Instructions to use spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit 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("spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit")

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 spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit"

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": "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit 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 "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit"

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 spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit"

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 "spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit" \
  --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"

Qwen3.6-35B-A3B-MLX-4.8bit / README.md

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	---
	library_name: mlx
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen3.6-35B-A3B/blob/main/LICENSE
	pipeline_tag: text-generation
	tags:
	- mlx
	base_model: Qwen/Qwen3.6-35B-A3B
	---

	[Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) optimized for MLX.

	- 4-bit baseline with important layers at 8-bit and BF16.
	- This quant does not support image input.

	I ended up selecting two winners from my trials. This is the speed+ version, and
	here's the [quality+](https://huggingface.co/spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit) version.

	EDIT: Added an [optional chat template patch](https://huggingface.co/spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit/blob/main/chat_template.optional.jinja)
	to improve prompt caching with thinking disabled.

	# Usage

	```sh
	# Start server at http://localhost:8080/v1/chat/completions
	uvx --from mlx-lm mlx_lm.server \
	--host 127.0.0.1 \
	--port 8080 \
	--model spicyneuron/Qwen3.6-35B-A3B-MLX-4.8bit
	```

	# Benchmarks

	metric \| mlx-community/ Qwen3.6-35B-A3B-4bit \| mlx-community/ Qwen3.6-35B-A3B-4.4bit-msq \| 4.8 bit (this model) \| 5.4 bit
	--- \| --- \| --- \| --- \| ---
	bpw \| 4.503 \| 4.787 \| 4.788 \| 5.438
	peak memory (1024/512) \| 20.683 \| 21.922 \| 21.928 \| 24.741
	prompt tok/s (1024) \| 2719.4470 ± 15.2250 \| 2695.9370 ± 12.5260 \| 2734.5260 ± 3.8810 \| 2665.3060 ± 11.4520
	gen tok/s (512) \| 108.4990 ± 0.4910 \| 94.2940 ± 0.3650 \| 97.2820 ± 0.0800 \| 89.4920 ± 0.2610
	kl divergence \| 0.0838 ± 0.0008 \| 0.1689 ± 0.0015 \| 0.0244 ± 0.0004 \| 0.0189 ± 0.0003
	perplexity \| 4.6150 ± 0.0320 \| 4.2490 ± 0.0280 \| 4.6410 ± 0.0320 \| 4.6440 ± 0.0320
	hellaswag \| 0.5560 ± 0.0220 \| 0.5780 ± 0.0220 \| 0.5440 ± 0.0220 \| 0.5370 ± 0.0110
	piqa \| 0.7940 ± 0.0180 \| 0.7920 ± 0.0180 \| 0.7920 ± 0.0180 \| 0.7980 ± 0.0180
	winogrande \| 0.7260 ± 0.0200 \| 0.7400 ± 0.0200 \| 0.7120 ± 0.0200 \| 0.7100 ± 0.0200

	I've moved over to using speed + KL divergence as my primary optimization metrics.
	Hellaswag, PIQA, Winogrande, and perplexity are kept as sanity checks, though these require
	high sample sizes to get usable signal.

	Tested on a Mac Studio M3 Ultra with:

	```
	mlx_lm.convert --hf-path Qwen/Qwen3.6-35B-A3B --mlx-path ./mlx && mlx_lm.kld --baseline-model ./mlx
	mlx_lm.perplexity --sequence-length 512 --seed 123
	mlx_lm.benchmark --prompt-tokens 1024 --generation-tokens 512 --num-trials 5
	mlx_lm.evaluate --tasks hellaswag --seed 123 --num-shots 0 --limit 500
	mlx_lm.evaluate --tasks piqa --seed 123 --num-shots 0 --limit 500
	mlx_lm.evaluate --tasks winogrande --seed 123 --num-shots 0 --limit 500
	```

	`mlx_lm.kld` is still an [open PR](https://github.com/ml-explore/mlx-lm/pull/1146).

	# Methodology

	Quantized with a [mlx-lm fork](https://github.com/ml-explore/mlx-lm/pull/922), drawing inspiration from Unsloth/AesSedai/ubergarm style mixed-precision GGUFs.
	MLX quantization options differ than llama.cpp, but the principles are the same:

	- Sensitive layers like MoE routing, attention, and output embeddings get higher precision
	- More tolerant layers like MoE experts get lower precision