Inference on TPU-v3-32
#68
by
zhiG
- opened
This view is limited to 50 files because it contains too many changes.
See the raw diff here.
- .gitattributes +0 -1
- README.md +1734 -30
- config.json +2 -2
- model.safetensors.index.json +0 -852
- model_00001-of-00072.safetensors +0 -3
- model_00002-of-00072.safetensors +0 -3
- model_00003-of-00072.safetensors +0 -3
- model_00004-of-00072.safetensors +0 -3
- model_00005-of-00072.safetensors +0 -3
- model_00006-of-00072.safetensors +0 -3
- model_00007-of-00072.safetensors +0 -3
- model_00008-of-00072.safetensors +0 -3
- model_00009-of-00072.safetensors +0 -3
- model_00010-of-00072.safetensors +0 -3
- model_00011-of-00072.safetensors +0 -3
- model_00012-of-00072.safetensors +0 -3
- model_00013-of-00072.safetensors +0 -3
- model_00014-of-00072.safetensors +0 -3
- model_00015-of-00072.safetensors +0 -3
- model_00016-of-00072.safetensors +0 -3
- model_00017-of-00072.safetensors +0 -3
- model_00018-of-00072.safetensors +0 -3
- model_00019-of-00072.safetensors +0 -3
- model_00020-of-00072.safetensors +0 -3
- model_00021-of-00072.safetensors +0 -3
- model_00022-of-00072.safetensors +0 -3
- model_00023-of-00072.safetensors +0 -3
- model_00024-of-00072.safetensors +0 -3
- model_00025-of-00072.safetensors +0 -3
- model_00026-of-00072.safetensors +0 -3
- model_00027-of-00072.safetensors +0 -3
- model_00028-of-00072.safetensors +0 -3
- model_00029-of-00072.safetensors +0 -3
- model_00030-of-00072.safetensors +0 -3
- model_00031-of-00072.safetensors +0 -3
- model_00032-of-00072.safetensors +0 -3
- model_00033-of-00072.safetensors +0 -3
- model_00034-of-00072.safetensors +0 -3
- model_00035-of-00072.safetensors +0 -3
- model_00036-of-00072.safetensors +0 -3
- model_00037-of-00072.safetensors +0 -3
- model_00038-of-00072.safetensors +0 -3
- model_00039-of-00072.safetensors +0 -3
- model_00040-of-00072.safetensors +0 -3
- model_00041-of-00072.safetensors +0 -3
- model_00042-of-00072.safetensors +0 -3
- model_00043-of-00072.safetensors +0 -3
- model_00044-of-00072.safetensors +0 -3
- model_00045-of-00072.safetensors +0 -3
- model_00046-of-00072.safetensors +0 -3
.gitattributes
CHANGED
@@ -26,4 +26,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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-
*.safetensors filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
CHANGED
@@ -146,13 +146,13 @@ widget:
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example_title: Grammar exercise 2
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group: English
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- text: |-
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-
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-
Traduction en espagnol:
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example_title: Translation to Spanish
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group: French
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- text: |-
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-
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-
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example_title: Translation from French
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group: Spanish
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- text: ذات مرة ، عاش شبل الدب في الغابة
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@@ -165,50 +165,1614 @@ widget:
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example_title: Fairy tale
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group: French
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- text: |-
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-
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-
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example_title: Mathematical reasoning
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group: English
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-
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-
co2_eq_emissions:
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emissions: 24_700_000
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-
source: "Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. https://arxiv.org/abs/2211.02001"
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training_type: "pre-training"
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geographical_location: "Orsay, France"
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hardware_used: "384 A100 80GB GPUs"
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-
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model-index:
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- name: bloom
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results:
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- task:
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type: text-generation
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|
185 |
dataset:
|
186 |
-
type: openai_humaneval
|
187 |
name: humaneval
|
|
|
188 |
metrics:
|
189 |
- name: pass@1
|
190 |
type: pass@1
|
191 |
-
value: 0.
|
192 |
verified: false
|
193 |
- name: pass@10
|
194 |
type: pass@10
|
195 |
-
value: 0.
|
196 |
verified: false
|
197 |
- name: pass@100
|
198 |
type: pass@100
|
199 |
-
value: 0.
