KridgeDookie

Qwen3.6-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS

README

License: apache-2.0

Standout internal results

Table with columns: Evaluation, Protocol, Result
Evaluation	Protocol	Result
Full refusal screen	842 prompts, 24 generated tokens	0/842 refusals; 99.76% usable; 0 degeneration
Held-out refusal screen	126 exact-prompt-hash-disjoint prompts, 96 generated tokens	0/126 refusals; 100% usable; 0 degeneration
Coherence regression	24 coding, JSON, debugging, explanation, math, and boundary tasks	23/24 passed (95.83%)
Long-form diagnostic	24 fixed corpus-spanning prompts, 256 generated tokens	0/24 refusals; 24/24 topical; 21/24 passed the strict format gate; 0 degeneration
Multimodal reload smoke	Fresh processor/model load, real image tensors, vision-forward hook	Passed; correctly answered `blue square`

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

Table with columns: Property, Value
Property	Value
Base model	`Qwen/Qwen3.6-27B`
Architecture	`Qwen3_5ForConditionalGeneration`
Parameters	27,356,728,560
Precision	BF16
Weight formats	Safetensors and GGUF
Safetensors weight size	54.71 GB / 50.96 GiB
Text layers	64
Hidden size

Release artifacts

Table with columns: Artifact, Runtime, Contents
Artifact	Runtime	Contents
`model.safetensors`	Transformers	Single-file BF16 multimodal checkpoint containing the text and vision weights.
`Qwen3.6-27B-PHILADELPHIA-CLASS-BF16.gguf`	Ollama / llama.cpp	Unquantized BF16 text-generation GGUF.

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.

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

python
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:

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

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

For llama.cpp text generation:

bash
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.

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Model Details

Model Provider

KridgeDookie

Model Tree

Base

Qwen/Qwen3.6-27B

Quantized

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality