Text Generation
Transformers
Safetensors
qwen3
dflash
speculative-decoding
speculative-decoding-draft
block-diffusion
draft-model
glm
glm-5.3
sglang
text-generation-inference
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
- Transformers
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
Download model.safetensors from modal-labs/GLM-5.3-Flash-DFlash: direct link, hf CLI and curl.
- Browser
- Download file 2.78 GB
-
https://huggingface.co/modal-labs/GLM-5.3-Flash-DFlash/resolve/dae6d319510889a7f1ac5242d5b7fb2d5d95eb05/model.safetensors
- Command line
-
hf download hf://modal-labs/GLM-5.3-Flash-DFlash@dae6d319510889a7f1ac5242d5b7fb2d5d95eb05/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/modal-labs/GLM-5.3-Flash-DFlash/resolve/dae6d319510889a7f1ac5242d5b7fb2d5d95eb05/model.safetensors
2.78 GB
- Xet hash:
- 6a063f26c9a26ae57d48c490841d1828cc8a66f7e4d9e990dccd54e9e6c6488b
- Size of remote file:
- 2.78 GB
- SHA256:
- 3860c574465b7c7523896a22a3cc83dc4b9c14e019a6755061de2b8a4b061539
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