Instructions to use alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ with Transformers:

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

pipe = pipeline("image-text-to-text", model="alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ")
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("alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ")
model = AutoModelForMultimodalLM.from_pretrained("alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ", 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 alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ",
		"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/alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ

SGLang

How to use alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ 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 "alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ" \
    --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": "alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ",
		"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 "alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ" \
        --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": "alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ",
		"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 alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ with Docker Model Runner:
```
docker model run hf.co/alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ
```

Huihui-Qwen3.6-27B-abliterated-AWQ

AWQ W4A16 quantized version of huihui-ai/Huihui-Qwen3.6-27B-abliterated.

This repository is marked as a quantized derivative of the Huihui model via:

base_model:
- huihui-ai/Huihui-Qwen3.6-27B-abliterated
base_model_relation: quantized

Quantization

The model uses native AutoAWQ-style AWQ INT4 weights with FP16 activations:

{
  "quant_method": "awq",
  "bits": 4,
  "group_size": 128,
  "version": "gemm",
  "zero_point": true
}

Additional modules intentionally left unquantized are recorded in config.json under quantization_config.modules_to_not_convert.

Tested Runtime

Validated locally with a modified 1Cat-vLLM build on 4 x Tesla V100-SXM2-32GB:

python -m vllm.entrypoints.openai.api_server \
  --model alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ \
  --quantization awq \
  --dtype float16 \
  --tensor-parallel-size 4 \
  --kv-cache-dtype fp8_e5m2

The tested local server used SM70 AWQ kernels, FLASH_ATTN_V100, and FP8 KV cache. For contexts above the model config limit, vLLM requires VLLM_ALLOW_LONG_MAX_MODEL_LEN=1; use that override only after validating quality/stability for your workload.

Notes

This model inherits the safety/usage characteristics of the upstream abliterated model. The upstream authors describe it as an uncensored/abliterated variant of Qwen3.6-27B and warn that safety filtering is reduced. Review outputs before using in production or public-facing systems.

Base Model

Quantized from: huihui-ai/Huihui-Qwen3.6-27B-abliterated
Original base model referenced by upstream: Qwen/Qwen3.6-27B

Downloads last month: 1,087

Safetensors

Model size

28B params

Tensor type

BF16

I32

Model tree for alexxorm/Huihui-Qwen3.6-27B-abliterated-AWQ

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Quantized

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