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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license: bigscience-bloom-rail-1.0
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datasets:
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- bigscience/xP3
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language:
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example_title: "hi-en fable"
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- [bloomz-mt](https://huggingface.co/bigscience/bloomz-mt): 176B parameter multitask finetuned version of [bloom](https://huggingface.co/bigscience/bloom) on [xP3](https://huggingface.co/bigscience/xP3) & [xP3mt](https://huggingface.co/bigscience/xP3). **Better than [bloomz](https://huggingface.co/bigscience/bloomz) when prompting in non-english**
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- [bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt): 7.1B parameter multitask finetuned version of [bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) on [xP3](https://huggingface.co/bigscience/xP3) & [xP3mt](https://huggingface.co/bigscience/xP3). **Better than [bloomz-7b1](https://huggingface.co/bigscience/bloomz-7b1) when prompting in non-english**
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----
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- [bloomz-p3](https://huggingface.co/bigscience/bloomz): 176B parameter multitask finetuned version of [bloom](https://huggingface.co/bigscience/bloom) on [P3](https://huggingface.co/bigscience/P3). **Released for research purposes, performance is inferior to [bloomz](https://huggingface.co/bigscience/bloomz)**
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- [bloomz-7b1-p3](https://huggingface.co/bigscience/bloomz-7b1): 7.1B parameter multitask finetuned version of [bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) on [P3](https://huggingface.co/bigscience/P3). **Released for research purposes, performance is inferior to [bloomz-7b1](https://huggingface.co/bigscience/bloomz-7b1)**
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----
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# Intended uses
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datasets:
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- bigscience/xP3
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license: bigscience-bloom-rail-1.0
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language:
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- ak
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- ar
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example_title: "hi-en fable"
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# Table of Contents
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1. [Model Summary](#model=summary)
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2. [Use](#use)
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3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
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4. [Training Details](#training-details)
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5. [Evaluation](#evaluation)
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6. [Environmental Impact](#environmental-impact)
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7. [Citation](#citation)
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8. [Model Card Authors](#model-card-authors)
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9. [How To Get Started With the Model](#how-to-get-started-with-the-model)
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# Model Summary
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> We present BLOOMZ & mT0, a family of models capable of following human instructions in hundreds of languages. By finetuning large BLOOM & mT5 pretrained multilingual language models on our multilingual task mixture (xP3), we discover various generalization properties of our finetuned models acrosss tasks and languages.
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- **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf)
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- **Paper:** [TODO]
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- **Funded by:** The French government & Hugging Face
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- **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co)
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- **BLOOMZ & mT0 Model Family:**
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|Name|Explanation|
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|----|-----------|
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|[bloomz-560m](https://huggingface.co/bigscience/bloomz-560m)| 560M parameter multitask finetuned version of [bloom-560m](https://huggingface.co/bigscience/bloom-560m) on [xP3](https://huggingface.co/bigscience/xP3)|
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|[bloomz-1b1](https://huggingface.co/bigscience/bloomz-1b1)| 1.1B parameter multitask finetuned version of [bloom-1b1](https://huggingface.co/bigscience/bloom-1b1) on [xP3](https://huggingface.co/bigscience/xP3)|
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|[bloomz-1b7](https://huggingface.co/bigscience/bloomz-1b7)| 1.7B parameter multitask finetuned version of [bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) on [xP3](https://huggingface.co/bigscience/xP3)|
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|[bloomz-3b](https://huggingface.co/bigscience/bloomz-3b)| 3B parameter multitask finetuned version of [bloom-3b](https://huggingface.co/bigscience/bloom-3b) on [xP3](https://huggingface.co/bigscience/xP3)|
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|[bloomz-7b1](https://huggingface.co/bigscience/bloomz-7b1)|7.1B parameter multitask finetuned version of [bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) on [xP3](https://huggingface.co/bigscience/xP3)|
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|[bloomz](https://huggingface.co/bigscience/bloomz)|176B parameter multitask finetuned version of [bloom](https://huggingface.co/bigscience/bloom) on [xP3](https://huggingface.co/bigscience/xP3)|
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|[bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt)|7.1B parameter multitask finetuned version of [bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) on [xP3](https://huggingface.co/bigscience/xP3) & [xP3mt](https://huggingface.co/bigscience/xP3mt). **Better than [bloomz-7b1](https://huggingface.co/bigscience/bloomz-7b1) when prompting in non-English**|
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|[bloomz-mt](https://huggingface.co/bigscience/bloomz-mt)| 176B parameter multitask finetuned version of [bloom](https://huggingface.co/bigscience/bloom) on [xP3](https://huggingface.co/bigscience/xP3) & [xP3mt](https://huggingface.co/bigscience/xP3mt). **Better than [bloomz](https://huggingface.co/bigscience/bloomz) when prompting in non-English**|
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|[bloomz-7b1-p3](https://huggingface.co/bigscience/bloomz-7b1)| 7.1B parameter multitask finetuned version of [bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) on [P3](https://huggingface.co/bigscience/P3). **Released for research purposes, performance is inferior to [bloomz-7b1](https://huggingface.co/bigscience/bloomz-7b1)**|
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|[bloomz-p3](https://huggingface.co/bigscience/bloomz)| 176B parameter multitask finetuned version of [bloom](https://huggingface.co/bigscience/bloom) on [P3](https://huggingface.co/bigscience/P3). **Released for research purposes, performance is inferior to [bloomz](https://huggingface.co/bigscience/bloomz)**|
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|[mt0-small](https://huggingface.co/bigscience/mt0-xxl)|300M parameter multitask finetuned version of [mt5-small](https://huggingface.co/google/mt5-small) on [xP3](https://huggingface.co/bigscience/xP3)|
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|[mt0-base](https://huggingface.co/bigscience/mt0-xxl)|580M parameter multitask finetuned version of [mt5-base](https://huggingface.co/google/mt5-base) on [xP3](https://huggingface.co/bigscience/xP3)|
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|[mt0-large](https://huggingface.co/bigscience/mt0-xxl)|1.2B parameter multitask finetuned version of [mt5-large](https://huggingface.co/google/mt5-large) on [xP3](https://huggingface.co/bigscience/xP3)|
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|[mt0-xl](https://huggingface.co/bigscience/mt0-xxl)|3.7B parameter multitask finetuned version of [mt5-xl](https://huggingface.co/google/mt5-xl) on [xP3](https://huggingface.co/bigscience/xP3)|
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|[mt0-xxl](https://huggingface.co/bigscience/mt0-xxl)|13B parameter multitask finetuned version of [mt5-xxl](https://huggingface.co/google/mt5-xxl) on [xP3](https://huggingface.co/bigscience/xP3)|
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|[mt0-xxl-mt](https://huggingface.co/bigscience/mt0-xxl-mt)|13B parameter multitask finetuned version of [mt5-xxl](https://huggingface.co/google/mt5-xxl) on [xP3](https://huggingface.co/bigscience/xP3) & [xP3mt](https://huggingface.co/bigscience/xP3mt). **Better than [mt0-xxl](https://huggingface.co/bigscience/mt0-xxl) when prompting in non-English**|
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|[mt0-xxl-p3](https://huggingface.co/bigscience/mt0-xxl-p3)| 13B parameter multitask finetuned version of [mt5-xxl](https://huggingface.co/google/mt5-xxl) on [P3](https://huggingface.co/bigscience/P3). **Released for research purposes, performance is inferior to [mt0-xxl](https://huggingface.co/bigscience/mt0-xxl)**|
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|----|-----------|
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# Intended uses
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