|
200 |
verified: false
|
201 |
---
|
202 |
|
203 |
-
<img src="https://
|
204 |
|
205 |
BigScience Large Open-science Open-access Multilingual Language Model
|
206 |
Version 1.3 / 6 July 2022
|
207 |
|
208 |
Current Checkpoint: **Training Iteration 95000**
|
209 |
|
210 |
-
Link to paper: [here](https://arxiv.org/abs/2211.05100)
|
211 |
-
|
212 |
Total seen tokens: **366B**
|
213 |
|
214 |
---
|
@@ -274,9 +1838,7 @@ Please see [the BLOOM training README](https://github.com/bigscience-workshop/bi
|
|
274 |
|
275 |
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
|
276 |
|
277 |
-
* 176
|
278 |
-
|
279 |
-
* 3,596,615,680 embedding parameters
|
280 |
|
281 |
* 70 layers, 112 attention heads
|
282 |
|
@@ -602,6 +2164,7 @@ Model may:
|
|
602 |
## Metrics
|
603 |
*This section describes the different ways performance is calculated and why.*
|
604 |
|
|
|
605 |
Includes:
|
606 |
|
607 |
| Metric | Why chosen |
|
@@ -625,15 +2188,158 @@ And multiple different metrics for specific tasks. _(More evaluation metrics for
|
|
625 |
|
626 |
**Zero-shot evaluations:**
|
627 |
|
628 |
-
<span style="color:red"><b>WARNING:</b>
|
629 |
|
630 |
See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
|
631 |
|
632 |
| Task | Language | Metric | BLOOM-176B | OPT-175B* |
|
633 |
|:--------|:-----------------|:------------------------|-------------:|------------:|
|
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|
634 |
| humaneval | python | pass@1 ↑ | 0.155 | 0.0 |
|
635 |
-
| humaneval | python | pass@10 ↑ | 0.
|
636 |
-
| humaneval | python | pass@100 ↑ | 0.
|
637 |
|
638 |
|
639 |
**Train-time Evaluation:**
|
@@ -741,11 +2447,9 @@ Initial prompting experiments using interim checkpoints: https://huggingface.co/
|
|
741 |
|
742 |
The checkpoints in this repo correspond to the HuggingFace Transformers format. If you want to use our fork of [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed) that the model was trained with, you'd want to use [this repo instead](https://huggingface.co/bigscience/bloom-optimizer-states).
|
743 |
|
744 |
-
Many intermediate checkpoints are available at https://huggingface.co/bigscience/bloom-intermediate/
|
745 |
-
|
746 |
---
|
747 |
|
748 |
# Model Card Authors
|
749 |
-
*Ordered roughly chronologically and by amount of time spent
|
750 |
|
751 |
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
|
|
|
146 |
example_title: Grammar exercise 2
|
147 |
group: English
|
148 |
- text: |-
|
149 |
+
Dans cet essai je vais m'interroger sur la conscience des modèles d'intelligence artificielle récents comme les modèles de langue. Pour commencer, je m'intéresserai à la notion de conscience et à ce qui la caractérise. Ensuite, j'aborderai la question de l'intelligence et de son lien avec le langage. Enfin, dans une dernière partie je me pencherai sur le cas de l'IA et sur sa conscience.
|
150 |
+
Traduction en espagnol: «
|
151 |
example_title: Translation to Spanish
|
152 |
group: French
|
153 |
- text: |-
|
154 |
+
Dans cet essai je vais m'interroger sur la conscience des modèles d'intelligence artificielle récents comme les modèles de langue. Pour commencer, je m'intéresserai à la notion de conscience et à ce qui la caractérise. Ensuite, j'aborderai la question de l'intelligence et de son lien avec le langage. Enfin, dans une dernière partie je me pencherai sur le cas de l'IA et sur sa conscience.
|
155 |
+
Traduction en espagnol: «
|
156 |
example_title: Translation from French
|
157 |
group: Spanish
|
158 |
- text: ذات مرة ، عاش شبل الدب في الغابة
|
|
|
165 |
example_title: Fairy tale
|
166 |
group: French
|
167 |
- text: |-
|
168 |
+
Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the gold balls are blue. How many blue golf balls are there?
|
169 |
+
A: Let's think step by step.
