bloomz / README.md

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	---
	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
	inference: false
	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: "Explain to me in Traditional Chinese what is the difference between Bitcoin and Ethereum."
	example_title: "zh-en qa"
	- text: "Write a code snippet with O(log(n)) computational complexity."
	example_title: "code-en"
	- 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"
	- text: "How many sides does a rectangle and heptagon have, when
	combined? Answer this question with some math.
	Ein Rechteck hat 4 Seiten. Ein Siebeneck hat 7 Seiten.
	In Kombination haben sie 4 + 7 = 11 Seiten.
	كم عدد الأضلاع التي يجمعها المربع والمثلث؟
	Répondez à cette question en chinois."
	example_title: "en-de-ar-fr-zh math"
	model-index:
	- name: bloomz
	results:
	- task:
	type: Coreference resolution
	dataset:
	type: winogrande
	name: Winogrande XL (xl)
	config: xl
	split: validation
	revision: a80f460359d1e9a67c006011c94de42a8759430c
	metrics:
	- type: Accuracy
	value: 59.27
	- task:
	type: Coreference resolution
	dataset:
	type: Muennighoff/xwinograd
	name: XWinograd (en)
	config: en
	split: test
	revision: 9dd5ea5505fad86b7bedad667955577815300cee
	metrics:
	- type: Accuracy
	value: 69.08
	- task:
	type: Coreference resolution
	dataset:
	type: Muennighoff/xwinograd
	name: XWinograd (fr)
	config: fr
	split: test
	revision: 9dd5ea5505fad86b7bedad667955577815300cee
	metrics:
	- type: Accuracy
	value: 68.67
	- task:
	type: Coreference resolution
	dataset:
	type: Muennighoff/xwinograd
	name: XWinograd (jp)
	config: jp
	split: test
	revision: 9dd5ea5505fad86b7bedad667955577815300cee
	metrics:
	- type: Accuracy
	value: 59.65
	- task:
	type: Coreference resolution
	dataset:
	type: Muennighoff/xwinograd
	name: XWinograd (pt)
	config: pt
	split: test
	revision: 9dd5ea5505fad86b7bedad667955577815300cee
	metrics:
	- type: Accuracy
	value: 64.26
	- task:
	type: Coreference resolution
	dataset:
	type: Muennighoff/xwinograd
	name: XWinograd (ru)
	config: ru
	split: test
	revision: 9dd5ea5505fad86b7bedad667955577815300cee
	metrics:
	- type: Accuracy
	value: 60.95
	- task:
	type: Coreference resolution
	dataset:
	type: Muennighoff/xwinograd
	name: XWinograd (zh)
	config: zh
	split: test
	revision: 9dd5ea5505fad86b7bedad667955577815300cee
	metrics:
	- type: Accuracy
	value: 70.24
	- task:
	type: Natural language inference
	dataset:
	type: anli
	name: ANLI (r1)
	config: r1
	split: validation
	revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
	metrics:
	- type: Accuracy
	value: 48.6
	- task:
	type: Natural language inference
	dataset:
	type: anli
	name: ANLI (r2)
	config: r2
	split: validation
	revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
	metrics:
	- type: Accuracy
	value: 44.1
	- task:
	type: Natural language inference
	dataset:
	type: anli
	name: ANLI (r3)
	config: r3
	split: validation
	revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
	metrics:
	- type: Accuracy
	value: 45.5
	- task:
	type: Natural language inference
	dataset:
	type: super_glue
	name: SuperGLUE (cb)
	config: cb
	split: validation
	revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
	metrics:
	- type: Accuracy
	value: 82.14
	- task:
	type: Natural language inference
	dataset:
	type: super_glue
	name: SuperGLUE (rte)
	config: rte
	split: validation
	revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
	metrics:
	- type: Accuracy
	value: 85.56
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (ar)
	config: ar
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 60.68
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (bg)
	config: bg
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 48.43
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (de)
	config: de
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 54.38
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (el)
	config: el
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 47.43
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (en)
	config: en
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 67.47
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (es)
	config: es
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 61.24
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (fr)
	config: fr
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 61.37
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (hi)
	config: hi
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 60.2
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (ru)
	config: ru
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 54.02
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (sw)
	config: sw
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 52.09
