Instructions to use bigscience/bloomz-petals with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/bloomz-petals with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/bloomz-petals")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-petals") model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-petals") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bigscience/bloomz-petals with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/bloomz-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/bloomz-petals", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/bloomz-petals
- SGLang
How to use bigscience/bloomz-petals 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 "bigscience/bloomz-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/bloomz-petals", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "bigscience/bloomz-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/bloomz-petals", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/bloomz-petals with Docker Model Runner:
docker model run hf.co/bigscience/bloomz-petals
| # BLOOMZ, a version for Petals | |
| This model is a version of [bigscience/bloomz](https://huggingface.co/bigscience/bloomz) | |
| post-processed to be run at home using the [Petals](https://github.com/bigscience-workshop/petals#readme) swarm. | |
| Please check out: | |
| - The [original model card](https://huggingface.co/bigscience/bloomz) | |
| to learn about the model's capabilities, specifications, and terms of use. | |
| - The [Petals repository](https://github.com/bigscience-workshop/petals#readme) | |
| to learn how to install Petals and run this model over the Petals swarm. | |
| We provide minimal code examples below. | |
| ## Using the model | |
| ```python | |
| from petals import DistributedBloomForCausalLM | |
| model = DistributedBloomForCausalLM.from_pretrained("bigscience/bloomz-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 | |
| ```bash | |
| python -m petals.cli.run_server bigscience/bloomz-petals | |
| ``` |