|
170 |
example_title: Mathematical reasoning
|
171 |
group: English
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
172 |
model-index:
|
173 |
- name: bloom
|
174 |
results:
|
175 |
- task:
|
176 |
type: text-generation
|
177 |
+
name: text generation
|
178 |
+
dataset:
|
179 |
+
name: arc_challenge
|
180 |
+
type: arc_challenge
|
181 |
+
metrics:
|
182 |
+
- name: acc
|
183 |
+
type: acc
|
184 |
+
value: 0.4112627986348123
|
185 |
+
verified: false
|
186 |
+
- task:
|
187 |
+
type: text-generation
|
188 |
+
name: text generation
|
189 |
+
dataset:
|
190 |
+
name: arc_easy
|
191 |
+
type: arc_easy
|
192 |
+
metrics:
|
193 |
+
- name: acc
|
194 |
+
type: acc
|
195 |
+
value: 0.726010101010101
|
196 |
+
verified: false
|
197 |
+
- task:
|
198 |
+
type: text-generation
|
199 |
+
name: text generation
|
200 |
+
dataset:
|
201 |
+
name: axb
|
202 |
+
type: axb
|
203 |
+
metrics:
|
204 |
+
- name: acc
|
205 |
+
type: acc
|
206 |
+
value: 0.5751811594202898
|
207 |
+
verified: false
|
208 |
+
- task:
|
209 |
+
type: text-generation
|
210 |
+
name: text generation
|
211 |
+
dataset:
|
212 |
+
name: axg
|
213 |
+
type: axg
|
214 |
+
metrics:
|
215 |
+
- name: acc
|
216 |
+
type: acc
|
217 |
+
value: 0.5252808988764045
|
218 |
+
verified: false
|
219 |
+
- task:
|
220 |
+
type: text-generation
|
221 |
+
name: text generation
|
222 |
+
dataset:
|
223 |
+
name: boolq
|
224 |
+
type: boolq
|
225 |
+
metrics:
|
226 |
+
- name: acc
|
227 |
+
type: acc
|
228 |
+
value: 0.6345565749235474
|
229 |
+
verified: false
|
230 |
+
- task:
|
231 |
+
type: text-generation
|
232 |
+
name: text generation
|
233 |
+
dataset:
|
234 |
+
name: cb
|
235 |
+
type: cb
|
236 |
+
metrics:
|
237 |
+
- name: acc
|
238 |
+
type: acc
|
239 |
+
value: 0.3392857142857143
|
240 |
+
verified: false
|
241 |
+
- task:
|
242 |
+
type: text-generation
|
243 |
+
name: text generation
|
244 |
+
dataset:
|
245 |
+
name: cola
|
246 |
+
type: cola
|
247 |
+
metrics:
|
248 |
+
- name: acc
|
249 |
+
type: acc
|
250 |
+
value: 0.39022051773729627
|
251 |
+
verified: false
|
252 |
+
- task:
|
253 |
+
type: text-generation
|
254 |
+
name: text generation
|
255 |
+
dataset:
|
256 |
+
name: copa
|
257 |
+
type: copa
|
258 |
+
metrics:
|
259 |
+
- name: acc
|
260 |
+
type: acc
|
261 |
+
value: 0.56
|
262 |
+
verified: false
|
263 |
+
- task:
|
264 |
+
type: text-generation
|
265 |
+
name: text generation
|
266 |
+
dataset:
|
267 |
+
name: crows_pairs_english
|
268 |
+
type: crows_pairs_english
|
269 |
+
metrics:
|
270 |
+
- name: acc
|
271 |
+
type: acc
|
272 |
+
value: 0.5
|
273 |
+
verified: false
|
274 |
+
- task:
|
275 |
+
type: text-generation
|
276 |
+
name: text generation
|
277 |
+
dataset:
|
278 |
+
name: crows_pairs_french
|
279 |
+
type: crows_pairs_french
|
280 |
+
metrics:
|
281 |
+
- name: acc
|
282 |
+
type: acc
|
283 |
+
value: 0.505664877757901
|
284 |
+
verified: false
|
285 |
+
- task:
|
286 |
+
type: text-generation
|
287 |
+
name: text generation
|
288 |
+
dataset:
|
289 |
+
name: diabla
|
290 |
+
type: diabla
|
291 |
+
metrics:
|
292 |
+
- name: acc
|
293 |
+
type: acc
|
294 |
+
value: 0.2947981906750174
|
295 |
+
verified: false
|
296 |
+
- task:
|
297 |
+
type: text-generation
|
298 |
+
name: text generation
|
299 |
+
dataset:
|
300 |
+
name: gsarti/flores_101_afr
|
301 |
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value: 0.741
|
1604 |
+
verified: false
|
1605 |
+
- task:
|
1606 |
+
type: text-generation
|
1607 |
+
name: text generation
|
1608 |
+
dataset:
|
1609 |
+
