Instructions to use klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP with Transformers:

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

pipe = pipeline("text-generation", model="klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP")
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("klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP")
model = AutoModelForMultimodalLM.from_pretrained("klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP", 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 klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP

SGLang

How to use klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP 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 "klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP" \
    --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": "klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP",
		"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 "klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP" \
        --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": "klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP with Docker Model Runner:
```
docker model run hf.co/klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP
```

Qwen3.8 Flash Next — AutoRound 3 bpw + MTP

Mixed-precision quantization of Qwen/Qwen3.8-Flash-Next: 2/3-bit routed experts (~2.998 effective bpw), 8-bit main projections, BF16 sensitive paths, and 4-bit MTP experts. All 13 safetensors shards are required.

KLD vs original weights: 0.1208.

GitHub: full vLLM patch and setup guide

SSD offloading with vLLM

The 95.37 GiB BF16 PLE table stays on SSD, using asynchronous Linux AIO; loaded model weights use approximately 47.3 GiB VRAM. Tested on a 64 GiB SM80 GPU at 180 W with 15 GiB RAM, 31 GiB swap, and enterprise SATA storage.

Apply the complete patch to vLLM a5a30471ff2bb7f0824f2da10e358af98d304472 and build ple_ssd_io.so. Required additions: metadata-only PLE loading, async row reads/read-ahead, CUDA graph host breaks, mixed-bit expert overrides, and MTP layer remapping. Installation and screen guide uses ~/vllm/.venv.

Performance

Workload	Throughput
Fresh 8K prefill	2,636 prompt tok/s
Single-request decode	111 tok/s
Aggregate output, 16 requests	667 tok/s

MTP=1, 2K prefill chunks. Three-round medians; chat uses 128 output tokens, temperature zero, thinking disabled, and EOS ignored. Aggregate throughput includes prefill; decode excludes first-token latency. Full measurements

Experimental runtime: an arithmetic consistency issue remains. Context is advertised as 262K; varied-token timing was tested through 32K.

License

Qwen Community License 1.0, inherited from the base model.

Downloads last month: 670

Safetensors

Model size

54B params

Tensor type

I64

I32

BF16

F16

Model tree for klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP

Base model

Qwen/Qwen3.8-Flash-Next

Quantized

(273)

this model