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

Libraries

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

iLLaDA-8B-Instruct / README.md

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	---

	# iLLaDA-8B-Instruct

	iLLaDA is an 8B fully bidirectional masked diffusion language model trained from scratch with 12T pre-training tokens, an 8192-token context length, variable-length generation, and confidence-based scoring for multiple-choice evaluation.

	For more details, please refer to the paper: [Improved Large Language Diffusion Models](https://huggingface.co/papers/2606.25331).

	Inference and evaluation codes can be found in the [LLaDA GitHub Repository](https://github.com/ML-GSAI/LLaDA).

	## How to Use

	You can load the model and tokenizer using the `transformers` library:

	```python
	import torch
	from transformers import AutoModel, AutoTokenizer

	# Load the tokenizer and model
	tokenizer = AutoTokenizer.from_pretrained('GSAI-ML/iLLaDA-8B-Instruct', trust_remote_code=True)
	model = AutoModel.from_pretrained('GSAI-ML/iLLaDA-8B-Instruct', trust_remote_code=True, torch_dtype=torch.bfloat16)
	```

	For customized generation and evaluation scripts (such as `generate.py` and `chat.py`), please visit the official [GitHub repository](https://github.com/ML-GSAI/LLaDA).

	## Architecture

	\| \| iLLaDA 8B \| LLaDA 8B \|
	\| --- \| ---: \| ---: \|
	\| Layers \| 32 \| 32 \|
	\| Model dimension \| 4096 \| 4096 \|
	\| Attention heads \| 32 \| 32 \|
	\| Key/Value heads \| 8 \| 32 \|
	\| FFN dimension \| 14,336 \| 12,288 \|
	\| Vocabulary size \| 155,136 \| 126,464 \|
	\| Maximum sequence length \| 8192 \| 4096 \|
	\| Embedding and LM-head \| Tied \| Untied \|
	\| Total parameters \| 7.62B \| 8.02B \|
	\| Non-embedding parameters \| 6.98B \| 6.98B \|

	## Benchmark Results of Instruct Models

	\| \| iLLaDA 8B \| LLaDA 8B \| Dream 7B \| Qwen2.5 7B \|
	\| --- \| ---: \| ---: \| ---: \| ---: \|
	\| Model \| Diffusion \| Diffusion \| Diffusion \| AR \|
	\| MMLU \| 71.6 \| 65.5 \| 67.0 \| 76.6 \|
	\| MMLU-Pro \| 52.3 \| 37.0 \| 43.3 \| 56.3 \|
	\| MMLU-Redux \| 76.4 \| 68.9 \| 76.3 \| 75.7 \|
	\| GSM8K \| 89.0 \| 77.5 \| 81.0 \| 91.6 \|
	\| MATH \| 56.7 \| 42.2 \| 39.2 \| 75.5 \|
	\| HumanEval \| 65.9 \| 49.4 \| 55.5 \| 84.8 \|
	\| MBPP \| 58.0 \| 41.0 \| 58.8 \| 79.2 \|
	\| Average \| 67.1 \| 54.5 \| 60.2 \| 77.1 \|

	## Citation

	```bibtex
	@article{nie2025large,
	title={Large Language Diffusion Models},
	author={Nie, Shen and Zhu, Fengqi and You, Zebin and Zhang, Xiaolu and Ou, Jingyang and Hu, Jun and Zhou, Jun and Lin, Yankai and Wen, Ji-Rong and Li, Chongxuan},
	journal={arXiv preprint arXiv:2502.09992},
	year={2025}
	}
	```