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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModel

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

Qwen3.8-27B-DFlash2

File size: 1,239 Bytes

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{
  "architectures": [
    "DFlash2DraftModel"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": null,
  "is_causal": false,
  "dflash_config": {
    "block_size": 8,
    "conv_group_size": 16,
    "conv_kernel_size": 2,
    "mask_token_id": 248070,
    "selector_rank": 256,
    "selector_top_k": 16,
    "target_layer_ids": [
      5,
      19,
      33,
      47,
      61
    ]
  },
  "dtype": "bfloat16",
  "eos_token_id": 248044,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 5120,
  "initializer_range": 0.02,
  "intermediate_size": 17408,
  "layer_types": [
    "sliding_attention",
    "sliding_attention",
    "sliding_attention",
    "sliding_attention",
    "sliding_attention"
  ],
  "max_position_embeddings": 262144,
  "max_window_layers": 5,
  "model_type": "qwen3",
  "num_attention_heads": 32,
  "num_hidden_layers": 5,
  "num_key_value_heads": 8,
  "num_target_layers": 64,
  "pad_token_id": 248044,
  "rms_norm_eps": 1e-06,
  "rope_parameters": {
    "rope_theta": 10000000,
    "rope_type": "default"
  },
  "sliding_window": 2048,
  "tie_word_embeddings": false,
  "transformers_version": "5.15.0",
  "use_cache": true,
  "use_sliding_window": true,
  "vocab_size": 248320
}