Instructions to use tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ with Transformers:

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

pipe = pipeline("image-text-to-text", model="tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ")
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("tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ")
model = AutoModelForMultimodalLM.from_pretrained("tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ", 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 tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ",
		"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/tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ

SGLang

How to use tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ 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 "tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ" \
    --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": "tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ",
		"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 "tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ" \
        --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": "tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ",
		"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 tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ with Docker Model Runner:
```
docker model run hf.co/tcclaviger/Qwen3.8-Flash-Next-MXFP4-FP8-GPTQ
```

Robert Smith commited on 16 days ago

Commit

390783e

unverified ·

1 Parent(s): d64bb4c

Updated gptq algo to yield better results, repacked safetensors

Browse files

This view is limited to 50 files because it contains too many changes. See raw diff

Files changed (50) hide show

aa_report.json +919 -0
model-00000.safetensors +2 -2
model-00021.safetensors → model-00001.safetensors +2 -2
model-00022.safetensors → model-00002.safetensors +2 -2
model-mtp-lmhead-int4.safetensors → model-00003.safetensors +2 -2
model-ple-int6-000.safetensors → model-00004.safetensors +2 -2
model-00005.safetensors +3 -0
model-00006.safetensors +3 -0
model-00007.safetensors +3 -0
model-00008.safetensors +3 -0
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model-00010.safetensors +2 -2
model-00011.safetensors +2 -2
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model-ple-int6-026.safetensors +0 -3
model-ple-int6-027.safetensors +0 -3
model-ple-int6-028.safetensors +0 -3

aa_report.json ADDED Viewed

	@@ -0,0 +1,919 @@

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