Instructions to use HYHPING2023/checkpoint-draft-dflash2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HYHPING2023/checkpoint-draft-dflash2 with Transformers:

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

pipe = pipeline("text-generation", model="HYHPING2023/checkpoint-draft-dflash2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("HYHPING2023/checkpoint-draft-dflash2", trust_remote_code=True)
model = AutoModel.from_pretrained("HYHPING2023/checkpoint-draft-dflash2", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HYHPING2023/checkpoint-draft-dflash2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HYHPING2023/checkpoint-draft-dflash2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HYHPING2023/checkpoint-draft-dflash2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HYHPING2023/checkpoint-draft-dflash2

SGLang

How to use HYHPING2023/checkpoint-draft-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 "HYHPING2023/checkpoint-draft-dflash2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HYHPING2023/checkpoint-draft-dflash2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "HYHPING2023/checkpoint-draft-dflash2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HYHPING2023/checkpoint-draft-dflash2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HYHPING2023/checkpoint-draft-dflash2 with Docker Model Runner:
```
docker model run hf.co/HYHPING2023/checkpoint-draft-dflash2
```

checkpoint-draft-dflash2

File size: 1,604 Bytes

edfa72c

{
  "transformers_version": "5.8.1",
  "architectures": [
    "DFlash2DraftModel"
  ],
  "output_hidden_states": false,
  "return_dict": true,
  "dtype": "bfloat16",
  "chunk_size_feed_forward": 0,
  "is_encoder_decoder": false,
  "id2label": {
    "0": "LABEL_0",
    "1": "LABEL_1"
  },
  "label2id": {
    "LABEL_0": 0,
    "LABEL_1": 1
  },
  "problem_type": null,
  "vocab_size": 248320,
  "hidden_size": 2048,
  "intermediate_size": 8192,
  "num_hidden_layers": 5,
  "num_attention_heads": 16,
  "num_key_value_heads": 2,
  "head_dim": 256,
  "hidden_act": "silu",
  "max_position_embeddings": 262144,
  "initializer_range": 0.02,
  "rms_norm_eps": 1e-06,
  "use_cache": true,
  "tie_word_embeddings": false,
  "rope_parameters": {
    "rope_theta": 1000000,
    "rope_type": "default"
  },
  "attention_bias": false,
  "use_sliding_window": false,
  "sliding_window": null,
  "max_window_layers": 5,
  "layer_types": [
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention",
    "full_attention"
  ],
  "attention_dropout": 0.0,
  "pad_token_id": null,
  "bos_token_id": null,
  "eos_token_id": 248044,
  "_name_or_path": "output/dflash2/draft_config.json",
  "model_type": "qwen3",
  "auto_map": {
    "AutoModel": "dflash.DFlash2DraftModel"
  },
  "block_size": 8,
  "dflash_config": {
    "mask_token_id": 248043,
    "target_layer_ids": [
      1,
      10,
      19,
      28,
      37
    ],
    "conv_kernel_size": 2,
    "conv_group_size": 16,
    "selector_rank": 256,
    "selector_top_k": 16
  },
  "num_target_layers": 40,
  "output_attentions": false
}