-e2b-hexagon
Run Hermes
hermes
- Atomic Chat
| # Stage this repo onto an adb-attached Snapdragon device. | |
| # | |
| # The QNN runtime (qnn-net-run + libQnnHtp*.so + the HTP skel/stub for your hexagon version) | |
| # is NOT in this repo -- it ships with Qualcomm's AI Engine Direct (QAIRT) SDK and is not | |
| # redistributable. Point QAIRT_DIR at your SDK install, or pre-stage those files yourself. | |
| # | |
| # QAIRT_DIR=/path/to/qairt/2.45.0.xxxxxx ./stage_device.sh <serial> [v79|v81] | |
| # | |
| # Note the KV buffers are NOT pushed: run_gate.py creates them on device with dd, because | |
| # pushing ~144 MB of zeros over a slow adb link was the flakiest part of the pipeline. | |
| set -euo pipefail | |
| SERIAL="${1:?usage: stage_device.sh <adb-serial> [v79|v81]}" | |
| HTP="${2:-v79}" | |
| REPO="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" | |
| BASE=/data/local/tmp/gemma | |
| ADB=(adb -s "$SERIAL") | |
| case "$HTP" in | |
| v79) SOC_ARCH=v79 ;; | |
| v81) SOC_ARCH=v81 ;; | |
| *) echo "unknown HTP '$HTP' (expected v79 or v81)" >&2; exit 1 ;; | |
| esac | |
| echo ">> device: $("${ADB[@]}" shell getprop ro.soc.model | tr -d '\r') (staging for HTP $SOC_ARCH)" | |
| "${ADB[@]}" shell "mkdir -p $BASE/bin $BASE/lib $BASE/dsp $BASE/artifacts $BASE/step $BASE/kv $BASE/out $BASE/tstep $BASE/tout" | |
| # ---- QNN runtime (from your QAIRT SDK) ---------------------------------------------- | |
| if [ -n "${QAIRT_DIR:-}" ]; then | |
| echo ">> pushing QNN runtime from QAIRT_DIR=$QAIRT_DIR" | |
| AA="$QAIRT_DIR/lib/aarch64-android" | |
| HX="$QAIRT_DIR/lib/hexagon-$SOC_ARCH/unsigned" | |
| [ -d "$AA" ] || { echo "missing $AA" >&2; exit 1; } | |
| [ -d "$HX" ] || { echo "missing $HX -- your SDK may not include hexagon-$SOC_ARCH" >&2; exit 1; } | |
| "${ADB[@]}" push "$QAIRT_DIR/bin/aarch64-android/qnn-net-run" "$BASE/bin/" >/dev/null | |
| "${ADB[@]}" shell "chmod 755 $BASE/bin/qnn-net-run" | |
| for f in libQnnHtp.so libQnnSystem.so libQnnHtpPrepare.so libQnnHtpNetRunExtensions.so \ | |
| "libQnnHtp${SOC_ARCH^^}Stub.so"; do | |
| [ -f "$AA/$f" ] && "${ADB[@]}" push "$AA/$f" "$BASE/lib/" >/dev/null | |
| done | |
| for f in "$HX"/libQnnHtp*.so; do "${ADB[@]}" push "$f" "$BASE/dsp/" >/dev/null; done | |
| else | |
| echo ">> QAIRT_DIR not set -- skipping QNN runtime." | |
| echo " You must stage these yourself under $BASE:" | |
| echo " bin/qnn-net-run" | |
| echo " lib/libQnnHtp.so libQnnSystem.so libQnnHtpPrepare.so libQnnHtpNetRunExtensions.so libQnnHtp${SOC_ARCH^^}Stub.so" | |
| echo " dsp/libQnnHtp${SOC_ARCH^^}.so libQnnHtp${SOC_ARCH^^}Skel.so" | |
| fi | |
| # ---- context binaries --------------------------------------------------------------- | |
| echo ">> pushing context binaries (~1.9 GB each, be patient)" | |
| for b in "$REPO"/*"_$HTP.bin"; do | |
| [ -f "$b" ] || continue | |
| echo " $(basename "$b")" | |
| "${ADB[@]}" push "$b" "$BASE/artifacts/" >/dev/null | |
| done | |
| # ---- on-device step scripts --------------------------------------------------------- | |
| for s in gate_ondevice_wgqa.sh gate_ondevice_int8kv.sh gate_ondevice_trunk.sh; do | |
| "${ADB[@]}" push "$REPO/runtime/$s" "$BASE/" >/dev/null | |
| "${ADB[@]}" shell "chmod 755 $BASE/$s" | |
| done | |
| echo ">> staged:" | |
| "${ADB[@]}" shell "ls -la $BASE/artifacts $BASE/bin $BASE/lib $BASE/dsp" | |
| echo ">> OK" | |