How to use from
SGLangUse 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 "bigscience/bloom-petals" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "bigscience/bloom-petals",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
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 SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "bigscience/bloom-petals" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigscience/bloom-petals", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'