Instructions to use z-lab/Qwen3.8-27B-DFlash2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use z-lab/Qwen3.8-27B-DFlash2 with Transformers:

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

pipe = pipeline("text-generation", model="z-lab/Qwen3.8-27B-DFlash2")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("z-lab/Qwen3.8-27B-DFlash2")
model = AutoModel.from_pretrained("z-lab/Qwen3.8-27B-DFlash2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use z-lab/Qwen3.8-27B-DFlash2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "z-lab/Qwen3.8-27B-DFlash2"
# 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/Qwen3.8-27B-DFlash2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/z-lab/Qwen3.8-27B-DFlash2

SGLang

How to use z-lab/Qwen3.8-27B-DFlash2 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/Qwen3.8-27B-DFlash2" \
    --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/Qwen3.8-27B-DFlash2",
		"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/Qwen3.8-27B-DFlash2" \
        --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/Qwen3.8-27B-DFlash2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use z-lab/Qwen3.8-27B-DFlash2 with Docker Model Runner:
```
docker model run hf.co/z-lab/Qwen3.8-27B-DFlash2
```

Unsloth Dynamic 3 GGUF support?

#11

by Mk2Oracle - opened 8 days ago

Discussion

Mk2Oracle

8 days ago

^ title

ravingamm

8 days ago

I'm using unsloth qwen 3.8 27b D3 version with it.

Mk2Oracle

8 days ago

I'm using unsloth qwen 3.8 27b D3 version with it.

I'm not seeing... any improvements not sure my flags are wrong . RTX 3090 gpu

Qwen3.8-27B-DFlash2-Q4_K_M.gguf -ngl auto -c 131072 --batch-size 2048 --ubatch-size 512 --threads 12 -ctk q4_0 -ctv q4_0 -n 8192 -fa on --reasoning on --reasoning-budget 2048 --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.00 --presence-penalty 0.0 --repeat-penalty 1.0 --spec-type draft-dflash --spec-draft-n-max 7 --reasoning-preserve --host 0.0.0.0 --port 8000 --timeout 7200 --sse-ping-interval 15 --sleep-idle-seconds -1 --api-key dev_key --alias qwen38-dflash2-rtx -np 1

ravingamm

8 days ago

-m /models/Qwen3.8-27B-UD-Q4_K_XL.gguf
--model-draft /models/Qwen3.8-27B-DFlash2-Q8_0.gguf
--n-gpu-layers all
--n-gpu-layers-draft all
--threads 20
--cache-type-k q4_0
--cache-type-v q4_0
--flash-attn on
--ctx-size 163840
--parallel 1
--reasoning off
--spec-type draft-dflash
--spec-draft-n-max 4
--jinja

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