Instructions to use Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit 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("Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit")

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 Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit"

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": "Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit 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 "Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit"

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 Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit

Run Hermes

hermes

OpenClaw new

How to use Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit"

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 "Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit" \
  --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 Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3-4B-Computer-Science-MLX-8bit / README.md

Irfanuruchi

Release MLX 8-bit model

c942d59 verified 7 days ago

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	---
	license: apache-2.0
	base_model: Irfanuruchi/Qwen3-4B-Computer-Science
	library_name: mlx
	pipeline_tag: text-generation
	tags:
	- mlx
	- apple-silicon
	- qwen3
	- computer-science
	- software-engineering
	- code
	- instruct
	---

	# Qwen3-4B-Computer-Science-MLX-8bit

	An Apple MLX 8-bit quantized version of Qwen3-4B-Computer-Science, optimized for efficient local inference on Apple Silicon Macs.

	This repository provides an MLX-compatible model for fast inference while maintaining excellent quality with significantly reduced memory usage.

	## Base Model

	- Base repository: `Irfanuruchi/Qwen3-4B-Computer-Science`
	- Architecture: Qwen3-4B
	- Format: MLX
	- Quantization: 8-bit
	- Group Size: 64

	## Features

	- Optimized for Apple Silicon (M-series)
	- Fast local inference using MLX
	- Reduced memory footprint
	- Compatible with `mlx-lm`

	## Installation

	```bash
	python3 -m venv .venv
	source .venv/bin/activate

	pip install mlx mlx-lm
	```

	## Usage

	```bash
	mlx_lm.generate \
	--model Irfanuruchi/Qwen3-4B-Computer-Science-MLX-8bit \
	--prompt "Write a Python function that validates an IPv4 address." \
	--max-tokens 256
	```

	## License

	This model is released under the Apache 2.0 License.

	The original Qwen3 model is licensed under Apache 2.0. This repository contains an MLX quantized version of the original weights.

	## Acknowledgements

	- Alibaba Qwen Team
	- Apple MLX
	- Hugging Face