Quantization made by Richard Erkhov.
bloom-3b - bnb 4bits
- Model creator: https://huggingface.co/bigscience/
- Original model: https://huggingface.co/bigscience/bloom-3b/
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
- task:
type: text-generation
name: text generation
dataset:
name: arc_challenge
type: arc_challenge
metrics:
BLOOM LM
BigScience Large Open-science Open-access Multilingual Language Model
Model Card
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
Send Questions to: bigscience-contact@googlegroups.com
Cite as: BigScience, BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model. International, May 2021-May 2022
Funded by:
The French government.
Hugging Face (website).
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)
DeepSpeed (Github link)
PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)
apex (Github link)
Training
Training logs: Tensorboard link
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: Γle-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
Users of Derivatives of the Model, as described in the License
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.
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
Contain personal information
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:
See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
| Task | Language | Metric | BLOOM-2B5 |
|---|---|---|---|
| arc_challenge | eng | acc β | 0.28 |
| arc_easy | eng | acc β | 0.595 |
| axb (Median of 10 prompts) | eng | acc β | 0.443 |
| axg (Median of 10 prompts) | eng | acc β | 0.5 |
| boolq (Median of 11 prompts) | eng | acc β | 0.617 |
| cb (Median of 15 prompts) | eng | acc β | 0.304 |
| cola (Median of 5 prompts) | eng | acc β | 0.611 |
| copa (Median of 9 prompts) | eng | acc β | 0.63 |
| crows_pairs_english (Median of 6 prompts) | eng | acc β | 0.497 |
| crows_pairs_french (Median of 7 prompts) | fra | acc β | 0.503 |
| diabla (Median of 2 prompts) | eng | acc β | 0.289 |
| gsarti/flores_101_afr | afr | byte_perplexity β | 6.501 |
| gsarti/flores_101_amh | amh | byte_perplexity β | 3.973 |
| gsarti/flores_101_ara | ara | byte_perplexity β | 1.808 |
| gsarti/flores_101_asm | asm | byte_perplexity β | 5.699 |
| gsarti/flores_101_ast | ast | byte_perplexity β | 3.925 |
| gsarti/flores_101_azj | azj | byte_perplexity β | 6.943 |
| gsarti/flores_101_bel | bel | byte_perplexity β | 3.614 |
| gsarti/flores_101_ben | ben | byte_perplexity β | 5.121 |
| gsarti/flores_101_bos | bos | byte_perplexity β | 5.653 |
| gsarti/flores_101_bul | bul | byte_perplexity β | 2.701 |
| gsarti/flores_101_cat | cat | byte_perplexity β | 2.305 |
| gsarti/flores_101_ceb | ceb | byte_perplexity β | 6.291 |
| gsarti/flores_101_ces | ces | byte_perplexity β | 5.447 |
| gsarti/flores_101_ckb | ckb | byte_perplexity β | 3.726 |
| gsarti/flores_101_cym | cym | byte_perplexity β | 12.539 |
| gsarti/flores_101_dan | dan | byte_perplexity β | 5.183 |
| gsarti/flores_101_deu | deu | byte_perplexity β | 3.118 |
| gsarti/flores_101_ell | ell | byte_perplexity β | 2.468 |
| gsarti/flores_101_eng | eng | byte_perplexity β | 2.019 |
| gsarti/flores_101_est | est | byte_perplexity β | 9.117 |
| gsarti/flores_101_fas | fas | byte_perplexity β | 3.058 |
| gsarti/flores_101_fin | fin | byte_perplexity β | 6.847 |
| gsarti/flores_101_fra | fra | byte_perplexity β | 1.998 |
| gsarti/flores_101_ful | ful | byte_perplexity β | 11.466 |
| gsarti/flores_101_gle | gle | byte_perplexity β | 8.681 |
| gsarti/flores_101_glg | glg | byte_perplexity β | 3.03 |
| gsarti/flores_101_guj | guj | byte_perplexity β | 4.955 |
| gsarti/flores_101_hau | hau | byte_perplexity β | 10.758 |
| gsarti/flores_101_heb | heb | byte_perplexity β | 3.6 |
| gsarti/flores_101_hin | hin | byte_perplexity β | 4.713 |
| gsarti/flores_101_hrv | hrv | byte_perplexity β | 5.822 |
| gsarti/flores_101_hun | hun | byte_perplexity β | 6.44 |
| gsarti/flores_101_hye | hye | byte_perplexity β | 3.658 |
