Instructions to use DreamFoundries/gemma-4-E2B-it-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DreamFoundries/gemma-4-E2B-it-6bit 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("DreamFoundries/gemma-4-E2B-it-6bit")

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 DreamFoundries/gemma-4-E2B-it-6bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "DreamFoundries/gemma-4-E2B-it-6bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "DreamFoundries/gemma-4-E2B-it-6bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use DreamFoundries/gemma-4-E2B-it-6bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "DreamFoundries/gemma-4-E2B-it-6bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "DreamFoundries/gemma-4-E2B-it-6bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "DreamFoundries/gemma-4-E2B-it-6bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use DreamFoundries/gemma-4-E2B-it-6bit 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 "DreamFoundries/gemma-4-E2B-it-6bit"

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 DreamFoundries/gemma-4-E2B-it-6bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DreamFoundries/gemma-4-E2B-it-6bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "DreamFoundries/gemma-4-E2B-it-6bit"

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 "DreamFoundries/gemma-4-E2B-it-6bit" \
  --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-E2B-it-6bit / README.md

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metadata

library_name: mlx
base_model: google/gemma-4-E2B-it
license: apache-2.0
pipeline_tag: text-generation
tags:
  - mlx
  - mlx-lm
  - quantized
  - 6-bit
  - base_model:google/gemma-4-E2B-it

gemma-4-E2B-it MLX 6-bit

This repository contains an MLX-LM conversion of google/gemma-4-E2B-it.

Conversion Details

Original model: google/gemma-4-E2B-it
Model family: Gemma4
Source model type: gemma4
Model size: 5,123,178,051 parameters
Quantization: MLX-LM affine quantization
Bits: 6-bit
Group size: 64
Local MLX folder size at upload time: 3.55 GiB
Local safetensors weight size at upload time: 3.52 GiB

This Gemma conversion follows the MLX-LM Gemma 4 shared-KV topology and uses non-strict checkpoint loading so extra HF tensors outside that topology are discarded during conversion.

For mlx-swift compatibility, per_layer_model_projection was left unquantized while the rest of the eligible linear layers were quantized.

Usage

mlx_lm.generate --model DreamFoundries/gemma-4-E2B-it-6bit --prompt "Hello" --max-tokens 64

Benchmarks

No comparative benchmarks have been run yet. The repository does not currently provide quality, speed, memory, or benchmark comparisons against the original weights or other quantizations.

License

This is a converted/quantized derivative of the original model. Please refer to the original model repository for the upstream license and usage terms: https://huggingface.co/google/gemma-4-E2B-it