Instructions to use nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx") model = AutoModelForMultimodalLM.from_pretrained("nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - MLX
How to use nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx 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("nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx") config = load_config("nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx") # 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) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx
- SGLang
How to use nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Unsloth Desktop
- Pi
How to use nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-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/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx"
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": "nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx with Docker Model Runner:
docker model run hf.co/nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx
- Hermes Agent
How to use nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-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/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-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/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-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/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-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/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-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"
Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx
Brainwaves
arc arc/e boolq hswag obkqa piqa wino
mxfp8 0.665,0.831,0.910,0.790,0.456,0.813,0.772
Quant Perplexity Peak Memory Tokens/sec
mxfp8 5.073 ± 0.038 34.74 GB 212
mxfp4 4.950 ± 0.036 21.30 GB 187
Baseline model
arc arc/e boolq hswag obkqa piqa wino
mxfp8 0.647,0.803,0.910,0.773,0.450,0.806,0.742
qx86-hi 0.637,0.798,0.911,0.775,0.442,0.807,0.737
This model is using the fixed jinja template from froggeric/Qwen-Fixed-Chat-Templates
Thinking toggle
Drop <|think_on|> or <|think_off|> anywhere in your system or user prompt. The template intercepts the tag, removes it from context so the model never sees it, and flips the mode.
Fast answer, no reasoning:
System: You are a coding assistant. <|think_off|>
User: What's 2+2?
Deep reasoning:
System: You are a coding assistant. <|think_on|>
User: Implement a red-black tree in Rust.
The tag syntax (<|think_on|>, <|think_off|>) uses Qwen's control-token delimiters, so it will never collide with real text. Earlier community templates used /think, which broke legitimate paths like cd /mnt/project/think.
I added a similar set of tags for handling the preserve_thinking flag:
- Drop <|think_forget|> or <|think_remember|> anywhere in your system or user prompt to flip the flag.
- The template intercepts the tag, removes it from context so the model never sees it, and flips the mode.
-G
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-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)
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Model tree for nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx
Base model
Qwen/Qwen3.6-27B
docker model run hf.co/nightmedia/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-mxfp4-mlx