Instructions to use GSAI-ML/LLaDA-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("GSAI-ML/LLaDA-8B-Instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
Commit History
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Update EvalEval source links to flat datastore
Verified signatureUpdates existing EvalEval Community Evals YAML entries to link to flat datastore aggregate objects pinned to an immutable EEE datastore commit, and normalizes source.name to EvalEval. No score, dataset, task, or date changes are intended. This updates existing open PR refs only. Contributor: evaleval71c0ea0
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Add EvalEval community eval results
Verified signatureAdds EvalEval Community Evals YAML entries with source backlinks to EEE aggregate records. Contributor: evaleval2c85519
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Add EvalEval community eval results
Verified signatureAdds EvalEval Community Evals YAML entries with source backlinks to EEE aggregate records. Contributor: evaleval813b611 -
Add EvalEval community eval results
Verified signatureAdds EvalEval Community Evals YAML entries with source backlinks to EEE aggregate records. Contributor: evaleval25da2cf
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Update README.md
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Supports the input of attention mask
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Update README.md
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Update README.md
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add LLaDA-8B-Instruct
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initial commit
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