Quantization made by Richard Erkhov.
bloom-3b - bnb 4bits
Original model description:
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
zhs
zht
zu
pipeline_tag: text-generation
model-index:
name: bloom
results:
task:
type: text-generation
name: text generation
dataset:
name: arc_challenge
type: arc_challenge
metrics:
name: acc
type: acc
value: 0.27986348122866894
verified: false
task:
type: text-generation
name: text generation
dataset:
name: arc_easy
type: arc_easy
metrics:
name: acc
type: acc
value: 0.5946969696969697
verified: false
task:
type: text-generation
name: text generation
dataset:
name: axb
type: axb
metrics:
name: acc
type: acc
value: 0.4433876811594203
verified: false
task:
type: text-generation
name: text generation
dataset:
name: axg
type: axg
metrics:
name: acc
type: acc
value: 0.5
verified: false
task:
type: text-generation
name: text generation
dataset:
name: boolq
type: boolq
metrics:
name: acc
type: acc
value: 0.6165137614678899
verified: false
task:
type: text-generation
name: text generation
dataset:
name: cb
type: cb
metrics:
name: acc
type: acc
value: 0.30357142857142855
verified: false
task:
type: text-generation
name: text generation
dataset:
name: cola
type: cola
metrics:
name: acc
type: acc
value: 0.610738255033557
verified: false
task:
type: text-generation
name: text generation
dataset:
name: copa
type: copa
metrics:
name: acc
type: acc
value: 0.63
verified: false
task:
type: text-generation
name: text generation
dataset:
name: crows_pairs_english
type: crows_pairs_english
metrics:
name: acc
type: acc
value: 0.4973166368515206
verified: false
task:
type: text-generation
name: text generation
dataset:
name: crows_pairs_french
type: crows_pairs_french
metrics:
name: acc
type: acc
value: 0.5032796660703638
verified: false
task:
type: text-generation
name: text generation
dataset:
name: diabla
type: diabla
metrics:
name: acc
type: acc
value: 0.28888308977035493
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_afr
type: gsarti/flores_101_afr
metrics:
name: byte_perplexity
type: byte_perplexity
value: 6.500798737976343
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_amh
type: gsarti/flores_101_amh
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.9726863338897145
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ara
type: gsarti/flores_101_ara
metrics:
name: byte_perplexity
type: byte_perplexity
value: 1.8083841089875814
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_asm
type: gsarti/flores_101_asm
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.699102962086425
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ast
type: gsarti/flores_101_ast
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.9252047073429384
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_azj
type: gsarti/flores_101_azj
metrics:
name: byte_perplexity
type: byte_perplexity
value: 6.942805054270002
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_bel
type: gsarti/flores_101_bel
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.614136245847082
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ben
type: gsarti/flores_101_ben
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.121491534300969
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_bos
type: gsarti/flores_101_bos
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.653353469118798
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_bul
type: gsarti/flores_101_bul
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.7014693938055068
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_cat
type: gsarti/flores_101_cat
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.305190041967345
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ceb
type: gsarti/flores_101_ceb
metrics:
name: byte_perplexity
type: byte_perplexity
value: 6.291000321323428
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ces
type: gsarti/flores_101_ces
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.447322753586386
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ckb
type: gsarti/flores_101_ckb
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.7255124939234765
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_cym
type: gsarti/flores_101_cym
metrics:
name: byte_perplexity
type: byte_perplexity
value: 12.539424151448149
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_dan
type: gsarti/flores_101_dan
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.183309001005672
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_deu
type: gsarti/flores_101_deu
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.1180422286591347
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ell
type: gsarti/flores_101_ell
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.467943456164706
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_eng
type: gsarti/flores_101_eng
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.018740628193298
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_est
type: gsarti/flores_101_est
metrics:
name: byte_perplexity
type: byte_perplexity
value: 9.11654425176368
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_fas
type: gsarti/flores_101_fas
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.058009097116482
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_fin
type: gsarti/flores_101_fin
metrics:
name: byte_perplexity
type: byte_perplexity
value: 6.847047959628553
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_fra
type: gsarti/flores_101_fra
metrics:
name: byte_perplexity
type: byte_perplexity
value: 1.9975177011840075
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ful
type: gsarti/flores_101_ful
metrics:
name: byte_perplexity
type: byte_perplexity