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (th)
	config: th
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 43.78
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (tr)
	config: tr
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 45.7
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (ur)
	config: ur
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 50.8
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (vi)
	config: vi
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 61.0
	- task:
	type: Natural language inference
	dataset:
	type: xnli
	name: XNLI (zh)
	config: zh
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 56.91
	- task:
	type: Program synthesis
	dataset:
	type: openai_humaneval
	name: HumanEval
	config: None
	split: test
	revision: e8dc562f5de170c54b5481011dd9f4fa04845771
	metrics:
	- type: Pass@1
	value: 12.06
	- type: Pass@10
	value: 26.53
	- type: Pass@100
	value: 48.44
	- task:
	type: Sentence completion
	dataset:
	type: story_cloze
	name: StoryCloze (2016)
	config: "2016"
	split: validation
	revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
	metrics:
	- type: Accuracy
	value: 96.26
	- task:
	type: Sentence completion
	dataset:
	type: super_glue
	name: SuperGLUE (copa)
	config: copa
	split: validation
	revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
	metrics:
	- type: Accuracy
	value: 91.0
	- task:
	type: Sentence completion
	dataset:
	type: xcopa
	name: XCOPA (et)
	config: et
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 51.0
	- task:
	type: Sentence completion
	dataset:
	type: xcopa
	name: XCOPA (ht)
	config: ht
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 58.0
	- task:
	type: Sentence completion
	dataset:
	type: xcopa
	name: XCOPA (id)
	config: id
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 86.0
	- task:
	type: Sentence completion
	dataset:
	type: xcopa
	name: XCOPA (it)
	config: it
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 74.0
	- task:
	type: Sentence completion
	dataset:
	type: xcopa
	name: XCOPA (qu)
	config: qu
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 56.0
	- task:
	type: Sentence completion
	dataset:
	type: xcopa
	name: XCOPA (sw)
	config: sw
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 64.0
	- task:
	type: Sentence completion
	dataset:
	type: xcopa
	name: XCOPA (ta)
	config: ta
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 69.0
	- task:
	type: Sentence completion
	dataset:
	type: xcopa
	name: XCOPA (th)
	config: th
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 58.0
	- task:
	type: Sentence completion
	dataset:
	type: xcopa
	name: XCOPA (tr)
	config: tr
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 57.0
	- task:
	type: Sentence completion
	dataset:
	type: xcopa
	name: XCOPA (vi)
	config: vi
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 87.0
	- task:
	type: Sentence completion
	dataset:
	type: xcopa
	name: XCOPA (zh)
	config: zh
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 90.0
	- task:
	type: Sentence completion
	dataset:
	type: Muennighoff/xstory_cloze
	name: XStoryCloze (ar)
	config: ar
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 92.79
	- task:
	type: Sentence completion
	dataset:
	type: Muennighoff/xstory_cloze
	name: XStoryCloze (es)
	config: es
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 94.37
	- task:
	type: Sentence completion
	dataset:
	type: Muennighoff/xstory_cloze
	name: XStoryCloze (eu)
	config: eu
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 86.9
	- task:
	type: Sentence completion
	dataset:
	type: Muennighoff/xstory_cloze
	name: XStoryCloze (hi)
	config: hi
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 88.42
	- task:
	type: Sentence completion
	dataset:
	type: Muennighoff/xstory_cloze
	name: XStoryCloze (id)
	config: id
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 92.12
	- task:
	type: Sentence completion
	dataset:
	type: Muennighoff/xstory_cloze
	name: XStoryCloze (my)
	config: my
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 52.35
	- task:
	type: Sentence completion
	dataset:
	type: Muennighoff/xstory_cloze
	name: XStoryCloze (ru)
	config: ru
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 81.73
	- task:
	type: Sentence completion
	dataset:
	type: Muennighoff/xstory_cloze
	name: XStoryCloze (sw)
	config: sw
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 79.81
	- task:
	type: Sentence completion
	dataset:
	type: Muennighoff/xstory_cloze
	name: XStoryCloze (te)
	config: te
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 81.2
	- task:
	type: Sentence completion
	dataset:
	type: Muennighoff/xstory_cloze
	name: XStoryCloze (zh)
	config: zh
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 93.12
	---

