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

Libraries

How to use ddalcu/gemma-4-e2b-it-4bit-textonly 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("ddalcu/gemma-4-e2b-it-4bit-textonly")

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 ddalcu/gemma-4-e2b-it-4bit-textonly with Pi:

Start the MLX server

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

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": "ddalcu/gemma-4-e2b-it-4bit-textonly"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ddalcu/gemma-4-e2b-it-4bit-textonly 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 "ddalcu/gemma-4-e2b-it-4bit-textonly"

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 ddalcu/gemma-4-e2b-it-4bit-textonly

Run Hermes

hermes

OpenClaw new

How to use ddalcu/gemma-4-e2b-it-4bit-textonly with OpenClaw:

Start the MLX server

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

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 "ddalcu/gemma-4-e2b-it-4bit-textonly" \
  --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 ddalcu/gemma-4-e2b-it-4bit-textonly with MLX LM:

Generate or start a chat session

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

Run an OpenAI-compatible server

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

gemma-4-e2b-it-4bit-textonly

A stripped-down MLX build of Google's Gemma 4 E2B (instruction-tuned), based on mlx-community/gemma-4-e2b-it-4bit, with the vision tower and audio tower weights removed.

Gemma 4 E2B is natively multimodal (text/image/audio), but the vision and audio encoders together account for roughly a quarter of the checkpoint's size while going largely unused in text-only chat deployments. This variant drops both, shrinking the download from 3.55 GB to 2.5 GB (~30% smaller) with no change to the language model's weights or quantization — text generation quality is identical to the source model.

Built for MLX Chat, an iOS app (not yet released) that runs Gemma 4 fully on-device. Apple App Store review flagged the original build's download size, and this app doesn't use image or audio input, so both towers were removed rather than kept unused.

Source: mlx-community/gemma-4-e2b-it-4bit
Removed: all vision_tower.*, embed_vision.*, audio_tower.*, embed_audio.* tensors (1,415 of 2,511 total)
Quantization: unchanged (4-bit affine, language backbone + embeddings, same as source)
config.json: audio_config set to null; vision_config left in place structurally (some MLX loaders instantiate the vision module unconditionally) — load with strict=False if your loader errors on the missing vision weights

What this model can't do

No image or audio input. Text-in, text-out only. If you need Gemma 4's multimodal capabilities, use the upstream mlx-community/gemma-4-e2b-it-4bit instead.

Usage

pip install mlx-vlm
python -m mlx_vlm generate \
  --model ddalcu/gemma-4-e2b-it-4bit-textonly \
  --prompt "Explain what a black hole is in two sentences." \
  --max-tokens 80

If your mlx-vlm version raises a shape error while loading, load with strict=False:

from mlx_vlm import load, generate
model, processor = load("ddalcu/gemma-4-e2b-it-4bit-textonly", strict=False)

Downloads last month: 44

Safetensors

Model size

0.7B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for ddalcu/gemma-4-e2b-it-4bit-textonly

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Quantized

(284)

this model