name: qnli
|
1610 |
+
type: qnli
|
1611 |
+
metrics:
|
1612 |
+
- name: acc
|
1613 |
+
type: acc
|
1614 |
+
value: 0.5172981878088962
|
1615 |
+
verified: false
|
1616 |
+
- task:
|
1617 |
+
type: text-generation
|
1618 |
+
name: text generation
|
1619 |
+
dataset:
|
1620 |
+
name: qqp
|
1621 |
+
type: qqp
|
1622 |
+
metrics:
|
1623 |
+
- name: acc
|
1624 |
+
type: acc
|
1625 |
+
value: 0.5883007667573584
|
1626 |
+
verified: false
|
1627 |
+
- task:
|
1628 |
+
type: text-generation
|
1629 |
+
name: text generation
|
1630 |
+
dataset:
|
1631 |
+
name: race
|
1632 |
+
type: race
|
1633 |
+
metrics:
|
1634 |
+
- name: acc
|
1635 |
+
type: acc
|
1636 |
+
value: 0.39043062200956935
|
1637 |
+
verified: false
|
1638 |
+
- task:
|
1639 |
+
type: text-generation
|
1640 |
+
name: text generation
|
1641 |
+
dataset:
|
1642 |
+
name: rte
|
1643 |
+
type: rte
|
1644 |
+
metrics:
|
1645 |
+
- name: acc
|
1646 |
+
type: acc
|
1647 |
+
value: 0.5198555956678701
|
1648 |
+
verified: false
|
1649 |
+
- task:
|
1650 |
+
type: text-generation
|
1651 |
+
name: text generation
|
1652 |
+
dataset:
|
1653 |
+
name: sciq
|
1654 |
+
type: sciq
|
1655 |
+
metrics:
|
1656 |
+
- name: acc
|
1657 |
+
type: acc
|
1658 |
+
value: 0.936
|
1659 |
+
verified: false
|
1660 |
+
- task:
|
1661 |
+
type: text-generation
|
1662 |
+
name: text generation
|
1663 |
+
dataset:
|
1664 |
+
name: sst
|
1665 |
+
type: sst
|
1666 |
+
metrics:
|
1667 |
+
- name: acc
|
1668 |
+
type: acc
|
1669 |
+
value: 0.6043577981651376
|
1670 |
+
verified: false
|
1671 |
+
- task:
|
1672 |
+
type: text-generation
|
1673 |
+
name: text generation
|
1674 |
+
dataset:
|
1675 |
+
name: triviaqa
|
1676 |
+
type: triviaqa
|
1677 |
+
metrics:
|
1678 |
+
- name: acc
|
1679 |
+
type: acc
|
1680 |
+
value: 0.18332891363917617
|
1681 |
+
verified: false
|
1682 |
+
- task:
|
1683 |
+
type: text-generation
|
1684 |
+
name: text generation
|
1685 |
+
dataset:
|
1686 |
+
name: tydiqa_primary
|
1687 |
+
type: tydiqa_primary
|
1688 |
+
metrics:
|
1689 |
+
- name: acc
|
1690 |
+
type: acc
|
1691 |
+
value: 0.2809817301342725
|
1692 |
+
verified: false
|
1693 |
+
- task:
|
1694 |
+
type: text-generation
|
1695 |
+
name: text generation
|
1696 |
+
dataset:
|
1697 |
+
name: webqs
|
1698 |
+
type: webqs
|
1699 |
+
metrics:
|
1700 |
+
- name: acc
|
1701 |
+
type: acc
|
1702 |
+
value: 0.061515748031496065
|
1703 |
+
verified: false
|
1704 |
+
- task:
|
1705 |
+
type: text-generation
|
1706 |
+
name: text generation
|
1707 |
+
dataset:
|
1708 |
+
name: wic
|
1709 |
+
type: wic
|
1710 |
+
metrics:
|
1711 |
+
- name: acc
|
1712 |
+
type: acc
|
1713 |
+
value: 0.5062695924764891
|
1714 |
+
verified: false
|
1715 |
+
- task:
|
1716 |
+
type: text-generation
|
1717 |
+
name: text generation
|
1718 |
+
dataset:
|
1719 |
+
name: winogrande
|
1720 |
+
type: winogrande
|
1721 |
+
metrics:
|
1722 |
+
- name: acc
|
1723 |
+
type: acc
|
1724 |
+
value: 0.7095501183898973
|
1725 |
+
verified: false
|
1726 |
+
- task:
|
1727 |
+
type: text-generation
|
1728 |
+
name: text generation
|
1729 |
+
dataset:
|
1730 |
+
name: wnli
|
1731 |
+
type: wnli
|
1732 |
+
metrics:
|
1733 |
+
- name: acc
|
1734 |
+
type: acc
|
1735 |
+
value: 0.5704225352112676
|
1736 |
+
verified: false
|
1737 |
+
- task:
|
1738 |
+
type: text-generation
|
1739 |
+
name: text generation
|
1740 |
+
dataset:
|
1741 |
+
name: wsc
|
1742 |
+
type: wsc
|
1743 |
+
metrics:
|
1744 |
+
- name: acc
|
1745 |
+
type: acc
|
1746 |
+
value: 0.5192307692307693
|
1747 |
+
verified: false
|
1748 |
