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

Libraries

How to use zaydiscold/gemma-4-E4B-it-OBLITERATED-MLX-4bit 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("zaydiscold/gemma-4-E4B-it-OBLITERATED-MLX-4bit")

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 zaydiscold/gemma-4-E4B-it-OBLITERATED-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 "zaydiscold/gemma-4-E4B-it-OBLITERATED-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": "zaydiscold/gemma-4-E4B-it-OBLITERATED-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zaydiscold/gemma-4-E4B-it-OBLITERATED-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 "zaydiscold/gemma-4-E4B-it-OBLITERATED-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 zaydiscold/gemma-4-E4B-it-OBLITERATED-MLX-4bit

Run Hermes

hermes

MLX LM

How to use zaydiscold/gemma-4-E4B-it-OBLITERATED-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "zaydiscold/gemma-4-E4B-it-OBLITERATED-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "zaydiscold/gemma-4-E4B-it-OBLITERATED-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "zaydiscold/gemma-4-E4B-it-OBLITERATED-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

gemma-4-E4B-it-OBLITERATED — MLX 4-bit

4-bit MLX quantization of OBLITERATUS/gemma-4-E4B-it-OBLITERATED. Fills the gap between existing fp16 and 8-bit MLX versions.


Base	google/gemma-4-E4B-it (4.5B)
Source	OBLITERATUS/gemma-4-E4B-it-OBLITERATED — 0% hard refusal (vs 98.8% stock)
Quant	4-bit MLX \| 3.9 GB disk \| ~4.3 GB RAM
Speed	73.8 tok/s generation on M4 Pro
License	Apache 2.0

Quick Start

pip install mlx-lm

# CLI
mlx_lm generate --model zaydiscold/gemma-4-E4B-it-OBLITERATED-MLX-4bit \
    --prompt "Your prompt here"

from mlx_lm import load, generate
model, tokenizer = load("zaydiscold/gemma-4-E4B-it-OBLITERATED-MLX-4bit")
response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=512)

Recommended: temperature=0.7, top_p=0.9, top_k=40, repeat_penalty=1.1

OBLITERATUS

OBLITERATUS by @elder_plinius removes refusal behavior via whitened SVD, attention head surgery (21/42 layers), winsorized activations, and SVD projection on refusal subspaces. Full methodology on the source model card.