value: 11.465912731488828
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_gle
type: gsarti/flores_101_gle
metrics:
name: byte_perplexity
type: byte_perplexity
value: 8.681491663539422
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_glg
type: gsarti/flores_101_glg
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.029991089015508
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_guj
type: gsarti/flores_101_guj
metrics:
name: byte_perplexity
type: byte_perplexity
value: 4.955224230286231
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hau
type: gsarti/flores_101_hau
metrics:
name: byte_perplexity
type: byte_perplexity
value: 10.758347356372159
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_heb
type: gsarti/flores_101_heb
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.6004478129801667
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hin
type: gsarti/flores_101_hin
metrics:
name: byte_perplexity
type: byte_perplexity
value: 4.712530650588064
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hrv
type: gsarti/flores_101_hrv
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.822418943372185
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hun
type: gsarti/flores_101_hun
metrics:
name: byte_perplexity
type: byte_perplexity
value: 6.440482646965992
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hye
type: gsarti/flores_101_hye
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.657718918347166
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ibo
type: gsarti/flores_101_ibo
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.564814003872672
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ind
type: gsarti/flores_101_ind
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.1597101468869373
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_isl
type: gsarti/flores_101_isl
metrics:
name: byte_perplexity
type: byte_perplexity
value: 8.082349269518136
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ita
type: gsarti/flores_101_ita
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.9687591414176207
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_jav
type: gsarti/flores_101_jav
metrics:
name: byte_perplexity
type: byte_perplexity
value: 7.0573805415708994
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_jpn
type: gsarti/flores_101_jpn
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.7758864197116933
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kam
type: gsarti/flores_101_kam
metrics:
name: byte_perplexity
type: byte_perplexity
value: 11.072949642861332
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kan
type: gsarti/flores_101_kan
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.551730651007082
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kat
type: gsarti/flores_101_kat
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.522630524283745
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kaz
type: gsarti/flores_101_kaz
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.3901748516975574
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kea
type: gsarti/flores_101_kea
metrics:
name: byte_perplexity
type: byte_perplexity
value: 8.918534182590863
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kir
type: gsarti/flores_101_kir
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.729278369847201
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kor
type: gsarti/flores_101_kor
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.932884847226212
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lao
type: gsarti/flores_101_lao
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.9077314760849924
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lav
type: gsarti/flores_101_lav
metrics:
name: byte_perplexity
type: byte_perplexity
value: 7.777221919194806
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lin
type: gsarti/flores_101_lin
metrics:
name: byte_perplexity
type: byte_perplexity
value: 7.524842908050988
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lit
type: gsarti/flores_101_lit
metrics:
name: byte_perplexity
type: byte_perplexity
value: 7.369179434621725
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ltz
type: gsarti/flores_101_ltz
metrics:
name: byte_perplexity
type: byte_perplexity
value: 8.801059747949214
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lug
type: gsarti/flores_101_lug
metrics:
name: byte_perplexity
type: byte_perplexity
value: 8.483203026364786
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_luo
type: gsarti/flores_101_luo
metrics:
name: byte_perplexity
type: byte_perplexity
value: 11.975963093623681
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mal
type: gsarti/flores_101_mal
metrics:
name: byte_perplexity
type: byte_perplexity
value: 4.615948455160037
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mar
type: gsarti/flores_101_mar
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.483253482821379
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mkd
type: gsarti/flores_101_mkd
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.9656732291754087
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mlt
type: gsarti/flores_101_mlt
metrics:
name: byte_perplexity
type: byte_perplexity
value: 15.004773437665275
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mon
type: gsarti/flores_101_mon
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.410598542315402
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mri
type: gsarti/flores_101_mri
metrics:
name: byte_perplexity
type: byte_perplexity
value: 7.474035895661322
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_msa
type: gsarti/flores_101_msa
