Instructions to use deepsweet/Qwen3.6-35B-A3B-DFlash-FP16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepsweet/Qwen3.6-35B-A3B-DFlash-FP16 with Transformers:

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

pipe = pipeline("text-generation", model="deepsweet/Qwen3.6-35B-A3B-DFlash-FP16", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("deepsweet/Qwen3.6-35B-A3B-DFlash-FP16", trust_remote_code=True)
model = AutoModel.from_pretrained("deepsweet/Qwen3.6-35B-A3B-DFlash-FP16", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use deepsweet/Qwen3.6-35B-A3B-DFlash-FP16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepsweet/Qwen3.6-35B-A3B-DFlash-FP16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepsweet/Qwen3.6-35B-A3B-DFlash-FP16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/deepsweet/Qwen3.6-35B-A3B-DFlash-FP16

SGLang

How to use deepsweet/Qwen3.6-35B-A3B-DFlash-FP16 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 "deepsweet/Qwen3.6-35B-A3B-DFlash-FP16" \
    --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": "deepsweet/Qwen3.6-35B-A3B-DFlash-FP16",
		"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 "deepsweet/Qwen3.6-35B-A3B-DFlash-FP16" \
        --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": "deepsweet/Qwen3.6-35B-A3B-DFlash-FP16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use deepsweet/Qwen3.6-35B-A3B-DFlash-FP16 with Docker Model Runner:
```
docker model run hf.co/deepsweet/Qwen3.6-35B-A3B-DFlash-FP16
```

This model was converted to FP16 from z-lab/Qwen3.6-35B-A3B-DFlash BF16.

What is "DFlash"?

DFlash is a novel speculative decoding method that utilizes a lightweight block diffusion model for drafting. It enables efficient, high-quality parallel drafting that pushes the limits of inference speed.

What is "FP16"?

"FP16" is M1/M2 Apple Silicon only optimization that leads to a very noticeable prompt processing boost. See "Metal FP32 Vs BF16 Vs FP16 benchmark" and jundot/omlx/pull/880 for details.

Use the original model if you have M3+ Apple Silicon.

Changelog

28.04.2026: original model got updated

Downloads last month: 750

Model tree for deepsweet/Qwen3.6-35B-A3B-DFlash-FP16

Base model

z-lab/Qwen3.6-35B-A3B-DFlash

Finetuned

(2)

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Collection including deepsweet/Qwen3.6-35B-A3B-DFlash-FP16

Qwen3.6-35B-A3B

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