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

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

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

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

OpenClaw

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"

gemma-4-e2b-hexagon

File size: 1,452 Bytes

7f02c5a

#!/system/bin/sh
# On-device single PREFILL pass via qnn-net-run against the A16W8 trunk context binary.
#
# Unlike the decode graph this has NO KV in or out: one forward over a fixed SEQ=128 window
# produces the full [1,128,1536] hidden. So verifying it is one net-run per prompt, not a loop.
#
# Layout on device (BASE=/data/local/tmp/gemma):
#   $BASE/bin/qnn-net-run, $BASE/lib/*.so, $BASE/dsp/*.so
#   $BASE/artifacts/gemma4_trunk_a16w8_v79.bin
#   $BASE/tstep/<per-prompt input .raw files>
#   $BASE/tout/                      (net-run output dir -> hidden.raw)
set -e
BASE=/data/local/tmp/gemma
export LD_LIBRARY_PATH=$BASE/lib:/system/lib64:/vendor/lib64
export ADSP_LIBRARY_PATH="$BASE/dsp;/vendor/dsp/cdsp;/vendor/lib/rfsa/cdsp;/vendor/dsp"
export LD_PRELOAD=/system/lib64/libbinder.so   # Android 16 linker fix

STEP=$BASE/tstep
OUT=$BASE/tout
rm -rf "$OUT"; mkdir -p "$OUT"

LINE="inputs_embeds:=$STEP/inputs_embeds.raw"
LINE="$LINE per_layer_inputs:=$STEP/per_layer_inputs.raw"
LINE="$LINE position_ids:=$STEP/position_ids.raw"
LINE="$LINE full_mask:=$STEP/full_mask.raw"
LINE="$LINE sliding_mask:=$STEP/sliding_mask.raw"
echo "$LINE" > "$STEP/in.txt"

"$BASE/bin/qnn-net-run" \
  --backend "$BASE/lib/libQnnHtp.so" \
  --retrieve_context "$BASE/artifacts/gemma4_trunk_a16w8_v79.bin" \
  --input_list "$STEP/in.txt" \
  --output_dir "$OUT" \
  --use_native_input_files \
  --use_native_output_files \
  --log_level error

echo "TRUNK_OK"