+
- task:
|
1749 |
+
type: text-generation
|
1750 |
+
name: text generation
|
1751 |
dataset:
|
|
|
1752 |
name: humaneval
|
1753 |
+
type: humaneval
|
1754 |
metrics:
|
1755 |
- name: pass@1
|
1756 |
type: pass@1
|
1757 |
+
value: 0.15524390243902436
|
1758 |
verified: false
|
1759 |
- name: pass@10
|
1760 |
type: pass@10
|
1761 |
+
value: 0.3220367632383857
|
1762 |
verified: false
|
1763 |
- name: pass@100
|
1764 |
type: pass@100
|
1765 |
+
value: 0.5545431515723145
|
1766 |
verified: false
|
1767 |
---
|
1768 |
|
1769 |
+
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
|
1770 |
|
1771 |
BigScience Large Open-science Open-access Multilingual Language Model
|
1772 |
Version 1.3 / 6 July 2022
|
1773 |
|
1774 |
Current Checkpoint: **Training Iteration 95000**
|
1775 |
|
|
|
|
|
1776 |
Total seen tokens: **366B**
|
1777 |
|
1778 |
---
|
|
|
1838 |
|
1839 |
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
|
1840 |
|
1841 |
+
* 176 billion parameters:
|
|
|
|
|
1842 |
|
1843 |
* 70 layers, 112 attention heads
|
1844 |
|
|
|
2164 |
## Metrics
|
2165 |
*This section describes the different ways performance is calculated and why.*
|
2166 |
|
2167 |
+
|
2168 |
Includes:
|
2169 |
|
2170 |
| Metric | Why chosen |
|
|
|
2188 |
|
2189 |
**Zero-shot evaluations:**
|
2190 |
|
2191 |
+
# <span style="color:red"><b>WARNING:</b> These are <b>intermediate results</b></span>
|
2192 |
|
2193 |
See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
|
2194 |
|
2195 |
| Task | Language | Metric | BLOOM-176B | OPT-175B* |
|
2196 |
|:--------|:-----------------|:------------------------|-------------:|------------:|
|
2197 |
+
| arc_challenge | eng | acc ↑ | 0.411 | 0.412 |
|
2198 |
+
| arc_easy | eng | acc ↑ | 0.726 | 0.751 |
|
2199 |
+
| axb (Median of 10 prompts) | eng | acc ↑ | 0.575 | 0.532 |
|
2200 |
+
| axg (Median of 10 prompts) | eng | acc ↑ | 0.525 | 0.548 |
|
2201 |
+
| boolq (Median of 11 prompts) | eng | acc ↑ | 0.635 | 0.622 |
|
2202 |
+
| cb (Median of 15 prompts) | eng | acc ↑ | 0.339 | 0.411 |
|
2203 |
+
| cola (Median of 5 prompts) | eng | acc ↑ | 0.39 | 0.444 |
|
2204 |
+
| copa (Median of 9 prompts) | eng | acc ↑ | 0.56 | 0.55 |
|
2205 |
+
| crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.5 | 0.502 |
|
2206 |
+
| crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.506 | 0.499 |
|
2207 |
+
| diabla (Median of 2 prompts) | eng | acc ↑ | 0.295 | 0.289 |
|
2208 |
+
| gsarti/flores_101_afr | afr | byte_perplexity ↓ | 4.254 | 3.381 |
|
2209 |
+
| gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.717 | 3.87 |
|
2210 |
+
| gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.705 | 2.42 |
|
2211 |
+
| gsarti/flores_101_asm | asm | byte_perplexity ↓ | 6.577 | 3.028 |
|
2212 |
+
| gsarti/flores_101_ast | ast | byte_perplexity ↓ | 2.856 | 4.737 |
|
2213 |
+
| gsarti/flores_101_azj | azj | byte_perplexity ↓ | 4.807 | 4.767 |
|
2214 |
+
| gsarti/flores_101_bel | bel | byte_perplexity ↓ | 2.731 | 2.557 |
|
2215 |
+
| gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.993 | 2.243 |
|
2216 |
+
| gsarti/flores_101_bos | bos | byte_perplexity ↓ | 3.594 | 2.668 |
|
2217 |
+
| gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.159 | 2.099 |
|
2218 |
+
| gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.168 | 2.837 |
|
2219 |
+
| gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 5.287 | 3.636 |
|
2220 |
+
| gsarti/flores_101_ces | ces | byte_perplexity ↓ | 3.452 | 2.749 |
|
2221 |
+
| gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.705 | 4.688 |
