Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
bloom
Eval Results (legacy)
text-generation-inference
Instructions to use bigscience/bloomz-560m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/bloomz-560m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/bloomz-560m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m") model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bigscience/bloomz-560m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/bloomz-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/bloomz-560m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/bloomz-560m
- SGLang
How to use bigscience/bloomz-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/bloomz-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/bloomz-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/bloomz-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/bloomz-560m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/bloomz-560m with Docker Model Runner:
docker model run hf.co/bigscience/bloomz-560m
Commit History
-
Adding `safetensors` variant of this model
9e05df5
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Fix architecture (#6)
- Fix architecture (2eb9bb8e672a2c9f5839ee4c60b39004b58dcc09) Co-authored-by: Lewis Tunstall <lewtun@users.noreply.huggingface.co>64a6f17 -
Fix architecture (#5)
- Fix architecture (e41b7df5cac28714a29171ff72cef3db1548f435) Co-authored-by: Lewis Tunstall <lewtun@users.noreply.huggingface.co>842d973
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Update README.md
e183ebb
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Update README.md
1bab45f
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Remove eval folder (Moved to bigscience/evaluation-results)
790efe9
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Add paper
63fdc61 -
Create README.md
e221b2d
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Organize
87637f3 -
Add eval files
cbda70b
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Add files
4e82575 -
initial commit
e245f9e