Instructions to use bigscience/bloom-560m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/bloom-560m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/bloom-560m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m") model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bigscience/bloom-560m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/bloom-560m" # 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-560m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/bloom-560m
- SGLang
How to use bigscience/bloom-560m 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-560m" \ --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-560m", "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-560m" \ --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-560m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/bloom-560m with Docker Model Runner:
docker model run hf.co/bigscience/bloom-560m
Add evaluation results on the default config and test split of phpthinh/data_1
Beep boop, I am a bot from Hugging Face's automatic model evaluator π!
Your model has been evaluated on the default config and test split of the phpthinh/data_1 dataset by @phpthinh , using the predictions stored here.
Accept this pull request to see the results displayed on the Hub leaderboard.
Evaluate your model on more datasets here.
Hey again @phpthinh ! Would you mind closing the PRs whenever you run a private evaluation against the models? It would help tremendously!
I wish I could close the PR and run a private evaluation!
It is impossible for me to close the PR since the PR is created by the tool.
I will contact the team about this issue.
Sorry for the annoyance!
No worries, not your fault. If you could do it, that would be great, if you can't, we'll open a feature request so that you can evaluate without having to open a PR I guess.
Hi @phpthinh , excited to see that you're using Evaluation on the Hub for the BLOOM models! We should definitely add a feature to allow people to evaluate models without opening a PR.
In the meantime, a temporary suggestion for mitigating this may be for you to clone the BLOOM model repository to your namespace on the Hub instead β then you can run evaluation jobs against it as much as you'd like without pinging the authors!
That would be great!
Thanks for your suggestion @mathemakitten !