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.5710001772665634
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mya
type: gsarti/flores_101_mya
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.413577969878331
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_nld
type: gsarti/flores_101_nld
metrics:
name: byte_perplexity
type: byte_perplexity
value: 4.127831721885065
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_nob
type: gsarti/flores_101_nob
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.402763169129877
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_npi
type: gsarti/flores_101_npi
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.199342701937889
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_nso
type: gsarti/flores_101_nso
metrics:
name: byte_perplexity
type: byte_perplexity
value: 8.154626800955667
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_nya
type: gsarti/flores_101_nya
metrics:
name: byte_perplexity
type: byte_perplexity
value: 8.179860208369393
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_oci
type: gsarti/flores_101_oci
metrics:
name: byte_perplexity
type: byte_perplexity
value: 4.8617357393685845
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_orm
type: gsarti/flores_101_orm
metrics:
name: byte_perplexity
type: byte_perplexity
value: 12.911595421079408
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ory
type: gsarti/flores_101_ory
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.189421861225964
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_pan
type: gsarti/flores_101_pan
metrics:
name: byte_perplexity
type: byte_perplexity
value: 4.698477289331806
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_pol
type: gsarti/flores_101_pol
metrics:
name: byte_perplexity
type: byte_perplexity
value: 4.625550458479643
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_por
type: gsarti/flores_101_por
metrics:
name: byte_perplexity
type: byte_perplexity
value: 1.9754515986213523
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_pus
type: gsarti/flores_101_pus
metrics:
name: byte_perplexity
type: byte_perplexity
value: 4.4963371422771585
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ron
type: gsarti/flores_101_ron
metrics:
name: byte_perplexity
type: byte_perplexity
value: 4.965456830031304
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_rus
type: gsarti/flores_101_rus
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.0498020542445303
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_slk
type: gsarti/flores_101_slk
metrics:
name: byte_perplexity
type: byte_perplexity
value: 6.450822127057479
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_slv
type: gsarti/flores_101_slv
metrics:
name: byte_perplexity
type: byte_perplexity
value: 6.620252120186232
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_sna
type: gsarti/flores_101_sna
metrics:
name: byte_perplexity
type: byte_perplexity
value: 8.462166771382726
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_snd
type: gsarti/flores_101_snd
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.466066951221973
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_som
type: gsarti/flores_101_som
metrics:
name: byte_perplexity
type: byte_perplexity
value: 11.95918054093392
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_spa
type: gsarti/flores_101_spa
metrics:
name: byte_perplexity
type: byte_perplexity
value: 1.8965140104323535
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_srp
type: gsarti/flores_101_srp
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.871214785885079
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_swe
type: gsarti/flores_101_swe
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.054972008155866
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_swh
type: gsarti/flores_101_swh
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.6973091886730676
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tam
type: gsarti/flores_101_tam
metrics:
name: byte_perplexity
type: byte_perplexity
value: 4.539493400469833
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tel
type: gsarti/flores_101_tel
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.807499987508966
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tgk
type: gsarti/flores_101_tgk
metrics:
name: byte_perplexity
type: byte_perplexity
value: 3.5994818827380426
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tgl
type: gsarti/flores_101_tgl
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.667053833119858
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tha
type: gsarti/flores_101_tha
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.365940201944242
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tur
type: gsarti/flores_101_tur
metrics:
name: byte_perplexity
type: byte_perplexity
value: 4.885014749844601
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ukr
type: gsarti/flores_101_ukr
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.7240934990288483
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_umb
type: gsarti/flores_101_umb
metrics:
name: byte_perplexity
type: byte_perplexity
value: 12.766915508610673
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_urd
type: gsarti/flores_101_urd
metrics:
name: byte_perplexity
type: byte_perplexity
value: 1.9797467071381232
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_uzb
type: gsarti/flores_101_uzb
metrics:
name: byte_perplexity
type: byte_perplexity
value: 12.002337637722146
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_vie
type: gsarti/flores_101_vie
metrics:
name: byte_perplexity
type: byte_perplexity
value: 1.76578415476397
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_wol
