library_name: transformers pipeline_tag: image-text-to-text base_model: Qwen/Qwen3.6-27B license: apache-2.0 language:

• multilingual tags:
• qwen
• qwen3.6
• transformers
• safetensors
• bfloat16
• multimodal
• image-text-to-text
• text-generation
• abliterated
• uncensored
• refusal-reduction

Qwen3.6 27B — ABLITERATED UNCENSORED PHILADELPHIA CLASS

0 refusals across an internal 842-prompt opening screen. 0 refusals across a separate 126-prompt, 96-token holdout. 23/24 coherence checks passed.

PHILADELPHIA CLASS is a BF16, 27.36B-parameter multimodal derivative of Qwen/Qwen3.6-27B, modified to sharply reduce refusal behavior while retaining the upstream hybrid text-and-vision architecture.

Standout internal results

These are automated internal development evaluations, not standardized public leaderboards or independent audits. The 842-prompt result includes prompts used during model development; the 126-prompt result excludes the 716 exact prompts used for direction fitting, but it is not a semantic-family holdout. “Usable” describes response form and topicality, not factual correctness or safety.

Model details

Release artifacts

Both distributions use the same validated language-model weights. The Safetensors release retains the upstream vision encoder; the GGUF release is text-only. The GGUF is an unquantized BF16 conversion, not a lower-bit quantization.

Transformers quickstart

Use a recent Transformers build with Qwen3.6 / qwen3_5 support.

pip install -U "transformers>=5.14.1" accelerate safetensors

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "KridgeDookie/Qwen3.6-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
).eval()

messages = [{
    "role": "user",
    "content": [{"type": "text", "text": "Explain why the sky appears blue."}],
}]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
    enable_thinking=False,
).to(model.device)

input_length = inputs["input_ids"].shape[-1]
with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
    )

print(processor.batch_decode(
    output[:, input_length:],
    skip_special_tokens=True,
)[0])

Plan for roughly 56 GB of RAM or VRAM for the BF16 model and vision-projector weights. Runtime overhead, KV cache, and long contexts require additional memory; use multi-GPU placement or CPU offload where necessary.

Ollama and llama.cpp

Download the GGUF file from this repository. For Ollama, create a Modelfile next to it:

FROM ./Qwen3.6-27B-PHILADELPHIA-CLASS-BF16.gguf
PARAMETER num_ctx 32768

ollama create qwen3.6-27b-philadelphia-class:bf16 -f Modelfile
ollama run qwen3.6-27b-philadelphia-class:bf16

For llama.cpp text generation:

llama-cli \
  -m ./Qwen3.6-27B-PHILADELPHIA-CLASS-BF16.gguf \
  -p "Explain why the sky appears blue."

No quantization flag is used in either path. Backend differences can still change behavior relative to the Transformers evaluation above.

Intended use

• Local general-assistant, creative, coding, and multimodal experimentation
• Controlled refusal-behavior and interpretability research
• Red-team evaluation with independent safeguards
• Applications that validate outputs and apply their own policy layer

Limitations

• Reduced refusal behavior can increase harmful, misleading, biased, private, or illegal output.
• “Uncensored” is a release label, not a guarantee about every prompt, language, decoding setting, quantization, or backend.
• The internal evaluations do not establish broad factuality, safety, reasoning, or multimodal benchmark performance.
• The GGUF artifact is text-only; use the Transformers/Safetensors checkpoint for image input.
• The configured 262,144-token context does not guarantee that the full window will fit on a particular system.
• High-impact medical, legal, financial, security, or autonomous decisions require independent review and appropriate controls.

Attribution and license

Derived from Qwen/Qwen3.6-27B and released under the Apache License 2.0. Review the upstream model card and license before deployment or redistribution.