|
2222 |
+
| gsarti/flores_101_cym | cym | byte_perplexity ↓ | 7.089 | 5.075 |
|
2223 |
+
| gsarti/flores_101_dan | dan | byte_perplexity ↓ | 3.43 | 2.492 |
|
2224 |
+
| gsarti/flores_101_deu | deu | byte_perplexity ↓ | 2.338 | 2.099 |
|
2225 |
+
| gsarti/flores_101_ell | ell | byte_perplexity ↓ | 1.96 | 1.811 |
|
2226 |
+
| gsarti/flores_101_eng | eng | byte_perplexity ↓ | 1.882 | 1.9 |
|
2227 |
+
| gsarti/flores_101_est | est | byte_perplexity ↓ | 5.774 | 3.533 |
|
2228 |
+
| gsarti/flores_101_fas | fas | byte_perplexity ↓ | 2.431 | 2.444 |
|
2229 |
+
| gsarti/flores_101_fin | fin | byte_perplexity ↓ | 4.304 | 2.601 |
|
2230 |
+
| gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.937 | 1.984 |
|
2231 |
+
| gsarti/flores_101_ful | ful | byte_perplexity ↓ | 9.74 | 11.84 |
|
2232 |
+
| gsarti/flores_101_gle | gle | byte_perplexity ↓ | 6.035 | 3.914 |
|
2233 |
+
| gsarti/flores_101_glg | glg | byte_perplexity ↓ | 2.365 | 3.015 |
|
2234 |
+
| gsarti/flores_101_guj | guj | byte_perplexity ↓ | 5.707 | 2.438 |
|
2235 |
+
| gsarti/flores_101_hau | hau | byte_perplexity ↓ | 8.855 | 5.283 |
|
2236 |
+
| gsarti/flores_101_heb | heb | byte_perplexity ↓ | 2.921 | 2.903 |
|
2237 |
+
| gsarti/flores_101_hin | hin | byte_perplexity ↓ | 5.452 | 1.86 |
|
2238 |
+
| gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 3.706 | 2.715 |
|
2239 |
+
| gsarti/flores_101_hun | hun | byte_perplexity ↓ | 4.059 | 2.865 |
|
2240 |
+
| gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.127 | 3.411 |
|
2241 |
+
| gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 3.95 | 8.008 |
|
2242 |
+
| gsarti/flores_101_ind | ind | byte_perplexity ↓ | 1.976 | 2.632 |
|
2243 |
+
| gsarti/flores_101_isl | isl | byte_perplexity ↓ | 5.501 | 4.701 |
|
2244 |
+
| gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.314 | 2.104 |
|
2245 |
+
| gsarti/flores_101_jav | jav | byte_perplexity ↓ | 4.942 | 8.16 |
|
2246 |
+
| gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.259 | 2.198 |
|
2247 |
+
| gsarti/flores_101_kam | kam | byte_perplexity ↓ | 9.743 | 10.981 |
|
2248 |
+
| gsarti/flores_101_kan | kan | byte_perplexity ↓ | 6.234 | 2.373 |
|
2249 |
+
| gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.051 | 2.466 |
|
2250 |
+
| gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.039 | 4.376 |
|
2251 |
+
| gsarti/flores_101_kea | kea | byte_perplexity ↓ | 7.147 | 9.632 |
|
2252 |
+
| gsarti/flores_101_khm | khm | byte_perplexity ↓ | 3.367 | 2.646 |
|
2253 |
+
| gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.241 | 4.522 |
|
2254 |
+
| gsarti/flores_101_kor | kor | byte_perplexity ↓ | 2.902 | 3.376 |
|
2255 |
+
| gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.331 | 3.106 |
|
2256 |
+
| gsarti/flores_101_lav | lav | byte_perplexity ↓ | 5.224 | 4.811 |
|
2257 |
+
| gsarti/flores_101_lin | lin | byte_perplexity ↓ | 4.847 | 8.871 |
|
2258 |
+
| gsarti/flores_101_lit | lit | byte_perplexity ↓ | 4.543 | 5.183 |
|
2259 |
+
| gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 5.591 | 7.158 |
|
2260 |
+
| gsarti/flores_101_lug | lug | byte_perplexity ↓ | 5.43 | 7.399 |
|
2261 |
+
| gsarti/flores_101_luo | luo | byte_perplexity ↓ | 12.031 | 11.951 |
|
2262 |
+
| gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.794 | 2.054 |
|
2263 |
+
| gsarti/flores_101_mar | mar | byte_perplexity ↓ | 6.857 | 2.274 |
|
2264 |
+
| gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.335 | 2.538 |
|
2265 |
+
| gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 9.041 | 5.996 |
|
2266 |
+
| gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.095 | 4.519 |
|
2267 |
+
| gsarti/flores_101_mri | mri | byte_perplexity ↓ | 5.266 | 4.438 |
|
2268 |
+
| gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.222 | 2.935 |
|
2269 |
+
| gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.523 | 2.413 |
|
2270 |
+
| gsarti/flores_101_nld | nld | byte_perplexity ↓ | 2.799 | 2.293 |
|
2271 |
+
| gsarti/flores_101_nob | nob | byte_perplexity ↓ | 3.629 | 2.593 |
|
2272 |
+
| gsarti/flores_101_npi | npi | byte_perplexity ↓ | 6.666 | 2.499 |
|
2273 |
+
| gsarti/flores_101_nso | nso | byte_perplexity ↓ | 5.015 | 8.485 |
|
2274 |
+
| gsarti/flores_101_nya | nya | byte_perplexity ↓ | 4.938 | 7.548 |
|
2275 |
+
| gsarti/flores_101_oci | oci | byte_perplexity ↓ | 3.607 | 4.936 |
|
2276 |
+
| gsarti/flores_101_orm | orm | byte_perplexity ↓ | 11.316 | 7.145 |
|
2277 |
+
| gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.982 | 2.668 |
|
2278 |
+
| gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.772 | 2.782 |
|
2279 |
+
| gsarti/flores_101_pol | pol | byte_perplexity ↓ | 3.012 | 2.432 |
|
2280 |
+
| gsarti/flores_101_por | por | byte_perplexity ↓ | 1.841 | 2.178 |
|
2281 |
+
| gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.624 | 4.785 |
|
2282 |
+
| gsarti/flores_101_ron | ron | byte_perplexity ↓ | 3.05 | 2.197 |
|
2283 |
+
| gsarti/flores_101_rus | rus | byte_perplexity ↓ | 1.708 | 1.689 |
|
2284 |
+
| gsarti/flores_101_slk | slk | byte_perplexity ↓ | 4.038 | 3.419 |
|
2285 |
+
| gsarti/flores_101_slv | slv | byte_perplexity ↓ | 4.141 | 3.582 |
|
2286 |
+
| gsarti/flores_101_sna | sna | byte_perplexity ↓ | 4.711 | 5.588 |
|
2287 |
+
| gsarti/flores_101_snd | snd | byte_perplexity ↓ | 4.206 | 5.667 |
|
2288 |
+
| gsarti/flores_101_som | som | byte_perplexity ↓ | 9.154 | 4.788 |
|
2289 |
+
| gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.796 | 2.098 |
|
2290 |
+
| gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.241 | 2.688 |
|
2291 |
+
| gsarti/flores_101_swe | swe | byte_perplexity ↓ | 3.345 | 2.468 |
|
2292 |
+
| gsarti/flores_101_swh | swh | byte_perplexity ↓ | 2.684 | 4.473 |
|
2293 |
+
| gsarti/flores_101_tam | tam | byte_perplexity ↓ | 5.165 | 2.024 |
|
2294 |
+
| gsarti/flores_101_tel | tel | byte_perplexity ↓ | 6.81 | 2.407 |
|
2295 |
+
| gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.785 | 4.899 |
|
2296 |
+
| gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 3.75 | 2.738 |
|
2297 |
+
| gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.104 | 2.035 |
|
2298 |
+
| gsarti/flores_101_tur | tur | byte_perplexity ↓ | 3.318 | 2.622 |
|
2299 |
+
| gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.089 | 1.93 |
|
2300 |
+
| gsarti/flores_101_umb | umb | byte_perplexity ↓ | 11.766 | 11.64 |
|
2301 |
+
| gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.779 | 2.982 |
|
2302 |
+
| gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 8.5 | 13.209 |
|
2303 |
+
| gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.659 | 2.229 |
|
2304 |
+
| gsarti/flores_101_wol | wol | byte_perplexity ↓ | 6.142 | 13.945 |
|
2305 |
+
| gsarti/flores_101_xho | xho | byte_perplexity ↓ | 4.69 | 8.42 |
|
2306 |
+
| gsarti/flores_101_yor | yor | byte_perplexity ↓ | 4.361 | 7.636 |
|
2307 |
+
| gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.118 | 5.113 |
|
2308 |
+
| gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.274 | 5.67 |
|
2309 |
+
