Instructions to use nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Transformers

How to use nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx

SGLang

How to use nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-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/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx",
    max_seq_length=2048,
)

MLX LM

How to use nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx" --prompt "Once upon a time"

Docker Model Runner
How to use nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx
```

Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-hi-mlx

Brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
qx86-hi  0.545,0.717,0.877,0.717,0.440,0.789,0.653
qx64-hi  0.551,0.726,0.872,0.706,0.444,0.791,0.660
mxfp4    0.530,0.685,0.872,0.705,0.408,0.785,0.642

Qwen3-30B-A3B-YOYO-V2
q8-hi    0.529,0.688,0.885,0.685,0.442,0.783,0.642
qx86-hi  0.531,0.690,0.885,0.685,0.448,0.785,0.646
q6       0.532,0.685,0.886,0.683,0.456,0.782,0.639
mxfp4    0.503,0.636,0.880,0.689,0.428,0.780,0.635

nightmedia/Qwen3-30B-A3B-Element7-1M
qx86-hi  0.578,0.750,0.883,0.742,0.478,0.804,0.684

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3-30B-A3B-YOYO-V2-Claude-4.6-Opus-High-INSTRUCT-qx64-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)