type: gsarti/flores_101_wol
metrics:
name: byte_perplexity
type: byte_perplexity
value: 9.144285650306488
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_xho
type: gsarti/flores_101_xho
metrics:
name: byte_perplexity
type: byte_perplexity
value: 7.403240538286952
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_yor
type: gsarti/flores_101_yor
metrics:
name: byte_perplexity
type: byte_perplexity
value: 5.91272037551173
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_zho_simpl
type: gsarti/flores_101_zho_simpl
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.2769070822768533
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_zho_trad
type: gsarti/flores_101_zho_trad
metrics:
name: byte_perplexity
type: byte_perplexity
value: 2.5180582198242383
verified: false
task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_zul
type: gsarti/flores_101_zul
metrics:
name: byte_perplexity
type: byte_perplexity
value: 8.53353320693145
verified: false
task:
type: text-generation
name: text generation
dataset:
name: headqa
type: headqa
metrics:
name: acc
type: acc
value: 0.26440554339897887
verified: false
task:
type: text-generation
name: text generation
dataset:
name: hellaswag
type: hellaswag
metrics:
name: acc
type: acc
value: 0.41236805417247563
verified: false
task:
type: text-generation
name: text generation
dataset:
name: logiqa
type: logiqa
metrics:
name: acc
type: acc
value: 0.2073732718894009
verified: false
task:
type: text-generation
name: text generation
dataset:
name: mathqa
type: mathqa
metrics:
name: acc
type: acc
value: 0.24958123953098826
verified: false
task:
type: text-generation
name: text generation
dataset:
name: mc_taco
type: mc_taco
metrics:
name: em
type: em
value: 0.11936936936936937
verified: false
task:
type: text-generation
name: text generation
dataset:
name: mnli
type: mnli
metrics:
name: acc
type: acc
value: 0.35496688741721855
verified: false
task:
type: text-generation
name: text generation
dataset:
name: mnli_mismatched
type: mnli_mismatched
metrics:
name: acc
type: acc
value: 0.35211554109031734
verified: false
task:
type: text-generation
name: text generation
dataset:
name: mrpc
type: mrpc
metrics:
name: acc
type: acc
value: 0.5857843137254902
verified: false
task:
type: text-generation
name: text generation
dataset:
name: multirc
type: multirc
metrics:
name: acc
type: acc
value: 0.5375412541254125
verified: false
task:
type: text-generation
name: text generation
dataset:
name: openbookqa
type: openbookqa
metrics:
name: acc
type: acc
value: 0.216
verified: false
task:
type: text-generation
name: text generation
dataset:
name: piqa
type: piqa
metrics:
name: acc
type: acc
value: 0.7078346028291621
verified: false
task:
type: text-generation
name: text generation
dataset:
name: prost
type: prost
metrics:
name: acc
type: acc
value: 0.22683603757472245
verified: false
task:
type: text-generation
name: text generation
dataset:
name: pubmedqa
type: pubmedqa
metrics:
name: acc
type: acc
value: 0.616
verified: false
task:
type: text-generation
name: text generation
dataset:
name: qnli
type: qnli
metrics:
name: acc
type: acc
value: 0.5072304594545122
verified: false
task:
type: text-generation
name: text generation
dataset:
name: qqp
type: qqp
metrics:
name: acc
type: acc
value: 0.3842443729903537
verified: false
task:
type: text-generation
name: text generation
dataset:
name: race
type: race
metrics:
name: acc
type: acc
value: 0.3521531100478469
verified: false
task:
type: text-generation
name: text generation
dataset:
name: rte
type: rte
metrics:
name: acc
type: acc
value: 0.47653429602888087
verified: false
task:
type: text-generation
name: text generation
dataset:
name: sciq
type: sciq
metrics:
name: acc
type: acc
value: 0.892
verified: false
task:
type: text-generation
name: text generation
dataset:
name: sst
type: sst
metrics:
name: acc
type: acc
value: 0.5177752293577982
verified: false
task:
type: text-generation
name: text generation
dataset:
name: triviaqa
type: triviaqa
metrics:
name: acc
type: acc
value: 0.041633518960487934
verified: false
task:
type: text-generation
name: text generation
dataset:
name: tydiqa_primary
type: tydiqa_primary
metrics:
name: acc
type: acc
value: 0.3011337608795236
verified: false
task:
type: text-generation
name: text generation
dataset:
name: webqs
type: webqs
metrics:
name: acc
type: acc
value: 0.01673228346456693
verified: false
task:
type: text-generation
name: text generation
dataset:
name: wic
type: wic
metrics:
name: acc
type: acc
value: 0.5015673981191222
verified: false
task:
type: text-generation
name: text generation
dataset:
name: winogrande
type: winogrande
metrics:
name: acc
type: acc
value: 0.5864246250986582
verified: false
task:
type: text-generation
name: text generation
dataset:
name: wnli
type: wnli
metrics:
name: acc
type: acc
value: 0.471830985915493
verified: false
task:
type: text-generation
name: text generation
dataset:
name: wsc
type: wsc
metrics:
name: acc
type: acc
value: 0.4423076923076923
verified: false
task:
type: text-generation
name: text generation
dataset:
name: humaneval
type: humaneval
metrics:
name: pass@1
type: pass@1
value: 0.15524390243902436
verified: false
name: pass@10
type: pass@10
value: 0.3220367632383857
verified: false
name: pass@100
type: pass@100
value: 0.5545431515723145
verified: false
BLOOM LM
BigScience Large Open-science Open-access Multilingual Language Model Model Card
BigScience Logo Version 1.0 / 26.May.2022
Table of Contents
Model Details Uses Training Data Risks and Limitations Evaluation Recommendations Glossary and Calculations More Information Model Card Authors
Model Details
Basics
This section provides information for anyone who wants to know about the model.
