Instructions to use SirSahOl/Qwen3-0.6B-chat-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SirSahOl/Qwen3-0.6B-chat-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("SirSahOl/Qwen3-0.6B-chat-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 SirSahOl/Qwen3-0.6B-chat-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 "SirSahOl/Qwen3-0.6B-chat-mlx-4bit"

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": "SirSahOl/Qwen3-0.6B-chat-mlx-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use SirSahOl/Qwen3-0.6B-chat-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 "SirSahOl/Qwen3-0.6B-chat-mlx-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "SirSahOl/Qwen3-0.6B-chat-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": "SirSahOl/Qwen3-0.6B-chat-mlx-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use SirSahOl/Qwen3-0.6B-chat-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 "SirSahOl/Qwen3-0.6B-chat-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 SirSahOl/Qwen3-0.6B-chat-mlx-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SirSahOl/Qwen3-0.6B-chat-mlx-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "SirSahOl/Qwen3-0.6B-chat-mlx-4bit"

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 "SirSahOl/Qwen3-0.6B-chat-mlx-4bit" \
  --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"

Qwen3-0.6B-mlx-4bit

4-bit MLX conversion of Qwen/Qwen3-0.6B for Apple Silicon.

Converted by: SirSahOl Source model: Qwen/Qwen3-0.6B Framework: MLX by Apple Quantization: 4-bit Format: safetensors License: apache-2.0

Quick Start

Installation

pip install mlx-lm

CLI Usage

# Chat interactively
mlx_lm.chat --model SirSahOl/Qwen3-0.6B-chat-mlx-4bit

# Generate text
mlx_lm.generate --model SirSahOl/Qwen3-0.6B-chat-mlx-4bit --prompt "Your prompt here"

Python Usage

from mlx_lm import load, generate

model, tokenizer = load("SirSahOl/Qwen3-0.6B-chat-mlx-4bit")
response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=256)
print(response)

Performance Benchmarks

| Metric | 4-bit | 8-bit | 16-bit | |--------|--------|--------|--------|| Tokens/sec | 114.11 | 70.97 | 42.97 | | TTFT | 8.77 ms | 14.09 ms | 23.28 ms | | Peak Memory | 449.8 MB | 393.5 MB | 171.6 MB |

Benchmarked on Apple M1 with 8GB unified memory. Average over 5 runs with 256 max tokens.

Who Should Use This?

Your Hardware	Recommended Quantization
M1/M2 (8GB)	4-bit — Best balance of quality and memory usage
M1/M2 Pro/Max (16-32GB)	8-bit — Higher quality with reasonable memory
M2/M3/M4 Ultra (64GB+)	16-bit — Full precision, no quality loss

General guidance:

Use 4-bit if you want to run this model alongside other applications
Use 8-bit if you have the memory and want better quality
Use 16-bit for research, evaluation, or if memory isn't a concern

Other Quantization Variants

Variant	Link
4-bit	SirSahOl/Qwen3-0.6B-chat-mlx-4bit
8-bit	SirSahOl/Qwen3-0.6B-chat-mlx-8bit
16-bit	SirSahOl/Qwen3-0.6B-chat-mlx-16bit

Conversion Details

Property	Value
Source Model	Qwen/Qwen3-0.6B
Quantization	4-bit
mlx-lm Version	0.31.3
Conversion Time	4.66s
Output Size	330.9 MB
Date	2026-09-10T17:45:38.042701+00:00

Reproduction

To reproduce this conversion:

pip install mlx-lm==0.31.3
python3 -m mlx_lm.convert --hf-path Qwen/Qwen3-0.6B --mlx-path output/Qwen3-0.6B-mlx-4bit -q --q-bits 4

Limitations & Known Issues

Performance may degrade with very long contexts (>8K tokens) at lower quantization levels.
This is a weight-only conversion; the model architecture and behavior are inherited from the source model.
Quantization introduces a small quality loss compared to the original model. Lower bit counts = more loss.
This model requires Apple Silicon (M1 or later) to run with MLX.

License

This model conversion inherits the license of the source model: apache-2.0.

See the original model card for full license details.

Changelog

Version	Date	Changes
v1.0	2026-09-10	Initial conversion

Converted with MLX Foundry — a professional pipeline for converting models to Apple MLX format.

Downloads last month: 41

Safetensors

Model size

0.6B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for SirSahOl/Qwen3-0.6B-chat-mlx-4bit

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Quantized

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this model

Collection including SirSahOl/Qwen3-0.6B-chat-mlx-4bit

MLX Models by SirSahOl — Optimized for Apple Silicon

Collection

Curated collection of high-performance MLX model conversions benchmarked and optimized for Apple Silicon (M1/M2/M3/M4 unified memory). • 32 items • Updated 2 days ago