Instructions to use nightmedia/SuperQwen-AgentWorld-35B-A3B-abliterated-mxfp4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nightmedia/SuperQwen-AgentWorld-35B-A3B-abliterated-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/SuperQwen-AgentWorld-35B-A3B-abliterated-mxfp4-mlx") config = load_config("nightmedia/SuperQwen-AgentWorld-35B-A3B-abliterated-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
- Pi
How to use nightmedia/SuperQwen-AgentWorld-35B-A3B-abliterated-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/SuperQwen-AgentWorld-35B-A3B-abliterated-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/SuperQwen-AgentWorld-35B-A3B-abliterated-mxfp4-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use nightmedia/SuperQwen-AgentWorld-35B-A3B-abliterated-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/SuperQwen-AgentWorld-35B-A3B-abliterated-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/SuperQwen-AgentWorld-35B-A3B-abliterated-mxfp4-mlx
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use nightmedia/SuperQwen-AgentWorld-35B-A3B-abliterated-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/SuperQwen-AgentWorld-35B-A3B-abliterated-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/SuperQwen-AgentWorld-35B-A3B-abliterated-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"
SuperQwen-AgentWorld-35B-A3B-abliterated-mxfp4-mlx
Brainwaves
arc arc/e boolq hswag obkqa piqa wino
mxfp4 0.646,0.838,0.902,0.778,0.444,0.822,0.703
Text only
mxfp4 0.657,0.862,0.906,0.766,0.490,0.825,0.692
Quant Perplexity Peak Memory Tokens/sec
mxfp4 5.286 ± 0.038 25.33 GB
Base model
Qwen-AgentWorld-35B-A3B (VL)
arc arc/e boolq hswag obkqa piqa wino
qx64-hi 0.644,0.818,0.909
mxfp4 0.626,0.813,0.901
Quant Perplexity Peak Memory Tokens/sec
qx64-hi 3.954 ± 0.025 32.86 GB 1311
mxfp4 4.170 ± 0.028 25.33 GB 1599
Qwen-AgentWorld-35B-A3B-Text
arc arc/e boolq hswag obkqa piqa wino
qx64-hi 0.647,0.835,0.909
mxfp4 0.626,0.813,0.901
Quant Perplexity Peak Memory Tokens/sec
mxfp8 4.012 ± 0.026 42.65 GB 1543
qx64-hi 3.973 ± 0.026 32.86 GB 1532
mxfp4 4.170 ± 0.028 25.33 GB 1471
Thinking toggle
This model is using(an early version of) the fixed jinja template from froggeric/Qwen-Fixed-Chat-Templates
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.
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 as <|think_forget|> or <|think_remember|> for handling the preserve_thinking flag.
-G
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("SuperQwen-AgentWorld-35B-A3B-abliterated-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/SuperQwen-AgentWorld-35B-A3B-abliterated-mxfp4-mlx
Base model
Qwen/Qwen3.5-35B-A3B-Base