Instructions to use AMAImedia/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-heretic-NOESIS-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AMAImedia/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-heretic-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-Cold-Fusion-GAIN-V1.1-heretic-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-Cold-Fusion-GAIN-V1.1-heretic-NOESIS-BF16")
model = AutoModelForMultimodalLM.from_pretrained("AMAImedia/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-heretic-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-Cold-Fusion-GAIN-V1.1-heretic-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-Cold-Fusion-GAIN-V1.1-heretic-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-Cold-Fusion-GAIN-V1.1-heretic-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-Cold-Fusion-GAIN-V1.1-heretic-NOESIS-BF16

SGLang

How to use AMAImedia/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-heretic-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-Cold-Fusion-GAIN-V1.1-heretic-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-Cold-Fusion-GAIN-V1.1-heretic-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-Cold-Fusion-GAIN-V1.1-heretic-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-Cold-Fusion-GAIN-V1.1-heretic-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-Cold-Fusion-GAIN-V1.1-heretic-NOESIS-BF16 with Docker Model Runner:
```
docker model run hf.co/AMAImedia/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-heretic-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: gorbatjovy
Original repository: gorbatjovy/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-heretic

Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-heretic

BF16 abliterated (refusal-removed) build of DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1.

This is DavidAU's Cold-Fusion (GAIN + Unsloth) tune of Qwen3.8-27B with its refusal behaviour removed via Heretic â€” an automated, KL-constrained abliteration. The full vision-language tower and the MTP speculative-decoding head are preserved, so this stays a drop-in for the base model. A W4A16 quantization of these weights is also available.

Credit: all of the model's capability comes from DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1 (itself a finetune of Qwen/Qwen3.8-27B). This repo only removes refusals.

âš ï¸ Disclaimer â€” read before use

This model has had its safety alignment removed via abliteration. As a result it will comply with harmful, unethical, or offensive requests that the base model would refuse. It is released for research â€” interpretability, refusal-mechanism study, red-teaming, and robustness evaluation. You are responsible for what you do with it and for everything it generates. Do not deploy it to end users without adding your own safety and moderation layers. Use must comply with the inherited Apache 2.0 license and all applicable law. The authors accept no liability for misuse.

What was done

Abliteration removes the single "refusal direction" from the residual stream (Arditi et al. 2024, Refusal in Language Models Is Mediated by a Single Direction). Heretic estimates that direction from harmful (AdvBench-style) vs harmless (Alpaca) prompts and runs a 200-trial Optuna search that co-minimizes refusals and KL divergence from the base model, then orthogonalizes the chosen direction out of the residual-writing weights.


Base model	`DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1`
Method	Heretic (automated, KL-constrained) â€” single refusal direction, k=1
Selected trial	direction index 34.95, per-layer ablation kernel from the Optuna search
Coverage	131 residual writers (`self_attn.o_proj`, `linear_attn.out_proj`, `mlp.down_proj`, `embed_tokens`) incl. the MTP head; the 333 `visual.*` tensors are untouched
Format	safetensors, BF16, 1199 tensors
Architecture	`Qwen3_5ForConditionalGeneration` â€” 64 layers, hybrid Gated DeltaNet + full attention, native VL tower, MTP head

Note: Heretic's own export drops the MTP head (transformers has no MTP module for this architecture). It was reinstated from the base checkpoint and abliterated consistently with the main model, so speculative decoding keeps working and the draft head does not re-introduce refusals.

Refusal / quality

Two different rulers, both reported honestly:

Heretic's own keyword-refusal scorer (BF16): 8 / 100 held-out harmful prompts, at KL divergence 0.0315 vs the base model. Untouched Cold-Fusion scores 97 / 100 on the same set. (This is Heretic's internal metric â€” a keyword classifier, not an LLM judge.)
Served-model refusal (opening-phrase classifier), on the W4A16 build: 0% on AdvBench, JailbreakBench (harmful) and MaliciousInstruct.

Capability is essentially retained (KL 0.0315 indicates minimal drift), and the vision tower is byte-for-byte identical to the base, so image understanding is unchanged. As with any abliteration, fine-tuning on refusal-heavy data can partially reintroduce refusals.

Usage (transformers)

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText

model_id = "gorbatjovy/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-heretic"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

messages = [{"role": "user", "content": "Prove that sqrt(2) is irrational."}]
inputs = processor.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True,
    return_dict=True, return_tensors="pt", enable_thinking=True,
).to(model.device)
out = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))