Instructions to use Irfanuruchi/Qwen2.5-0.5B-Instruct-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Irfanuruchi/Qwen2.5-0.5B-Instruct-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/Qwen2.5-0.5B-Instruct-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/Qwen2.5-0.5B-Instruct-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/Qwen2.5-0.5B-Instruct-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/Qwen2.5-0.5B-Instruct-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Irfanuruchi/Qwen2.5-0.5B-Instruct-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/Qwen2.5-0.5B-Instruct-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/Qwen2.5-0.5B-Instruct-MLX-8bit

Run Hermes

hermes

MLX LM

How to use Irfanuruchi/Qwen2.5-0.5B-Instruct-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/Qwen2.5-0.5B-Instruct-MLX-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Irfanuruchi/Qwen2.5-0.5B-Instruct-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/Qwen2.5-0.5B-Instruct-MLX-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen2.5-0.5B-Instruct (MLX, 8-bit)

This repository contains an MLX-converted and 8-bit quantized version of Qwen/Qwen2.5-0.5B-Instruct.

No fine-tuning or training was performed
Format conversion + post-training quantization only
8-bit prioritizes output stability and quality

Usage

pip install -U mlx-lm

mlx_lm.generate \
  --model Irfanuruchi/Qwen2.5-0.5B-Instruct-MLX-8bit \
  --prompt "Write a helpful onboarding message for an iOS app in 3 bullet points."

Bench notes (MacBook Pro M3 Pro)

Prompt tokens: 45
Generation tokens: 100
Generation speed: ~192.9 tokens/sec
Peak memory: ~0.565 GB

Tooling

mlx-lm: 0.30.2
mlx: bundled with Apple MLX (no public version string)

Related models

4-bit variant (recommended default):
https://huggingface.co/Irfanuruchi/Qwen2.5-0.5B-Instruct-MLX-4bit

Downloads last month: 4

Safetensors

Model size

0.1B params

Tensor type

BF16

U32

MLX

Hardware compatibility

8-bit

Model tree for Irfanuruchi/Qwen2.5-0.5B-Instruct-MLX-8bit

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Quantized

(220)

this model