Instructions to use nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx 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("nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx")

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 nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx"

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": "nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx 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 "nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx"

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 nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx

Run Hermes

hermes

OpenClaw new

How to use nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx"

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 "nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx" \
  --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 nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "nightmedia/Qwen3.5-35B-A3B-Text-qx86-hi-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3.5-35B-A3B-Text-qx86-hi-mlx

The video part was removed, no other changes.

Brainwaves

         arc   arc/e boolq hswag obkqa piqa  wino
qx86-hi  0.420,0.457,0.379,0.671,0.354,0.777,0.702
qx64-hi  0.413,0.459,0.378,0.670,0.366,0.772,0.687
mxfp4    0.413,0.464,0.378,0.675,0.364,0.771,0.687

Quant    Perplexity     Max Memory
qx86-hi  4.042 ± 0.026  41.52 GB
qx64-hi  4.073 ± 0.026  32.86 GB

Older models with VL

Qwen3-VL-30B-A3B-Instruct
qx86-hi  0.439,0.541,0.894,0.619,0.430,0.764,0.592
qx64-hi  0.454,0.544,0.893,0.618,0.428,0.749,0.590

Qwen3-VL-30B-A3B-Thinking
qx86-hi  0.393,0.466,0.751,0.648,0.366,0.776,0.667

For a thinking model, it performs above the previous VL MoE.

Deckard formula update

This quant has been updated with the new Deckard(qx) formula for Qwen3.5, and includes some attention layers new to the architecture.

While the changes seem minimal for the added weight, the model is more confident, with better attention to detail, and the vibe quality has improved.

Old formula metrics

qx86-hi  0.418,...
qx64-hi  0.419,...

Perplexity
qx86-hi  4.037 ± 0.026   37.46 GB
qx64-hi  4.079 ± 0.026   28.79 GB

More metrics will be available soon

-G

This model Qwen3.5-35B-A3B-Text-qx86-hi-mlx was converted to MLX format from Qwen/Qwen3.5-35B-A3B using mlx-lm version 0.30.8.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3.5-35B-A3B-Text-qx86-hi-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)