Instructions to use bigscience/bloom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/bloom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/bloom")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom") model = AutoModelForCausalLM.from_pretrained("bigscience/bloom") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bigscience/bloom with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/bloom" # 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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/bloom
- SGLang
How to use bigscience/bloom 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/bloom" \ --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", "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/bloom" \ --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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/bloom with Docker Model Runner:
docker model run hf.co/bigscience/bloom
Paraphrasing with Bloom
Will Bloom be good at paraphrasing if fine tuned?
what is the proper way to train it?
paraphrase :
sentence1
sentence2
and give it millions of examples like the above
what exactly happens to the token relationships behind the scenes so that it knows how to paraphrase never before seen sentences? How does it get from
training data
I would like to go to the movies.
The cinema seems like an ideal choice.
to be able to paraphrase a sentence like
I would vote for this candidate for president.
Hey we have finetuned BLOOM on paraphrasing among many other tasks to produce BLOOMZ. It should work very well with e.g. this prompt:
Sentence 1: YOUR_SENTENCE
Sentence 2: YOUR_SENTENCE
Question: Do Sentence 1 and Sentence 2 express the same meaning? Yes or No?
I would like to paraphrase a new sentence from the sentence given, not to see whether two sentences paraphrase each other. Is this possible?
are you talking about Bloom or Bloomz?
Bloomz
@mishavee Did you make progress and can you share some insights? I am also interested in this topic since I dealt with this paper: https://ris.uni-paderborn.de/record/26049
I have Bloomz running, but the results are by far not sufficient in this task.
