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Check out the documentation for more information.
BLOOM, a version for Petals
This model is a version of bigscience/bloom post-processed to be run at home using the Petals swarm.
Please check out:
- The original model card to learn about the model's capabilities, specifications, and terms of use.
- The Petals repository to learn how to install Petals and run this model over the Petals swarm.
We provide minimal code examples below.
Using the model
from petals import DistributedBloomForCausalLM
model = DistributedBloomForCausalLM.from_pretrained("bigscience/bloom-petals")
# Embeddings & prompts are on your device, BLOOM blocks are distributed across the Internet
inputs = tokenizer("A cat sat", return_tensors="pt")["input_ids"]
outputs = model.generate(inputs, max_new_tokens=5)
print(tokenizer.decode(outputs[0])) # A cat sat on a mat...
Serving the model blocks
python -m petals.cli.run_server bigscience/bloom-petals
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Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "bigscience/bloom-petals"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigscience/bloom-petals", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'