Click to expand Developed by: BigScience (
website )
All collaborators are either volunteers or have an agreement with their employer. (Further breakdown of participants forthcoming.)
Model Type: Transformer-based Language Model
Version: 1.0.0
Languages: Multiple; see
training data License: RAIL License v1.0 (
link )
Release Date Estimate: Monday, 11.July.2022
Cite as: BigScience, BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model . International, May 2021-May 2022
Funded by:
The French government.
Organizations of contributors. (Further breakdown of organizations forthcoming.)
Technical Specifications
This section provides information for people who work on model development.
Click to expand Please see
the BLOOM training README for full details on replicating training.
Model Architecture: Modified from Megatron-LM GPT2 (see
paper ,
BLOOM Megatron code ):
Decoder-only architecture
Layer normalization applied to word embeddings layer (
StableEmbedding; see
code ,
paper )
ALiBI positional encodings (see
paper ), with GeLU activation functions
3,002,557,440 parameters:
642,252,800 embedding parameters
30 layers, 32 attention heads
Hidden layers are 2560-dimensional
Sequence length of 2048 tokens used (see
BLOOM tokenizer ,
tokenizer description )
Objective Function: Cross Entropy with mean reduction (see
API documentation ).
Compute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see
announcement ).
Hardware: 384 A100 80GB GPUs (48 nodes):
Additional 32 A100 80GB GPUs (4 nodes) in reserve
8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
CPU: AMD
CPU memory: 512GB per node
GPU memory: 640GB per node
Inter-node connect: Omni-Path Architecture (OPA)
NCCL-communications network: a fully dedicated subnet
Disc IO network: shared network with other types of nodes
Software:
Megatron-DeepSpeed (
Github link )
PyTorch (pytorch-1.11 w/ CUDA-11.5; see
Github link )
Training
Number of epochs: 1 (current target )
Dates:
Started 11th March, 2022 11:42am PST
Ended 5th July, 2022
Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
Server training location: Île-de-France, France
Tokenization The BLOOM tokenizer (
link ) is a learned subword tokenizer trained using:
A byte-level Byte Pair Encoding (BPE) algorithm
A simple pre-tokenization rule, no normalization
A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
Environmental Impact
Click to expand The training supercomputer, Jean Zay (
website ), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
Estimated carbon emissions: (Forthcoming upon completion of training.)
Estimated electricity usage: (Forthcoming upon completion of training.)
Uses
This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
It provides information for anyone considering using the model or who is affected by the model.
Click to expand Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
Direct Use
Text generation
Exploring characteristics of language generated by a language model
Examples: Cloze tests, counterfactuals, generations with reframings
Downstream Use
Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
Misuse and Out-of-scope Use
This section addresses what users ought not do with the model.
See the
BLOOM License , Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
Out-of-scope Uses Using the model in
high-stakes settings is out of scope for this model. The model is not designed for
critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
Out-of-scope Uses Include:
Usage in biomedical domains, political and legal domains, or finance domains
Usage for evaluating or scoring individuals, such as for employment, education, or credit
Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
Misuse Intentionally using the model for harm, violating
human rights , or other kinds of malicious activities, is a misuse of this model. This includes:
Spam generation
Disinformation and influence operations
Disparagement and defamation
Harassment and abuse
Unconsented impersonation and imitation
Unconsented surveillance
Generating content without attribution to the model, as specified in the
RAIL License, Use Restrictions
Intended Users
Direct Users
General Public
Researchers
Students
Educators
Engineers/developers
Non-commercial entities
Community advocates, including human and civil rights groups
Indirect Users
Users of derivatives created by Direct Users, such as those using software with an
intended use
Others Affected (Parties Prenantes)
People and groups referred to by the LLM
People and groups exposed to outputs of, or decisions based on, the LLM
People and groups whose original work is included in the LLM
Training Data
This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.
Click to expand Details for each dataset are provided in individual
Data Cards .
Training data includes:
45 natural languages
12 programming languages
In 1.5TB of pre-processed text, converted into 350B unique tokens (see
the tokenizer section for more.)
