Instructions to use spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit 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("spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit")

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
LM Studio

Pi new

How to use spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit"

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": "spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit 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 "spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit"

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 spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit

Run Hermes

hermes

MLX LM

How to use spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit"

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-5.4bit"
# 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-5.4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

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 quality+ version, and here's the speed+ version.

Usage

# 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-5.4bit

Benchmarks

metric	mlx-community/ Qwen3.6-35B-A3B-4bit	mlx-community/ Qwen3.6-35B-A3B-4.4bit-msq	4.8 bit	5.4 bit (this model)
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.

Methodology

Quantized with a mlx-lm fork, 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

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Safetensors

Model size

35B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for spicyneuron/Qwen3.6-35B-A3B-MLX-5.4bit

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(405)

this model