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metadata
datasets:
  - bigscience/xP3
license: bigscience-bloom-rail-1.0
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

xmtf

Table of Contents

  1. Model Summary
  2. Use
  3. Limitations
  4. Training
  5. Evaluation
  6. Citation

Model Summary

We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find our resulting models capable of crosslingual generalization to unseen tasks & languages.

Use

Intended use

We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "Translate to English: Je t’aime.", the model will most likely answer "I love you.". Some prompt ideas from our paper:

  • 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?
  • Suggest at least five related search terms to "Mạng neural nhân tạo".
  • 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):
  • Explain in a sentence in Telugu what is backpropagation in neural networks.

Feel free to share your generations in the Community tab!

How to use

CPU

Click to expand 
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "bigscience/bloomz"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)

inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

GPU

Click to expand 
# pip install -q transformers accelerate
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "bigscience/bloomz"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto")

inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

GPU in 8bit

Click to expand 
# pip install -q transformers accelerate bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "bigscience/bloomz"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True)

inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

Limitations

Prompt Engineering: The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "Translate to English: Je t'aime" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "Translate to English: Je t'aime.", "Translate to English: Je t'aime. Translation:" "What is "Je t'aime." in English?", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "Explain in a sentence in Telugu what is backpropagation in neural networks.".

Training

Model

  • Architecture: Same as bloom, also refer to the config.json file
  • Finetuning steps: 498
  • Finetuning tokens: 2.09 billion
  • Finetuning layout: 72x pipeline parallel, 1x tensor parallel, 4x data parallel
  • Precision: bfloat16

Hardware

  • CPUs: AMD CPUs with 512GB memory per node
  • GPUs: 288 A100 80GB GPUs (36 nodes) with 8 GPUs per node using NVLink 4 inter-gpu connects, 4 OmniPath links
  • Communication: NCCL-communications network with a fully dedicated subnet

Software

Evaluation

We refer to Table 7 from our paper [TODO LINK] and bigscience/evaluation-results.

Citation 

TODO