Instructions to use modal-labs/GLM-5.3-Flash-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modal-labs/GLM-5.3-Flash-DFlash with Transformers:

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

pipe = pipeline("text-generation", model="modal-labs/GLM-5.3-Flash-DFlash")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("modal-labs/GLM-5.3-Flash-DFlash")
model = AutoModel.from_pretrained("modal-labs/GLM-5.3-Flash-DFlash", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use modal-labs/GLM-5.3-Flash-DFlash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modal-labs/GLM-5.3-Flash-DFlash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "modal-labs/GLM-5.3-Flash-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/modal-labs/GLM-5.3-Flash-DFlash

SGLang

How to use modal-labs/GLM-5.3-Flash-DFlash 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 "modal-labs/GLM-5.3-Flash-DFlash" \
    --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": "modal-labs/GLM-5.3-Flash-DFlash",
		"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 "modal-labs/GLM-5.3-Flash-DFlash" \
        --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": "modal-labs/GLM-5.3-Flash-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use modal-labs/GLM-5.3-Flash-DFlash with Docker Model Runner:
```
docker model run hf.co/modal-labs/GLM-5.3-Flash-DFlash
```

GLM-5.3-Flash-DFlash / README.md

harmya-modal

Add GLM-5.3-Flash DFlash draft model

dae6d31 21 days ago

preview code

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metadata

pipeline_tag: text-generation
library_name: transformers
base_model:
  - zai-org/GLM-5.3-Flash
license: mit
license_link: LICENSE
inference: false
tags:
  - dflash
  - speculative-decoding
  - speculative-decoding-draft
  - block-diffusion
  - draft-model
  - glm
  - glm-5.3
  - sglang

GLM-5.3-Flash-DFlash

Paper | Github | Blog

This repository contains a DFlash draft model for zai-org/GLM-5.3-Flash. It is not a standalone language model. It is intended to be paired with the target model in a speculative decoding server.

DFlash uses a lightweight block diffusion draft model to propose multiple tokens in parallel. The target model verifies those proposals, improving serving throughput while preserving the target model's output distribution.

Quick Start

GLM-5.3-Flash needs the DFlash capture hooks in the glm5_next model, available on SGLang main. An example deployment is:

python -m sglang.launch_server \
  --model-path zai-org/GLM-5.3-Flash \
  --tp-size 4 \
  --trust-remote-code \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path modal-labs/GLM-5.3-Flash-DFlash \
  --speculative-dflash-block-size 8 \
  --speculative-draft-model-quantization unquant \
  --speculative-draft-attention-backend trtllm_mha \
  --speculative-draft-kv-cache-dtype fp8_e4m3 \
  --host 0.0.0.0 \
  --port 30000

Keep the draft model unquantized. Quantizing it lowers the accept length.

License

Distributed under the MIT License, inherited from the target model.

Citation

If you find DFlash useful, please cite the original paper:

@article{chen2026dflash,
  title   = {{DFlash: Block Diffusion for Flash Speculative Decoding}},
  author  = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
  journal = {arXiv preprint arXiv:2602.06036},
  year    = {2026}
}