Languages The pie chart shows the distribution of languages in training data.
pie chart showing the distribution of languages in training data The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
Click to expand Niger Congo Percentage Indic Percentage Chi Tumbuka 0.00002 Assamese 0.01 Kikuyu 0.00004 Odia 0.04 Bambara 0.00004 Gujarati 0.04 Akan 0.00007 Marathi 0.05 Xitsonga 0.00007 Punjabi 0.05 Sesotho 0.00007 Kannada 0.06 Chi Chewa 0.0001 Nepali 0.07 Setswana 0.0002 Telugu 0.09 Northern Sotho 0.0002 Malayalam 0.10 Fon 0.0002 Urdu 0.10 Kirundi 0.0003 Tamil 0.20 Wolof 0.0004 Bengali 0.50 Kuganda 0.0004 Hindi 0.70 Chi Shona 0.001 Isi Zulu 0.001 Igbo 0.001 Xhosa 0.001 Kinyarwanda 0.003 Yoruba 0.006 Swahili 0.02
The following table shows the distribution of programming languages.
Click to expand Extension Language Number of files java Java 5,407,724 php PHP 4,942,186 cpp C++ 2,503,930 py Python 2,435,072 js JavaScript 1,905,518 cs C# 1,577,347 rb Ruby 6,78,413 cc C++ 443,054 hpp C++ 391,048 lua Lua 352,317 go GO 227,763 ts TypeScript 195,254 C C 134,537 scala Scala 92,052 hh C++ 67,161 H C++ 55,899 tsx TypeScript 33,107 rs Rust 29,693 phpt PHP 9,702 c++ C++ 1,342 h++ C++ 791 php3 PHP 540 phps PHP 270 php5 PHP 166 php4 PHP 29
Risks and Limitations
This section identifies foreseeable harms and misunderstandings.
Click to expand Model may:
Overrepresent some viewpoints and underrepresent others
Contain stereotypes
Generate:
Hateful, abusive, or violent language
Discriminatory or prejudicial language
Content that may not be appropriate for all settings, including sexual content
Make errors, including producing incorrect information as if it were factual
Generate irrelevant or repetitive outputs
Evaluation
This section describes the evaluation protocols and provides the results.
Click to expand Metrics
This section describes the different ways performance is calculated and why.
Includes:
Metric Why chosen Perplexity Standard metric for quantifying model improvements during training Cross Entropy Loss Standard objective for language models.
And multiple different metrics for specific tasks. (More evaluation metrics forthcoming upon completion of evaluation protocol.)
Factors
This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.
Language, such as English or Yoruba
Domain, such as newswire or stories
Demographic characteristics, such as gender or nationality
Results
Results are based on the Factors and Metrics . Zero-shot evaluations:
Task Language Metric BLOOM-2B5 arc_challenge eng acc ↑ 0.28 arc_easy eng acc ↑ 0.595 axb (Median of 10 prompts) eng acc ↑ 0.443 axg (Median of 10 prompts) eng acc ↑ 0.5 boolq (Median of 11 prompts) eng acc ↑ 0.617 cb (Median of 15 prompts) eng acc ↑ 0.304 cola (Median of 5 prompts) eng acc ↑ 0.611 copa (Median of 9 prompts) eng acc ↑ 0.63 crows_pairs_english (Median of 6 prompts) eng acc ↑ 0.497 crows_pairs_french (Median of 7 prompts) fra acc ↑ 0.503 diabla (Median of 2 prompts) eng acc ↑ 0.289 gsarti/flores_101_afr afr byte_perplexity ↓ 6.501 gsarti/flores_101_amh amh byte_perplexity ↓ 3.973 gsarti/flores_101_ara ara byte_perplexity ↓ 1.808 gsarti/flores_101_asm asm byte_perplexity ↓ 5.699 gsarti/flores_101_ast ast byte_perplexity ↓ 3.925 gsarti/flores_101_azj azj byte_perplexity ↓ 6.943 gsarti/flores_101_bel bel byte_perplexity ↓ 3.614 gsarti/flores_101_ben ben byte_perplexity ↓ 5.121 gsarti/flores_101_bos bos byte_perplexity ↓ 5.653 gsarti/flores_101_bul bul byte_perplexity ↓ 2.701 gsarti/flores_101_cat cat byte_perplexity ↓ 2.305 gsarti/flores_101_ceb ceb byte_perplexity ↓ 6.291 gsarti/flores_101_ces ces byte_perplexity ↓ 5.447 gsarti/flores_101_ckb ckb