Image-Text-to-Text
MLX
Safetensors
English
Chinese
qwen3_5
apple-silicon
qwen3.5
fine tune
heretic
uncensored
abliterated
Merge
thinking
reasoning
creative
writing
fiction
roleplaying
vision
conversational
4-bit precision
Instructions to use pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit 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("pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit") config = load_config("pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit") # 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 pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit"
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": "pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit 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 "pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit"
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 pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit
Run Hermes
hermes
- OpenClaw new
How to use pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit"
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 "pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit" \ --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"
Add 5bit tier to the measured quantization comparison
Browse files
README.md
CHANGED
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@@ -100,6 +100,7 @@ print(generate(model, processor, prompt, ["your_image.jpg"], max_tokens=512, ver
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| [...-MLX-4bit](https://huggingface.co/pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit) | 4 | 6.0 GB |
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| [...-MLX-6bit](https://huggingface.co/pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-6bit) | 6 | 7.7 GB |
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| [...-MLX-8bit](https://huggingface.co/pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-8bit) | 8 | 9.7 GB |
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| [...-MLX-bf16](https://huggingface.co/pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-bf16) | 16 | 18.8 GB |
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| model | lang. weights | ppl | Δppl | KL(bf16‖quant) | top-1 vs bf16 | decode |
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| bf16 reference | 17.91 GB | 8.1273 | — | — | — |
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| 8bit | 9.51 GB | 8.1277 | +0.00% | 0.00124 | 98.24% | 67.
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| 6bit | 7.28 GB | 8.1426 | +0.19% | 0.00523 | 96.20% | 80.
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Reading it:
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- **8bit is effectively free** — bf16 perplexity to four decimals at 47% of the footprint
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and 1.7x the decode speed.
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- **The
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- At the 4-bit tier this affine group-64 quant loses about half the perplexity MXFP4 does,
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costing 4.5 vs 4.25 bits/weight (~6% more storage, ~4% slower decode).
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All tiers are quantizations of the same weights, so this isolates the quantization scheme.
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Reproduce with [`bench.py`](https://github.com/PipeNetwork/defiant-fable-mlx).
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## Sampling
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DavidAU's notes for this model, which carry over:
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| [...-MLX-4bit](https://huggingface.co/pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-4bit) | 4 | 6.0 GB |
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| [...-MLX-6bit](https://huggingface.co/pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-6bit) | 6 | 7.7 GB |
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| [...-MLX-5bit](https://huggingface.co/pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-5bit) | 5 | 6.6 GB |
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| [...-MLX-8bit](https://huggingface.co/pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-8bit) | 8 | 9.7 GB |
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| [...-MLX-bf16](https://huggingface.co/pipenetwork/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-MLX-bf16) | 16 | 18.8 GB |
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| model | lang. weights | ppl | Δppl | KL(bf16‖quant) | top-1 vs bf16 | decode |
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| bf16 reference | 17.91 GB | 8.1273 | — | — | — | 39.2 t/s |
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| 8bit | 9.51 GB | 8.1277 | +0.00% | 0.00124 | 98.24% | 67.9 t/s |
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| 6bit | 7.28 GB | 8.1426 | +0.19% | 0.00523 | 96.20% | 80.9 t/s |
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| 5bit | 6.16 GB | 8.2012 | +0.91% | 0.01845 | 93.24% | 92.5 t/s |
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| **4bit** (this repo) | 5.04 GB | 8.5816 | +5.59% | 0.07330 | 87.06% | 110.0 t/s |
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| [nightmedia mxfp4](https://huggingface.co/nightmedia/Qwen3.5-9B-DS9-USS-Defiant-mxfp4-mlx) | 4.76 GB | 8.9443 | +10.05% | 0.11328 | 82.34% | 114.7 t/s |
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Reading it:
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- **8bit is effectively free** — bf16 perplexity to four decimals at 47% of the footprint
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and 1.7x the decode speed.
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- **5bit is the best quality-per-GB**: under 1% perplexity for 6.2 GB.
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- **The cliff is 5bit → 4bit**, where KL jumps 4x and perplexity goes +0.91% → +5.59%.
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If 4bit feels lossy, 5bit is the tier to move to, not 6bit.
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- At the 4-bit tier this affine group-64 quant loses about half the perplexity MXFP4 does,
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costing 4.5 vs 4.25 bits/weight (~6% more storage, ~4% slower decode).
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All tiers are quantizations of the same weights, so this isolates the quantization scheme.
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Reproduce with [`bench.py`](https://github.com/PipeNetwork/defiant-fable-mlx).
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## Sampling## Sampling
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DavidAU's notes for this model, which carry over:
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