Instructions to use GSAI-ML/iLLaDA-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Security and privacy
If you discover potential security issues in the project, or believe you may have found a security issue, please notify the ByteDance security team through our security center or vulnerability reporting email. Please do not create public GitHub Issues.
We will assess the vulnerability based on the Common Vulnerability Scoring System (CVSS 3.1). The security team will keep you updated on key progress and may request further information or guidance from you. You are welcome to contact us via the email or website mentioned above to ask questions or discuss disclosure matters.
To protect the security of our customers, ByteDance requests that you do not publish or share information regarding the vulnerability in any public forum, nor publish or share data involving users, until the vulnerability has been remediated and our users have been notified. Please understand that the time required for remediation depends on the severity of the vulnerability and the scope of the impact.
Individuals, companies, and security teams may wish to publish security advisories on their own websites or other forums. Please contact us via the email or website mentioned above prior to publication to discuss the information that can be disclosed and to coordinate the disclosure timeline.
Bug Bounty Reward
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