Instructions to use Irfanuruchi/Qwen3-4B-Computer-Science-MLX-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Irfanuruchi/Qwen3-4B-Computer-Science-MLX-BF16 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-BF16")

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-BF16 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-BF16"

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-BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Irfanuruchi/Qwen3-4B-Computer-Science-MLX-BF16 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-BF16"

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-BF16

Run Hermes

hermes

OpenClaw new

How to use Irfanuruchi/Qwen3-4B-Computer-Science-MLX-BF16 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-BF16"

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-BF16" \
  --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-BF16 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-BF16"

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-BF16"
# 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-BF16",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3-4B-Computer-Science-MLX-BF16

An Apple MLX BF16 version of Qwen3-4B-Computer-Science, optimized for high-quality local inference on Apple Silicon Macs.

This repository contains a native MLX conversion of the original model using bfloat16 (BF16) precision, providing maximum inference quality while leveraging Apple's unified memory architecture.

Base Model

Base repository: Irfanuruchi/Qwen3-4B-Computer-Science
Architecture: Qwen3-4B
Format: MLX
Precision: BF16 (bfloat16)

Features

Native Apple MLX format
Optimized for Apple Silicon (M-series)
Full BF16 precision
High-quality local inference
Compatible with mlx-lm

Installation

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

pip install mlx mlx-lm

Usage

mlx_lm.generate \
  --model Irfanuruchi/Qwen3-4B-Computer-Science-MLX-BF16 \
  --prompt "Write a Python function that validates an IPv4 address." \
  --max-tokens 256

Model Information

Property	Value
Base Model	Qwen3-4B-Computer-Science
Precision	BF16
Format	MLX
Target Platform	Apple Silicon
Framework	MLX

License

This repository is released under the Apache 2.0 License.

The original Qwen3 model is licensed under Apache 2.0. This repository contains an MLX BF16 conversion of the original weights.

Acknowledgements

Alibaba Qwen Team
Apple MLX
Hugging Face

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Safetensors

Model size

4B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

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