byte_perplexity ↓ 3.726 gsarti/flores_101_cym cym byte_perplexity ↓ 12.539 gsarti/flores_101_dan dan byte_perplexity ↓ 5.183 gsarti/flores_101_deu deu byte_perplexity ↓ 3.118 gsarti/flores_101_ell ell byte_perplexity ↓ 2.468 gsarti/flores_101_eng eng byte_perplexity ↓ 2.019 gsarti/flores_101_est est byte_perplexity ↓ 9.117 gsarti/flores_101_fas fas byte_perplexity ↓ 3.058 gsarti/flores_101_fin fin byte_perplexity ↓ 6.847 gsarti/flores_101_fra fra byte_perplexity ↓ 1.998 gsarti/flores_101_ful ful byte_perplexity ↓ 11.466 gsarti/flores_101_gle gle byte_perplexity ↓ 8.681 gsarti/flores_101_glg glg byte_perplexity ↓ 3.03 gsarti/flores_101_guj guj byte_perplexity ↓ 4.955 gsarti/flores_101_hau hau byte_perplexity ↓ 10.758 gsarti/flores_101_heb heb byte_perplexity ↓ 3.6 gsarti/flores_101_hin hin byte_perplexity ↓ 4.713 gsarti/flores_101_hrv hrv byte_perplexity ↓ 5.822 gsarti/flores_101_hun hun byte_perplexity ↓ 6.44 gsarti/flores_101_hye hye byte_perplexity ↓ 3.658 gsarti/flores_101_ibo ibo byte_perplexity ↓ 5.565 gsarti/flores_101_ind ind byte_perplexity ↓ 2.16 gsarti/flores_101_isl isl byte_perplexity ↓ 8.082 gsarti/flores_101_ita ita byte_perplexity ↓ 2.969 gsarti/flores_101_jav jav byte_perplexity ↓ 7.057 gsarti/flores_101_jpn jpn byte_perplexity ↓ 2.776 gsarti/flores_101_kam kam byte_perplexity ↓ 11.073 gsarti/flores_101_kan kan byte_perplexity ↓ 5.552 gsarti/flores_101_kat kat byte_perplexity ↓ 2.523 gsarti/flores_101_kaz kaz byte_perplexity ↓ 3.39 gsarti/flores_101_kea kea byte_perplexity ↓ 8.919 gsarti/flores_101_kir kir byte_perplexity ↓ 3.729 gsarti/flores_101_kor kor byte_perplexity ↓ 3.933 gsarti/flores_101_lao lao byte_perplexity ↓ 2.908 gsarti/flores_101_lav lav byte_perplexity ↓ 7.777 gsarti/flores_101_lin lin byte_perplexity ↓ 7.525 gsarti/flores_101_lit lit byte_perplexity ↓ 7.369 gsarti/flores_101_ltz ltz byte_perplexity ↓ 8.801 gsarti/flores_101_lug lug byte_perplexity ↓ 8.483 gsarti/flores_101_luo luo byte_perplexity ↓ 11.976 gsarti/flores_101_mal mal byte_perplexity ↓ 4.616 gsarti/flores_101_mar mar byte_perplexity ↓ 5.483 gsarti/flores_101_mkd mkd byte_perplexity ↓ 2.966 gsarti/flores_101_mlt mlt byte_perplexity ↓ 15.005 gsarti/flores_101_mon mon byte_perplexity ↓ 3.411 gsarti/flores_101_mri mri byte_perplexity ↓ 7.474 gsarti/flores_101_msa msa byte_perplexity ↓ 2.571 gsarti/flores_101_mya mya byte_perplexity ↓ 2.414 gsarti/flores_101_nld nld byte_perplexity ↓ 4.128 gsarti/flores_101_nob nob byte_perplexity ↓ 5.403 gsarti/flores_101_npi npi byte_perplexity ↓ 5.199 gsarti/flores_101_nso nso byte_perplexity ↓ 8.155 gsarti/flores_101_nya nya byte_perplexity ↓ 8.18 gsarti/flores_101_oci oci byte_perplexity ↓ 4.862 gsarti/flores_101_orm orm byte_perplexity ↓ 12.912 gsarti/flores_101_ory ory byte_perplexity ↓ 5.189 gsarti/flores_101_pan pan byte_perplexity ↓ 4.698 gsarti/flores_101_pol pol byte_perplexity ↓ 4.626 gsarti/flores_101_por por byte_perplexity ↓ 1.975 gsarti/flores_101_pus pus byte_perplexity ↓ 4.496 gsarti/flores_101_ron ron byte_perplexity ↓ 4.965 gsarti/flores_101_rus rus byte_perplexity ↓ 2.05 gsarti/flores_101_slk slk byte_perplexity ↓ 6.451 gsarti/flores_101_slv slv byte_perplexity ↓ 6.62 gsarti/flores_101_sna sna byte_perplexity ↓ 8.462 gsarti/flores_101_snd snd byte_perplexity ↓ 5.466 gsarti/flores_101_som som byte_perplexity ↓ 11.959 gsarti/flores_101_spa spa byte_perplexity ↓ 1.897 gsarti/flores_101_srp srp byte_perplexity ↓ 2.871 gsarti/flores_101_swe swe byte_perplexity ↓ 5.055 gsarti/flores_101_swh swh byte_perplexity ↓ 3.697 gsarti/flores_101_tam tam byte_perplexity ↓ 4.539 gsarti/flores_101_tel tel byte_perplexity ↓ 5.807 gsarti/flores_101_tgk tgk byte_perplexity ↓ 3.599 gsarti/flores_101_tgl tgl byte_perplexity ↓ 5.667 gsarti/flores_101_tha tha byte_perplexity ↓ 2.366 gsarti/flores_101_tur tur byte_perplexity ↓ 4.885 gsarti/flores_101_ukr ukr byte_perplexity ↓ 2.724 gsarti/flores_101_umb umb byte_perplexity ↓ 12.767 gsarti/flores_101_urd urd byte_perplexity ↓ 1.98 gsarti/flores_101_uzb uzb byte_perplexity ↓ 12.002 gsarti/flores_101_vie vie byte_perplexity ↓ 1.766 gsarti/flores_101_wol wol byte_perplexity ↓ 9.144 gsarti/flores_101_xho xho byte_perplexity ↓ 7.403 gsarti/flores_101_yor yor byte_perplexity ↓ 5.913 gsarti/flores_101_zho_simpl zho_simpl byte_perplexity ↓ 2.277 gsarti/flores_101_zho_trad zho_trad byte_perplexity ↓ 2.518 gsarti/flores_101_zul zul byte_perplexity ↓ 8.534 headqa esp acc ↑ 0.264 hellaswag eng acc ↑ 0.412 logiqa eng acc ↑ 0.207 mathqa eng acc ↑ 0.25 mc_taco eng em ↑ 0.119 mnli (Median of 15 prompts) eng acc ↑ 0.355 mnli_mismatched (Median of 15 prompts) eng acc ↑ 0.352 mrpc eng acc ↑ 0.586 multirc (Median of 11 prompts) eng acc ↑ 0.538 openbookqa eng acc ↑ 0.216 piqa eng acc ↑ 0.708 prost eng acc ↑ 0.227 pubmedqa eng acc ↑ 0.616 qnli eng acc ↑ 0.507 qqp (Median of 7 prompts) eng acc ↑ 0.384 race eng acc ↑ 0.352 rte (Median of 6 prompts) eng acc ↑ 0.477 sciq eng acc ↑ 0.892 sst (Median of 6 prompts) eng acc ↑ 0.518 triviaqa eng acc ↑ 0.042 tydiqa_primary (Median of 24 prompts) eng acc ↑ 0.301 webqs eng acc ↑ 0.017 wic (Median of 11 prompts) eng acc ↑ 0.502 winogrande eng acc ↑ 0.586 wnli (Median of 6 prompts) eng acc ↑ 0.472 wsc (Median of 11 prompts) eng acc ↑ 0.442 humaneval python pass@1 ↑ 0.155 humaneval python pass@10 ↑ 0.322 humaneval python pass@100 ↑ 0.555
Train-time Evaluation:
As of 25.May.2022, 15:00 PST:
Training Loss: 2.0
Validation Loss: 2.2
Perplexity: 8.9
Recommendations
This section provides information on warnings and potential mitigations.
Click to expand
Indirect users should be made aware when the content they're working with is created by the LLM.
Users should be aware of
Risks and Limitations , and include an appropriate age disclaimer or blocking interface as necessary.
Models pretrained with the LLM should include an updated Model Card.
Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
Glossary and Calculations
This section defines common terms and how metrics are calculated.
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Loss: A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
Perplexity: This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
High-stakes settings: Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed
Artificial Intelligence (AI) Act .
Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as "
personal data " in the
European Union's General Data Protection Regulation ; and "personal information" in the Republic of South Africa's
Protection of Personal Information Act , The People's Republic of China's
Personal information protection law .
Sensitive characteristics: This includes specifically protected categories in human rights (see
UHDR, Article 2 ) and personal information regulation (see GDPR,
Article 9; Protection of Personal Information Act, Chapter 1 )
Deception: Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
More Information
Click to expand Dataset Creation
Technical Specifications
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions):
https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md Initial Results
Initial prompting experiments using interim checkpoints:
https://huggingface.co/spaces/bigscience/bloom-book
Model Card Authors
Ordered roughly chronologically and by amount of time spent.
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
