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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("GSAI-ML/iLLaDA-8B-Instruct", trust_remote_code=True, device_map="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
```

nieshen commited on Jun 24

Commit

13bdb7b

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1 Parent(s): a72a744

Update README.md

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@@ -23,18 +23,16 @@ Inference and evaluation codes: https://github.com/ML-GSAI/LLaDA.
 | Total parameters | 7.62B | 8.02B |
 | Non-embedding parameters | 6.98B | 6.98B |
-## Benchmark Results of Base Models
 |  | iLLaDA 8B | LLaDA 8B | Dream 7B | Qwen2.5 7B |
 | --- | ---: | ---: | ---: | ---: |
 | Model | Diffusion | Diffusion | Diffusion | AR |
-| Training tokens | 12T | 2.3T | 18T + 0.6T | 18T |
-| MMLU | 74.8 | 65.9 | 69.5 | 71.9 |
-| BBH | 71.3 | 49.7 | 57.9 | 63.9 |
-| ARC-C | 60.8 | 45.9 | 59.8 | 51.5 |
-| HellaSwag | 76.6 | 70.5 | 73.3 | 79.0 |
-| GSM8K | 81.9 | 70.3 | 77.2 | 78.9 |
-| MATH | 38.4 | 31.4 | 39.6 | 41.1 |
-| HumanEval | 50.0 | 35.4 | 57.9 | 56.7 |
-| MBPP | 57.8 | 40.0 | 56.2 | 63.6 |
-| Average | 63.9 | 51.1 | 61.4 | 63.3 |

 | 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 |