	![xmtf](https://github.com/bigscience-workshop/xmtf/blob/master/xmtf_banner.png?raw=true)

	# Table of Contents

	1. [Model Summary](#model-summary)
	2. [Use](#use)
	3. [Limitations](#limitations)
	4. [Training](#training)
	5. [Evaluation](#evaluation)
	7. [Citation](#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 the resulting models capable of crosslingual generalization to unseen tasks & languages.

	- Repository: [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf)
	- Paper: [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
	- Point of Contact: [Niklas Muennighoff](mailto:niklas@hf.co)
	- Languages: Refer to [bloom](https://huggingface.co/bigscience/bloom) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages.
	- BLOOMZ & mT0 Model Family:

	<div class="max-w-full overflow-auto">
	<table>
	<tr>
	<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English.
	</tr>
	<tr>
	<td>Parameters</td>
	<td>300M</td>
	<td>580M</td>
	<td>1.2B</td>
	<td>3.7B</td>
	<td>13B</td>
	<td>560M</td>
	<td>1.1B</td>
	<td>1.7B</td>
	<td>3B</td>
	<td>7.1B</td>
	<td>176B</td>
	</tr>
	<tr>
	<td>Finetuned Model</td>
	<td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td>
	<td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td>
	<td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td>
	<td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td>
	<td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
	<td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td>
	<td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td>
	<td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td>
	<td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td>
	<td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td>
	<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
	</tr>
	</tr>
	<tr>
	<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th>
	</tr>
	<tr>
	<td>Finetuned Model</td>
	<td></td>
	<td></td>
	<td></td>
	<td></td>
	<td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
	<td></td>
	<td></td>
	<td></td>
	<td></td>
	<td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td>
	<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td>
	</tr>
	<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th>
	</tr>
	<tr>
	<td>Finetuned Model</td>
	<td></td>
	<td></td>
	<td></td>
	<td></td>
	<td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
	<td></td>
	<td></td>
	<td></td>
	<td></td>
	<td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td>
	<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td>
	</tr>
	<th colspan="12">Original pretrained checkpoints. Not recommended.</th>
	<tr>
	<td>Pretrained Model</td>
	<td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td>
	<td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td>
	<td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td>
	<td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td>
	<td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td>
	<td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td>
	<td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td>
	<td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td>
	<td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td>
	<td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td>
	<td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td>
	</tr>
	</table>
	</div>


	# 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

	<details>
	<summary> Click to expand </summary>

	```python
	# 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]))
	```

	</details>

	### GPU

	<details>
	<summary> Click to expand </summary>

	```python
	# 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]))
	```

	</details>

	### GPU in 8bit

	<details>
	<summary> Click to expand </summary>

	```python
	# 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]))
	```

	</details>

	<!-- Necessary for whitespace -->
	###

	# 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](https://huggingface.co/bigscience/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 with 8 GPUs per node (36 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links
	- Communication: NCCL-communications network with a fully dedicated subnet

	## Software

	- Orchestration: [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed)
	- Optimizer & parallelism: [DeepSpeed](https://github.com/microsoft/DeepSpeed)
	- Neural networks: [PyTorch](https://github.com/pytorch/pytorch) (pytorch-1.11 w/ CUDA-11.5)
	- FP16 if applicable: [apex](https://github.com/NVIDIA/apex)

	# Evaluation

	We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.

	# Citation
	```bibtex
	@article{muennighoff2022crosslingual,
	title={Crosslingual generalization through multitask finetuning},
	author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others},
	journal={arXiv preprint arXiv:2211.01786},
	year={2022}
	}
	```