Instructions to use SciTools/gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use SciTools/gemma 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 SciTools/gemma:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SciTools/gemma:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SciTools/gemma:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SciTools/gemma: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 SciTools/gemma:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SciTools/gemma: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 SciTools/gemma:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SciTools/gemma:Q4_K_M

Use Docker

docker model run hf.co/SciTools/gemma:Q4_K_M

LM Studio
Jan
Ollama
How to use SciTools/gemma with Ollama:
```
ollama run hf.co/SciTools/gemma:Q4_K_M
```
Unsloth Desktop

How to use SciTools/gemma with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SciTools/gemma:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SciTools/gemma:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SciTools/gemma with Docker Model Runner:
```
docker model run hf.co/SciTools/gemma:Q4_K_M
```

Lemonade

How to use SciTools/gemma with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SciTools/gemma:Q4_K_M

Run and chat with the model

lemonade run user.gemma-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use SciTools/gemma with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SciTools/gemma: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 SciTools/gemma:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SciTools/gemma with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SciTools/gemma: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 "SciTools/gemma: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"

zenpeach commited on 5 days ago

Commit

d4cbec8

verified ·

1 Parent(s): 1ad3dbf

README: describe the QAT E4B file Understand now downloads

Browse files

Files changed (1) hide show

README.md +13 -3

README.md CHANGED Viewed

@@ -5,10 +5,20 @@ license: gemma
 Gemma 4 model weights in GGUF format, used by Understand's AI features and
 served locally by ullama (llama.cpp).
-- `gemma-4-E4B-it-Q4_K_M.gguf` — the quantization qualified in SciTools'
   two-workload evaluation (chat quality 148.6 on the frozen GitAhead scale,
-  3/3 qualifying runs). Ship and use exactly this file: verdicts do not carry
-  across quantizations.
 **Gemma is provided under and subject to the Gemma Terms of Use found at
 [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms).** By

 Gemma 4 model weights in GGUF format, used by Understand's AI features and
 served locally by ullama (llama.cpp).
+- `gemma-4-E4B-it-qat-UD-Q4_K_XL.gguf` — what Understand's Gemma4-E4B choice
+  downloads. Google's quantization-aware-trained (QAT) E4B as quantized by
+  Unsloth, copied unmodified from
+  [unsloth/gemma-4-E4B-it-qat-GGUF](https://huggingface.co/unsloth/gemma-4-E4B-it-qat-GGUF)
+  (SHA-256 `df0fd4ee07072c607c29a0a1cb4f98918426cca12f45a2776bdd6ee6d09a4de3`).
+  Tested against the file below on the same machine and database, it qualified
+  with chat answers closer to the code (0.749 vs 0.727), better instruction
+  following (0.906 vs 0.844), 1.5x faster answers and more accurate overviews
+  (0.669 vs 0.640), in a 3.9 GB download instead of 4.6 GB.
+- `gemma-4-E4B-it-Q4_K_M.gguf` — the earlier E4B choice, qualified in SciTools'
   two-workload evaluation (chat quality 148.6 on the frozen GitAhead scale,
+  3/3 qualifying runs). Kept for installs that already use it.
+Ship and use exactly these files: verdicts do not carry across quantizations.
 **Gemma is provided under and subject to the Gemma Terms of Use found at
 [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms).** By