Instructions to use groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128

SGLang

How to use groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128 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 "groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128" \
    --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": "groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128" \
        --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": "groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128 with Docker Model Runner:
```
docker model run hf.co/groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128
```

Qwen3.6-27B-AEON-Ultimate-Uncensored — GPTQ-Pro FOEM 4-bit g128

Overview

Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128 is a GPTQ-quantized checkpoint intended for efficient GPU inference, published by groxaxo. It is intended for open-source evaluation, reproducible experimentation, and compatible local or hosted inference workflows. The wording below is deliberately limited to what can be verified from this repository's metadata and artifacts.

The repository name identifies a behavior-modified or reduced-filtering lineage. That label describes the source or conversion history; it is not a guarantee of unrestricted behavior in every prompt or runtime. Test outputs carefully before sharing or deploying them.

At a glance

Field	Details
Format	GPTQ
Source / base	`AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16`
Intended task	`image-text-to-text`
License	`apache-2.0`

What is included

*.safetensors (5 files)
config.json
generation_config.json
tokenizer.json
tokenizer_config.json
processor_config.json
chat_template.jinja
quantize_config.json
Additional configuration, tokenizer, processor, or shard files (16 visible artifacts total)

Quick start

vLLM (documented configuration)

vllm serve groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128 \
  --quantization gptq_marlin \
  --dtype float16 \
  --trust-remote-code

This command is taken from the repository documentation. Adjust tensor parallelism, context length, and cache settings to match your hardware and vLLM version.

Compatibility and responsible use

Use a runtime that explicitly supports this format, architecture, and modality.
Keep configuration, tokenizer, processor, projection, and weight files from the same revision together.
Review the source model card and license before redistribution or deployment.
Hardware needs depend on parameter count, context length, cache precision, quantization, and concurrency.
Report reproducible issues with the runtime version, hardware, launch command, and a minimal example.

Quantization or conversion changes numerical behavior, memory use, and throughput relative to the source checkpoint; validate quality on your own workload.

Generated outputs may be inaccurate or unsuitable for a given use case. Users are responsible for testing behavior, applying appropriate safeguards, and complying with applicable licenses and laws.

Quantized version of AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16
using GPTQModel with FOEM (First-Order Error Minimization) enhancement.

Quantization Recipe

Setting	Value
Method	GPTQ-Pro
Bits	4
Group size	128
Symmetric	✅
desc_act	❌
true_sequential	✅
FOEM alpha	0.25
FOEM beta	0.2
activation_weighted_mse	✅
lm_head quantized	❌
Kernel	MarlinLinear (auto)

Quantized: language_model.layers linear modules only
(attn projections, MLP gate/up/down)

Preserved in full BF16:

model.visual.* — entire vision tower (333 tensors)
lm_head.weight
model.language_model.embed_tokens.weight
All norm layers, RoPE, and multimodal glue

Perplexity Comparison (WikiText-2)

Evaluated on identical settings (512 ctx / 256 stride, wikitext-2-raw-v1 test set):

Model	PPL	Δ vs BF16
BF16 baseline	7.6228	—
GPTQ-Pro FOEM 4-bit	7.7447	+0.12 (+1.6%)

Only 1.6% perplexity degradation at 4-bit — an excellent result for W4G128 GPTQ. FOEM + activation-weighted MSE preserved language model fidelity across all 64 transformer layers.

Usage

from gptqmodel import GPTQModel, BACKEND

model = GPTQModel.load(
    "AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128",
    device="cuda:0",
    backend=BACKEND.AUTO,
)

Quantization Details

Tool: GPTQModel v6.1.0-dev
Calibration: 64 samples from WikiText-2
Hardware: 3× RTX 3090/3060 (CUDA_VISIBLE_DEVICES=0,1,2)
Duration: ~112 minutes
gc_mode: on_stage_end (VRAM-safe for large VLMs)
Offload: disk offload enabled during quantization

About the Base Model

This quantization is based on AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16, an uncensored variant of Qwen3 27B with vision capabilities (architecture: Qwen3_5ForConditionalGeneration).

Downloads last month: 1,258

Safetensors

Model size

27B params

Tensor type

BF16

I32

Model tree for groxaxo/Qwen3.6-27B-AEON-Ultimate-Uncensored-GPTQ-Pro-FOEM-4bit-g128

Base model

Qwen/Qwen3.6-27B

Finetuned

AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16

Quantized

(41)

this model