Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
bloom
Eval Results (legacy)
text-generation-inference
Instructions to use bigscience/bloomz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/bloomz with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/bloomz")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz") model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bigscience/bloomz with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/bloomz" # 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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/bloomz
- SGLang
How to use bigscience/bloomz 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" \ --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", "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" \ --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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/bloomz with Docker Model Runner:
docker model run hf.co/bigscience/bloomz
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<td>Finetuned Model</td>
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<td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td>
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<td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td>
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<td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td>
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<th colspan="12">Original pretrained checkpoints. Not recommended.</th>
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<td>Pretrained Model</td>
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<td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td>
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<td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td>
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<td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td>
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<td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td>
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<td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td>
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<td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td>
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<td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td>
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<td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td>
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<td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td>
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<td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
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<th colspan="12">Original pretrained checkpoints. Not recommended.</th>
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<tr>
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<td>Pretrained Model</td>
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<td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td>
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<td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td>
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<td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td>
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<td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td>
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<td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td>
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