Instructions to use edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4")
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("edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4", 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 edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4",
		"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/edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4

SGLang

How to use edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4 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 "edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4" \
    --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": "edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4",
		"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 "edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4" \
        --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": "edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4",
		"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 edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4 with Docker Model Runner:
```
docker model run hf.co/edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4
```

Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4

Community model combining RadixArk NVFP4 with changes extracted from Huihui’s abliterated GGUF. Distributed as safetensors, with RadixArk’s original MTP, vision weights and tokenizer retained.

Tested

TP1 / one DGX Spark: 64K context setting; basic text, code, tool-call and image checks passed.
TP2 / two DGX Sparks: 1M context setting with runtime YaRN ×4; basic checks and a 66K-token retrieval request passed. Full 1M input quality was not tested.

Changes were recovered from quantized GGUF weights, so this is not an exact Huihui BF16 reconstruction. Original activation scales were retained without recalibration. Comprehensive quality and refusal-removal benchmarks remain untested.

Source revisions and weight hashes

Sources and license

Qwen Community License 1.0. Credits: Qwen, RadixArk, Huihui, Unsloth, NVIDIA ModelOpt, and llama.cpp.

Downloads last month: 463

Safetensors

Model size

120B params

Tensor type

BF16

I64

F8_E4M3

Model tree for edp1096/Huihui-RadixArk-Qwen3.8-Flash-Next-abliterated-NVFP4

Base model

Qwen/Qwen3.8-Flash-Next

Quantized

RadixArk/Qwen3.8-Flash-Next-NVFP4

Quantized

(7)

this model