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metadata
license: bigscience-bloom-rail-1.0
datasets:
  - bigscience/xP3
language:
  - ak
  - ar
  - as
  - bm
  - bn
  - ca
  - code
  - en
  - es
  - eu
  - fon
  - fr
  - gu
  - hi
  - id
  - ig
  - ki
  - kn
  - lg
  - ln
  - ml
  - mr
  - ne
  - nso
  - ny
  - or
  - pa
  - pt
  - rn
  - rw
  - sn
  - st
  - sw
  - ta
  - te
  - tn
  - ts
  - tum
  - tw
  - ur
  - vi
  - wo
  - xh
  - yo
  - zh
  - zu
programming_language:
  - C
  - C++
  - C#
  - Go
  - Java
  - JavaScript
  - Lua
  - PHP
  - Python
  - Ruby
  - Rust
  - Scala
  - TypeScript
pipeline_tag: text-generation
widget:
  - text: >-
      一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the
      previous review as positive, neutral or negative?
    example_title: zh-en sentiment
  - text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？
    example_title: zh-zh sentiment
  - text: Suggest at least five related search terms to "Mạng neural nhân tạo".
    example_title: vi-en query
  - text: >-
      Proposez au moins cinq mots clés concernant «Réseau de neurones
      artificiels».
    example_title: fr-fr query
  - text: >-
      Explain in a sentence in Telugu what is backpropagation in neural
      networks.
    example_title: te-en qa
  - text: Why is the sky blue?
    example_title: en-en qa
  - text: >-
      Write a fairy tale about a troll saving a princess from a dangerous
      dragon. The fairy tale is a masterpiece that has achieved praise worldwide
      and its moral is "Heroes Come in All Shapes and Sizes". Story (in
      Spanish):
    example_title: es-en fable
  - text: >-
      Write a fable about wood elves living in a forest that is suddenly invaded
      by ogres. The fable is a masterpiece that has achieved praise worldwide
      and its moral is "Violence is the last refuge of the incompetent". Fable
      (in Hindi):
    example_title: hi-en fable

Repository: bigscience-workshop/bloomz

Models

Multilingual model capable of following user instructions in a variety of languages. Together with our paper [TODO: LINK], we release the following models:

  • bloomz-p3: 176B parameter multitask finetuned version of bloom on P3. Released for research purposes, performance is inferior to bloomz
  • bloomz-7b1-p3: 7.1B parameter multitask finetuned version of bloom-7b1 on P3. Released for research purposes, performance is inferior to bloomz-7b1

Intended uses

You can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction. For instance, you can ask "Translate this to Chinese: Je t'aime.", and the model will hopefully generate "我爱你".

How to use

Here is how to use the model in PyTorch:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m")

inputs = tokenizer.encode("Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

To use another checkpoint, replace the path in AutoTokenizer and AutoModelForCausalLM.

Note: 176B models are trained with bfloat16, while smaller models are trained with fp16. We recommend using the same precision type or fp32 at inference

Limitations

  • Large model size may require large computational resources
  • High performance variance depending on the prompt

BibTeX entry and citation info 

TODO