Qwen3.6 MLX
Collection
5 items • Updated
How to use TheCluster/Qwen3.6-35B-A3B-MLX-mixed-9bit with MLX:
# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
# Load the model
model, processor = load("TheCluster/Qwen3.6-35B-A3B-MLX-mixed-9bit")
config = load_config("TheCluster/Qwen3.6-35B-A3B-MLX-mixed-9bit")
# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."
# Apply chat template
formatted_prompt = apply_chat_template(
processor, config, prompt, num_images=1
)
# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)How to use TheCluster/Qwen3.6-35B-A3B-MLX-mixed-9bit with Pi:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "TheCluster/Qwen3.6-35B-A3B-MLX-mixed-9bit"
# 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": "TheCluster/Qwen3.6-35B-A3B-MLX-mixed-9bit"
}
]
}
}
}# Start Pi in your project directory: pi
How to use TheCluster/Qwen3.6-35B-A3B-MLX-mixed-9bit with Hermes Agent:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "TheCluster/Qwen3.6-35B-A3B-MLX-mixed-9bit"
# 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 TheCluster/Qwen3.6-35B-A3B-MLX-mixed-9bit
hermes

Quality: quantized (mixed quants per tensor, group size: 32, 9.191 bpw)
Most layers use 8-bit affine quantization with a group size 32; some important layers are saved in bf16.
temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0 temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0 temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0 temperature=1.0, top_p=1.0, top_k=40, min_p=0.0, presence_penalty=2.0, repetition_penalty=1.0presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.This model was converted to MLX format from Qwen/Qwen3.6-35B-A3B using mlx-vlm version 0.4.4.
8-bit
Base model
Qwen/Qwen3.6-35B-A3B