Instructions to use AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16")
model = AutoModelForMultimodalLM.from_pretrained("AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16

SGLang

How to use AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16 with Docker Model Runner:
```
docker model run hf.co/AMAImedia/Qwen3.8-27B-Abliterated-Uncensored-NOESIS-BF16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

NOESIS / AMAImedia

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).

Founder: Ilia Bolotnikov
Organization: AMAImedia.com
X (Twitter): @AMAImediacom
LinkedIn: Ilia Bolotnikov
Telegram: @djbionicl
Release date: 2026-09-03

Release date: 2026-09-03

Original model

Author: KridgeDookie
Original repository: KridgeDookie/Qwen3.8-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS

Qwen3.8 27B - ABLITERATED UNCENSORED PHILADELPHIA CLASS

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

PHILADELPHIA CLASS is a BF16 multimodal derivative of Qwen/Qwen3.8-27B, modified to sharply reduce refusal behavior while retaining the upstream hybrid text-and-vision backbone.

Results

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.

Files

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.

Transformers

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

import torch
from 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.

Ollama

Download a GGUF and place this Modelfile beside it:

FROM ./Q4_K_M-Qwen3.8-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS.gguf
PARAMETER num_ctx 32768

ollama create qwen3.8-27b-philadelphia-class:q4_k_m -f Modelfile
ollama run qwen3.8-27b-philadelphia-class:q4_k_m

Notes

"Uncensored" describes strong refusal reduction; it is not a guarantee for every prompt, language, decoding configuration, quantization, or runtime.
The upstream MTP head is not included. Standard generation and thinking remain available, but MTP-dependent speculative decoding is not supported.
This release does not claim that upstream reasoning, factuality, coding, or vision benchmark scores were preserved unchanged.