Instructions to use mouri45/gemma-4-e2b-it-lite-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mouri45/gemma-4-e2b-it-lite-mlx with MLX:

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

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mouri45/gemma-4-e2b-it-lite-mlx")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use mouri45/gemma-4-e2b-it-lite-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mouri45/gemma-4-e2b-it-lite-mlx"

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": "mouri45/gemma-4-e2b-it-lite-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mouri45/gemma-4-e2b-it-lite-mlx 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 "mouri45/gemma-4-e2b-it-lite-mlx"

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 mouri45/gemma-4-e2b-it-lite-mlx

Run Hermes

hermes

OpenClaw new

How to use mouri45/gemma-4-e2b-it-lite-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mouri45/gemma-4-e2b-it-lite-mlx"

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 "mouri45/gemma-4-e2b-it-lite-mlx" \
  --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"

MLX LM

How to use mouri45/gemma-4-e2b-it-lite-mlx with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mouri45/gemma-4-e2b-it-lite-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mouri45/gemma-4-e2b-it-lite-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mouri45/gemma-4-e2b-it-lite-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

gemma-4-e2b-it-lite-mlx

Text-only, lightweight 4-bit MLX quantization of google/gemma-4-e2b-it, optimized for on-device inference on iPhone (8GB devices) and Apple Silicon Macs.

~2.05 GB — compared to 3.58 GB for the official mlx-community/gemma-4-e2b-it-4bit (which bundles the audio/vision towers in BF16).

What was changed (modification notice)

Converted from the original bf16 checkpoint with mlx-lm 0.31.3 (mlx_lm.convert).
Text-only: audio_tower, vision_tower, embed_audio, embed_vision, multi_modal_projector weights are not included. This checkpoint works with text-only Gemma 4 runtimes (e.g. mlx-lm / mlx-swift-lm MLXLLM).
Quantization recipe: 4-bit / group size 64 (affine) for all layers, except embed_tokens_per_layer (per-layer embeddings, ~1.3 GB at 4-bit) which is quantized to 2-bit / group size 64. The per-layer override is recorded in config.json (quantization section).
KV-shared layers (15-34) do not include k_proj / v_proj / k_norm (same layout as the official MLX conversion).

Japanese smoke tests (greeting / Q&A / no repetition loops) show quality on par with the official 4-bit conversion on this recipe. A uniform 3-bit recipe of the same size was clearly worse and was rejected.

Attribution

Gemma 4 is developed by Google DeepMind and released under the Apache License 2.0. This repository redistributes a quantized derivative under the same license.

Usage (mlx-lm)

pip install mlx-lm
mlx_lm.generate --model mouri45/gemma-4-e2b-it-lite-mlx --prompt "こんにちは!"

Created as part of the AppleSiliconLLM project (Issue0012).

Downloads last month: -

Safetensors

Model size

0.6B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit