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
Browse filesBeep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\
Your model has been evaluated on the default config and test split of the [phpthinh/data_1](https://huggingface.co/datasets/phpthinh/data_1) dataset by @phpthinh , using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__data_1-default-4c0514-1832562967).\
Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=phpthinh/data_1).\
Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=phpthinh/data_1).
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pipeline_tag: text-generation
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---
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<h1 style='text-align: center '>BLOOM LM</h1>
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pipeline_tag: text-generation
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model-index:
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- name: bigscience/bloom-560m
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results:
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- task:
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type: zero-shot-classification
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name: Zero-Shot Text Classification
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dataset:
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name: phpthinh/data_1
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type: phpthinh/data_1
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config: default
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split: test
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.24439986626546306
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verified: true
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- name: Loss
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type: loss
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value: 2.5313685071514187
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verified: true
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---
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<h1 style='text-align: center '>BLOOM LM</h1>
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