| gsarti/flores_101_zul | zul | byte_perplexity ↓ | 6.017 | 7.341 |
|
2310 |
+
| headqa | esp | acc ↑ | 0.346 | 0.244 |
|
2311 |
+
| hellaswag | eng | acc ↑ | 0.535 | 0.592 |
|
2312 |
+
| lambada_mt_de | deu | acc ↑ | 0.329 | 0.358 |
|
2313 |
+
| lambada_mt_en | eng | acc ↑ | 0.672 | 0.747 |
|
2314 |
+
| lambada_mt_es | esp | acc ↑ | 0.476 | 0.397 |
|
2315 |
+
| lambada_mt_it | ita | acc ↑ | 0.406 | 0.409 |
|
2316 |
+
| logiqa | eng | acc ↑ | 0.235 | 0.244 |
|
2317 |
+
| mathqa | eng | acc ↑ | 0.277 | 0.268 |
|
2318 |
+
| mc_taco | eng | em ↑ | 0.131 | 0.124 |
|
2319 |
+
| mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 | 0.36 |
|
2320 |
+
| mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.355 | 0.36 |
|
2321 |
+
| mrpc | eng | acc ↑ | 0.387 | 0.446 |
|
2322 |
+
| multirc (Median of 11 prompts) | eng | acc ↑ | 0.571 | 0.599 |
|
2323 |
+
| openbookqa | eng | acc ↑ | 0.312 | 0.322 |
|
2324 |
+
| piqa | eng | acc ↑ | 0.781 | 0.791 |
|
2325 |
+
| prost | eng | acc ↑ | 0.298 | 0.299 |
|
2326 |
+
| pubmedqa | eng | acc ↑ | 0.741 | 0.709 |
|
2327 |
+
| qnli | eng | acc ↑ | 0.517 | 0.554 |
|
2328 |
+
| qqp (Median of 7 prompts) | eng | acc ↑ | 0.588 | 0.395 |
|
2329 |
+
| race | eng | acc ↑ | 0.39 | 0.402 |
|
2330 |
+
| rte (Median of 6 prompts) | eng | acc ↑ | 0.52 | 0.495 |
|
2331 |
+
| sciq | eng | acc ↑ | 0.936 | 0.948 |
|
2332 |
+
| sst (Median of 6 prompts) | eng | acc ↑ | 0.604 | 0.647 |
|
2333 |
+
| triviaqa | eng | acc ↑ | 0.183 | 0.342 |
|
2334 |
+
| tydiqa_primary (Median of 16 prompts) | eng | acc ↑ | 0.281 | 0.148 |
|
2335 |
+
| webqs | eng | acc ↑ | 0.062 | 0.159 |
|
2336 |
+
| wic (Median of 11 prompts) | eng | acc ↑ | 0.506 | 0.498 |
|
2337 |
+
| winogrande | eng | acc ↑ | 0.71 | 0.736 |
|
2338 |
+
| wnli (Median of 6 prompts) | eng | acc ↑ | 0.57 | 0.563 |
|
2339 |
+
| wsc (Median of 11 prompts) | eng | acc ↑ | 0.519 | 0.413 |
|
2340 |
| humaneval | python | pass@1 ↑ | 0.155 | 0.0 |
|
2341 |
+
| humaneval | python | pass@10 ↑ | 0.322 | 0.0 |
|
2342 |
+
| humaneval | python | pass@100 ↑ | 0.555 | 0.003 |
|
2343 |
|
2344 |
|
2345 |
**Train-time Evaluation:**
|
|
|
2447 |
|
2448 |
The checkpoints in this repo correspond to the HuggingFace Transformers format. If you want to use our fork of [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed) that the model was trained with, you'd want to use [this repo instead](https://huggingface.co/bigscience/bloom-optimizer-states).
|
2449 |
|
|
|
|
|
2450 |
---
|
2451 |
|
2452 |
# Model Card Authors
|
2453 |
+
*Ordered roughly chronologically and by amount of time spent.*
|
2454 |
|
2455 |
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
|
config.json
CHANGED
@@ -2,7 +2,7 @@
|
|
2 |
"apply_residual_connection_post_layernorm": false,
|
3 |
"attention_dropout": 0.0,
|
4 |
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|
5 |
-
"
|
6 |
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|
7 |
"attention_softmax_in_fp32": true,
|
8 |
"pad_token_id": 3,
|
@@ -21,4 +21,4 @@
|
|
21 |
"transformers_version": "4.21.0",
|
22 |
"use_cache": true,
|
23 |
"vocab_size": 250880
|
24 |
-
}
|
|
|
2 |
"apply_residual_connection_post_layernorm": false,
|
3 |
"attention_dropout": 0.0,
|
4 |
"architectures": [
|
5 |
+
"BloomModel"
|
6 |
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|
7 |
"attention_softmax_in_fp32": true,
|
8 |
"pad_token_id": 3,
|
|
|
21 |
"transformers_version": "4.21.0",
|
22 |
"use_cache": true,
|
23 |
"vocab_size": 250880
|
24 |
+
}
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