Instructions to use AtomicChat/Qwen3.5-9B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AtomicChat/Qwen3.5-9B-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("AtomicChat/Qwen3.5-9B-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 AtomicChat/Qwen3.5-9B-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 "AtomicChat/Qwen3.5-9B-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": "AtomicChat/Qwen3.5-9B-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use AtomicChat/Qwen3.5-9B-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 "AtomicChat/Qwen3.5-9B-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 "AtomicChat/Qwen3.5-9B-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"

MLX LM

How to use AtomicChat/Qwen3.5-9B-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 "AtomicChat/Qwen3.5-9B-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "AtomicChat/Qwen3.5-9B-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": "AtomicChat/Qwen3.5-9B-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use AtomicChat/Qwen3.5-9B-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 "AtomicChat/Qwen3.5-9B-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 AtomicChat/Qwen3.5-9B-MLX-4bit

Run Hermes

hermes

Qwen3.5-9B-MLX-4bit / README.md

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	---
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen3.5-9B/blob/main/LICENSE
	thumbnail: https://huggingface.co/AtomicChat/Qwen3.5-9B-MLX-4bit/resolve/main/hero.png
	base_model:
	- Qwen/Qwen3.5-9B
	base_model_relation: quantized
	quantized_by: AtomicChat
	pipeline_tag: text-generation
	library_name: mlx
	tags:
	- atomic-chat
	- qwen3.5
	- qwen
	- mlx
	- apple-silicon
	- quantized
	---

	<center>

	<div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;">
	<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen3.5-9B-MLX-4bit/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>
	<a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen3.5-9B-MLX-4bit/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a>
	<a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen3.5-9B-MLX-4bit/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>
	</div>

	<br/>

	<img src="https://huggingface.co/AtomicChat/Qwen3.5-9B-MLX-4bit/resolve/main/hero.png" alt="Qwen3.5 9B" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

	<div style="display:flex; justify-content:center; gap:0.5em;">
	<a href="https://huggingface.co/Qwen/Qwen3.5-9B"><strong>Base model: Qwen/Qwen3.5-9B</strong></a>
	</div>
	</center>

	Qwen3.5 9B, self-quantized to MLX by [Atomic Chat](https://atomic.chat). Built straight from Qwen's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

	## Highlights

	- 9.7B parameters: the weights this repo quantizes.
	- Context length: 262,144 tokens (256K), as published by Qwen.
	- 32 layers: Dense decoder.
	- Modalities: Text, Image.
	- Full imatrix ladder: every quant is calibrated with an importance matrix.
	- Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
	- Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.

	> [!NOTE]
	> These MLXs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

	## Model Overview

	\| Property \| Value \|
	\|---\|---\|
	\| Base model \| `Qwen/Qwen3.5-9B` \|
	\| Parameters \| 9.7B \|
	\| Layers \| 32 \|
	\| Context length \| 262,144 tokens (256K) \|
	\| Vocabulary \| 248,320 \|
	\| Modalities \| Text, Image \|
	\| Architecture \| Dense decoder, 16 attention heads over 4 KV heads, `Qwen3_5ForConditionalGeneration` \|
	\| This repo \| MLX weights \|

	## Get started

	- [Atomic Chat](https://atomic.chat): search `AtomicChat/Qwen3.5-9B-MLX-4bit` and hit Use this model.
	- mlx-lm: `mlx_lm.generate --model AtomicChat/Qwen3.5-9B-MLX-4bit --prompt "Hello" --max-tokens 512`
	- Server: `mlx_lm.server --model AtomicChat/Qwen3.5-9B-MLX-4bit --port 8080`

	## Best practices

	\| Parameter \| Value \|
	\|---\|---\|
	\| temperature \| 1.0 \|
	\| top_p \| 0.95 \|
	\| top_k \| 20 \|
	\| min_p \| 0.0 \|
	\| repetition_penalty \| 1.0 \|

	Qwen's recommended sampling configuration for `Qwen/Qwen3.5-9B`.

	## How these were made

	1. Download `Qwen/Qwen3.5-9B` (original weights).
	2. Convert and quantize with `mlx_lm.convert` on our pipeline.

	## License

	Original model by Qwen, released under the Apache 2.0 license. Full terms: [Apache 2.0](https://huggingface.co/Qwen/Qwen3.5-9B/blob/main/LICENSE). Quantized by Atomic Chat.