Text Generation
Transformers
Safetensors
qwen3
feature-extraction
dflash
speculative-decoding
diffusion
efficiency
flash-decoding
qwen
diffusion-language-model
custom_code
text-generation-inference
Instructions to use z-lab/Qwen3.6-27B-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use z-lab/Qwen3.6-27B-DFlash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="z-lab/Qwen3.6-27B-DFlash", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("z-lab/Qwen3.6-27B-DFlash", trust_remote_code=True) model = AutoModel.from_pretrained("z-lab/Qwen3.6-27B-DFlash", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use z-lab/Qwen3.6-27B-DFlash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "z-lab/Qwen3.6-27B-DFlash" # 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/Qwen3.6-27B-DFlash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/z-lab/Qwen3.6-27B-DFlash
- SGLang
How to use z-lab/Qwen3.6-27B-DFlash 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/Qwen3.6-27B-DFlash" \ --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/Qwen3.6-27B-DFlash", "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/Qwen3.6-27B-DFlash" \ --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/Qwen3.6-27B-DFlash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use z-lab/Qwen3.6-27B-DFlash with Docker Model Runner:
docker model run hf.co/z-lab/Qwen3.6-27B-DFlash
Access request
1
#13 opened 4 days ago
by
einsk325
This model is still under training?
๐ 9
#12 opened 6 days ago
by
sandysong
MyAi
#11 opened 11 days ago
by
Xqm-QaD-KNR-ry5
RuntimeError: expected mat1 and mat2 to have the same dtype, but got: float != c10::Half
#10 opened 11 days ago
by
mancub
It is fast but run for a while easily get error and cause vllm stop.
1
#9 opened 12 days ago
by
james0010
Full Local Hands-on Step-by-Step Demo Video
#8 opened 17 days ago
by
fahdmirzac
Why is there a 4k ctx limit?
2
#7 opened 21 days ago
by
crazyi
with latest sglang, gibberish output
2
#6 opened 21 days ago
by
cudaoom
Would this work with the FP8 version of the model?
4
#5 opened 22 days ago
by
pathosethoslogos
I used it on Omlx,but it showed thinking as content.
3
#4 opened 23 days ago
by
BeCreated
Thank you!
3
#3 opened 24 days ago
by
xneoenx
Avg Draft acceptance rate is low.
17
#2 opened 25 days ago
by
fouvy
LLAMA.CPP + ROCm + DFlash on 7900 XTX
๐๐ฅ 5
3
#1 opened 25 days ago
by
flamme-demon