Instructions to use deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16 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("deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16")

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

How to use deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16"

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": "deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16 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 "deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16"

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 deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16

Run Hermes

hermes

MLX LM

How to use deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

This model was converted to MLX format from Qwen/Qwen3.5-35B-A3B using:

mlx_lm v0.31.2
specific optimization for M1/M2 Apple Silicon performance, see jundot/omlx/issues/604 for more details and benchmarks
fully fixed chat_template.jinja by @froggeric

mlx_lm.convert --hf-path Qwen/Qwen3.5-35B-A3B --mlx-path deepsweet/Qwen3.5-35B-A3B-MLX-MXFP4-FP16 --quantize --q-mode mxfp4 --q-group-size 32 --dtype float16