Instructions to use ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit")
config = load_config("ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit"

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 ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit

Run Hermes

hermes

OpenClaw new

How to use ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit"

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 "ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

gemma-4-E4B-it-qat-mlx-4bit / README.md

ToPo-ToPo

Add/standardize MTP usage with the model's matching drafter

77f84a7 verified 26 days ago

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metadata

license: gemma
base_model: google/gemma-4-E4B-it-qat-q4_0-unquantized
library_name: mlx
tags:
  - mlx
  - vision
  - gemma
pipeline_tag: image-text-to-text

ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit

MLX 4-bit conversion of google/gemma-4-E4B-it-qat-q4_0-unquantized, made with mlx-vlm 0.6.3.

Provenance (self-converted)

Source: google/gemma-4-E4B-it-qat-q4_0-unquantized (license: gemma)
Tool: mlx-vlm 0.6.3 mlx_vlm.convert (q_bits=4, group_size=64, affine), ~5.2 bpw
Validated: loads and translates correctly under mlx-vlm 0.6.3.

Usage

from mlx_vlm import load
model, processor = load("ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit")

License

Derivative of Google Gemma; governed by the Gemma Terms of Use (https://ai.google.dev/gemma/terms) and Prohibited Use Policy. Converted to MLX.

⚡ Faster generation with MTP (speculative decoding, lossless)

Recommended drafter: google/gemma-4-E4B-it-assistant — Google's official MTP drafter for this model. It loads directly in mlx-vlm (no conversion needed) and gives up to ~3x faster generation (≈1.4–1.5x measured on short prompts); output is identical to non-MTP decoding.

# requires:  pip install "mlx-vlm>=0.6.3"
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

model, processor = load("ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit")
draft_model, _   = load("google/gemma-4-E4B-it-assistant")
config = load_config("ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit")

prompt = apply_chat_template(processor, config, "Hello!", num_images=0)
out = generate(model, processor, prompt,
               draft_model=draft_model, draft_kind="mtp", max_tokens=256)

CLI (draft_kind auto-detected): mlx_vlm.generate --model ToPo-ToPo/gemma-4-E4B-it-qat-mlx-4bit --draft-model google/gemma-4-E4B-it-assistant

Notes

draft_kind="mtp" is required in the Python API (the CLI auto-detects it).
Use this model's own drafter above — drafters are size-specific and not interchangeable across Gemma 4 variants.
Needs mlx-vlm >= 0.6.3. MTP is lossless — if output differs from non-MTP, your versions are mismatched.