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

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---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
inference: false
tags:
- dflash2
- speculative-decoding
- draft-model
- sglang
---

# checkpoint-draft-dflash2 (Qwen3.5-35B-A3B VCLR3 drafter)

DFlash2 speculative-decoding **draft model** for the Qwen3.5-35B-A3B (VCLR3 game-video
SFT) target. Not a standalone language model: it runs inside a speculative decoding
server and drafts tokens for the target to verify (target weights NOT included;
point `--model-path` at your own merged Qwen3.5-35B-A3B VCLR3 checkpoint).

## Architecture

DFlash2 (Inco AI, [blog](https://inco.ai/blog/dflash2/); z-lab-compatible weight
layout) with block_size 8:

- 5-layer Qwen3-style dual-stream draft over the target's captured layer
  `{1, 10, 19, 28, 37}` hidden states, full 248k vocab (uses the target's
  embed_tokens / lm_head)
- Two-tap grouped dynamic causal convolutions (kernel 2, group 16) around every
  attention/MLP sublayer — fixes block-end (suffix) decay
- Top-16 candidate path selector (rank 256): `S_t(a,b) = U_t(b) + <A(a) ⊙ H(h_t), B(b)>`

## Serving (SGLang main, DFlash2-aware DFLASH worker)

```bash
python -m sglang.launch_server \
  --model-path /path/to/Qwen3.5-35B-A3B-vclr3-target \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path HYHPING2023/checkpoint-draft-dflash2 \
  --speculative-num-draft-tokens 8 \
  --trust-remote-code --tp 1 --enable-metrics
```

Notes: request `chat_template_kwargs: {"enable_thinking": false}` (the draft was
trained on non-thinking answers); SGLang builds before DFlash2 support silently
ignore the selector.

## Evaluation (temperature 0, greedy; block 8)

| Metric | DFlash1 baseline | **DFlash2 (this)** |
|---|---|---|
| Offline accept length / cycle (12.1k blocks, mixed corpus) | 2.72 | **2.97** (+9%) |
| — video blocks | — | **3.54** |
| — text blocks | — | 2.89 |
| SGLang serve accept length (single stream, text) | 1.80 | **2.15** (+19%) |

Per-position conditional acceptance rises 0.63 → 0.72 toward the block end
(DFlash1 stays flat ~0.60) — the convolution's suffix-decay fix.

## Training

4 epochs on the mixed video+zh+en corpus (70.6k samples), warm-started from the
DFlash1 b8 mixed2 checkpoint, FSDP + frozen online 35B target. Teacher-forced
selector CE. Checkpoint: `epoch_3_step_35300`.