Instructions to use z-lab/Qwen3-4B-DFlash-b16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use z-lab/Qwen3-4B-DFlash-b16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="z-lab/Qwen3-4B-DFlash-b16", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("z-lab/Qwen3-4B-DFlash-b16", trust_remote_code=True) model = AutoModel.from_pretrained("z-lab/Qwen3-4B-DFlash-b16", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use z-lab/Qwen3-4B-DFlash-b16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "z-lab/Qwen3-4B-DFlash-b16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "z-lab/Qwen3-4B-DFlash-b16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/z-lab/Qwen3-4B-DFlash-b16
- SGLang
How to use z-lab/Qwen3-4B-DFlash-b16 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 "z-lab/Qwen3-4B-DFlash-b16" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "z-lab/Qwen3-4B-DFlash-b16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "z-lab/Qwen3-4B-DFlash-b16" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "z-lab/Qwen3-4B-DFlash-b16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use z-lab/Qwen3-4B-DFlash-b16 with Docker Model Runner:
docker model run hf.co/z-lab/Qwen3-4B-DFlash-b16
Add diffusers model_index.json and scheduler config for DFlashPipeline.from_pretrained
Browse filesThis adds the two files needed for `diffusers` to load the pipeline directly via `DFlashPipeline.from_pretrained`:
- `model_index.json` — declares `DFlashPipeline` as the pipeline class and points
`_target_model_name_or_path` to the target/verifier model
- `scheduler/scheduler_config.json` — config for `DFlashTokenDiffusionScheduler`
After merging, the pipeline can be loaded with:
```python
import torch
from diffusers import DFlashPipeline
pipe = DFlashPipeline.from_pretrained(
"z-lab/Qwen3-4B-DFlash-b16",
torch_dtype=torch.bfloat16,
device_map="auto",
)
out = pipe(prompt="Hello, world!")
print(out.texts[0])
```
- model_index.json +9 -0
- scheduler/scheduler_config.json +4 -0
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{
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"_class_name": "DFlashPipeline",
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"_diffusers_version": "0.40.0.dev0",
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"_target_model_name_or_path": "Qwen/Qwen3-4B",
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"scheduler": [
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"diffusers",
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"DFlashTokenDiffusionScheduler"
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]
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}
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{
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"_class_name": "DFlashTokenDiffusionScheduler",
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"_diffusers_version": "0.40.0.dev0"
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}
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