Instructions to use z-lab/Qwen3-4B-DFlash-b16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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)

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
```

jianchen0311 commited on Jan 4

Commit

92d8d54

verified ·

1 Parent(s): 034c56f

Upload model

Browse files

Files changed (1) hide show

modeling_dflash.py +0 -1

modeling_dflash.py CHANGED Viewed

@@ -179,7 +179,6 @@ def extract_context_feature(
 class DFlashDraftModel(Qwen3PreTrainedModel):
     config_class = Qwen3Config
     _no_split_modules = ["Qwen3DFlashDecoderLayer"]
-    _keys_to_ignore_on_load_missing = ["lm_head.weight", "embed_tokens.weight"]
     def __init__(self, config) -> None:
         super().__init__(config)
         self.config = config

 class DFlashDraftModel(Qwen3PreTrainedModel):
     config_class = Qwen3Config
     _no_split_modules = ["Qwen3DFlashDecoderLayer"]
     def __init__(self, config) -> None:
         super().__init__(config)
         self.config = config