Instructions to use GSAI-ML/LLaDA-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GSAI-ML/LLaDA-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="GSAI-ML/LLaDA-8B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("GSAI-ML/LLaDA-8B-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GSAI-ML/LLaDA-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GSAI-ML/LLaDA-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GSAI-ML/LLaDA-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GSAI-ML/LLaDA-8B-Instruct

SGLang

How to use GSAI-ML/LLaDA-8B-Instruct 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 "GSAI-ML/LLaDA-8B-Instruct" \
    --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": "GSAI-ML/LLaDA-8B-Instruct",
		"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 "GSAI-ML/LLaDA-8B-Instruct" \
        --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": "GSAI-ML/LLaDA-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GSAI-ML/LLaDA-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/GSAI-ML/LLaDA-8B-Instruct
```

what part of the code is diffusion?

by fblgit - opened Mar 7, 2025

Discussion

fblgit

Mar 7, 2025

Im not entirely sure which part of the modeling.py is "diffusion".
It seems to lacks the defining features of diffusion models—noise addition, denoising, time-step conditioning, or a reverse process. Instead, it processes input tokens autoregressively to predict the next token, consistent with standard language models of transformers.
Which part is exactly "diffusion" maybe you can point the lines in the code?

nieshen

GSAI-ML org Mar 7, 2025

Thank you for your interest!

For the code related to the forward and reverse processes, please refer to https://github.com/ML-GSAI/LLaDA. To learn how the models in this repository are created, please see the https://github.com/ML-GSAI/LLaDA/blob/main/GUIDELINES.md.

Please note that our model is not a Gaussian continuous diffusion model, but a mask-based discrete diffusion model. Prior work (https://arxiv.org/abs/2406.03736) has theoretically shown that mask-based discrete diffusion models do not require time conditioning.

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