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KridgeDookie
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Model Details
Model Provider
KridgeDookie
Model Tree
Qwen/Qwen3.8-27B
Input Modalities
Output Modalities
Supported Functionality
| Internal evaluation | Result |
|---|---|
| Full refusal screen - 842 prompts | 0/842 refusals; 100% usable; 0 degeneration |
| Family-disjoint holdout - 126 prompts, 96 generated tokens | 0/126 refusals; 100% usable; 0 degeneration |
| Coherence regression - coding, JSON, debugging, explanation, math, and boundary tasks | 23/24 passed |
| Long-form diagnostic - 24 prompts, 256 generated tokens | 0/24 refusals; 23/24 usable; 0 degeneration |
| Fresh multimodal reload smoke | Passed; correctly identified a blue square |
These are automated internal development evaluations, not public leaderboards or independent audits. The 842-prompt screen includes the 716 prompts used to fit the transformation; the separate 126-prompt result uses held-out prompt families. "Usable" measures response form and topicality, not factual accuracy. Results above were measured on the BF16 checkpoint with thinking disabled. Quantization and backend changes can affect behavior.
| File | Use |
|---|---|
model.safetensors | Single-file BF16 Transformers checkpoint with text and vision weights |
Q4_K_M-Qwen3.8-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS.gguf | Smaller local text-generation GGUF |
Q5_K_M-Qwen3.8-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS.gguf | Balanced local text-generation GGUF |
Q8_0-Qwen3.8-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS.gguf | Higher-precision local text-generation GGUF |
Use the Safetensors checkpoint for the validated multimodal path. The GGUF files are text-only unless a matching vision projector is explicitly provided.
bash
pip install -U "transformers>=5.14.1" accelerate safetensorspython
import torchfrom transformers import AutoModelForImageTextToText, AutoProcessor model_id = "KridgeDookie/Qwen3.8-27B-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])Qwen3.8 thinking mode remains available by omitting enable_thinking=False or setting it to True. Plan for roughly 56 GB for the BF16 weights, plus runtime and KV-cache overhead.
Download a GGUF and place this Modelfile beside it:
text
FROM ./Q4_K_M-Qwen3.8-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS.ggufPARAMETER num_ctx 32768bash
ollama create qwen3.8-27b-philadelphia-class:q4_k_m -f Modelfileollama run qwen3.8-27b-philadelphia-class:q4_k_mDerived from Qwen/Qwen3.8-27B and released under the Apache License 2.0.
pip install -U "transformers>=5.14.1" accelerate safetensorsimport torchfrom transformers import AutoModelForImageTextToText, AutoProcessor model_id = "KridgeDookie/Qwen3.8-27B-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])ollama create qwen3.8-27b-philadelphia-class:q4_k_m -f Modelfileollama run qwen3.8-27b-philadelphia-class:q4_k_m