| gsarti/flores_101_ibo | ibo | byte_perplexity β | 5.565 |
| gsarti/flores_101_ind | ind | byte_perplexity β | 2.16 |
| gsarti/flores_101_isl | isl | byte_perplexity β | 8.082 |
| gsarti/flores_101_ita | ita | byte_perplexity β | 2.969 |
| gsarti/flores_101_jav | jav | byte_perplexity β | 7.057 |
| gsarti/flores_101_jpn | jpn | byte_perplexity β | 2.776 |
| gsarti/flores_101_kam | kam | byte_perplexity β | 11.073 |
| gsarti/flores_101_kan | kan | byte_perplexity β | 5.552 |
| gsarti/flores_101_kat | kat | byte_perplexity β | 2.523 |
| gsarti/flores_101_kaz | kaz | byte_perplexity β | 3.39 |
| gsarti/flores_101_kea | kea | byte_perplexity β | 8.919 |
| gsarti/flores_101_kir | kir | byte_perplexity β | 3.729 |
| gsarti/flores_101_kor | kor | byte_perplexity β | 3.933 |
| gsarti/flores_101_lao | lao | byte_perplexity β | 2.908 |
| gsarti/flores_101_lav | lav | byte_perplexity β | 7.777 |
| gsarti/flores_101_lin | lin | byte_perplexity β | 7.525 |
| gsarti/flores_101_lit | lit | byte_perplexity β | 7.369 |
| gsarti/flores_101_ltz | ltz | byte_perplexity β | 8.801 |
| gsarti/flores_101_lug | lug | byte_perplexity β | 8.483 |
| gsarti/flores_101_luo | luo | byte_perplexity β | 11.976 |
| gsarti/flores_101_mal | mal | byte_perplexity β | 4.616 |
| gsarti/flores_101_mar | mar | byte_perplexity β | 5.483 |
| gsarti/flores_101_mkd | mkd | byte_perplexity β | 2.966 |
| gsarti/flores_101_mlt | mlt | byte_perplexity β | 15.005 |
| gsarti/flores_101_mon | mon | byte_perplexity β | 3.411 |
| gsarti/flores_101_mri | mri | byte_perplexity β | 7.474 |
| gsarti/flores_101_msa | msa | byte_perplexity β | 2.571 |
| gsarti/flores_101_mya | mya | byte_perplexity β | 2.414 |
| gsarti/flores_101_nld | nld | byte_perplexity β | 4.128 |
| gsarti/flores_101_nob | nob | byte_perplexity β | 5.403 |
| gsarti/flores_101_npi | npi | byte_perplexity β | 5.199 |
| gsarti/flores_101_nso | nso | byte_perplexity β | 8.155 |
| gsarti/flores_101_nya | nya | byte_perplexity β | 8.18 |
| gsarti/flores_101_oci | oci | byte_perplexity β | 4.862 |
| gsarti/flores_101_orm | orm | byte_perplexity β | 12.912 |
| gsarti/flores_101_ory | ory | byte_perplexity β | 5.189 |
| gsarti/flores_101_pan | pan | byte_perplexity β | 4.698 |
| gsarti/flores_101_pol | pol | byte_perplexity β | 4.626 |
| gsarti/flores_101_por | por | byte_perplexity β | 1.975 |
| gsarti/flores_101_pus | pus | byte_perplexity β | 4.496 |
| gsarti/flores_101_ron | ron | byte_perplexity β | 4.965 |
| gsarti/flores_101_rus | rus | byte_perplexity β | 2.05 |
| gsarti/flores_101_slk | slk | byte_perplexity β | 6.451 |
| gsarti/flores_101_slv | slv | byte_perplexity β | 6.62 |
| gsarti/flores_101_sna | sna | byte_perplexity β | 8.462 |
| gsarti/flores_101_snd | snd | byte_perplexity β | 5.466 |
| gsarti/flores_101_som | som | byte_perplexity β | 11.959 |
| gsarti/flores_101_spa | spa | byte_perplexity β | 1.897 |
| gsarti/flores_101_srp | srp | byte_perplexity β | 2.871 |
| gsarti/flores_101_swe | swe | byte_perplexity β | 5.055 |
| gsarti/flores_101_swh | swh | byte_perplexity β | 3.697 |
| gsarti/flores_101_tam | tam | byte_perplexity β | 4.539 |
| gsarti/flores_101_tel | tel | byte_perplexity β | 5.807 |
| gsarti/flores_101_tgk | tgk | byte_perplexity β | 3.599 |
| gsarti/flores_101_tgl | tgl | byte_perplexity β | 5.667 |
| gsarti/flores_101_tha | tha | byte_perplexity β | 2.366 |
| gsarti/flores_101_tur | tur | byte_perplexity β | 4.885 |
| gsarti/flores_101_ukr | ukr | byte_perplexity β | 2.724 |
| gsarti/flores_101_umb | umb | byte_perplexity β | 12.767 |
| gsarti/flores_101_urd | urd | byte_perplexity β | 1.98 |
| gsarti/flores_101_uzb | uzb | byte_perplexity β | 12.002 |
| gsarti/flores_101_vie | vie | byte_perplexity β | 1.766 |
| gsarti/flores_101_wol | wol | byte_perplexity β | 9.144 |
| gsarti/flores_101_xho | xho | byte_perplexity β | 7.403 |
| gsarti/flores_101_yor | yor | byte_perplexity β | 5.913 |
| gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity β | 2.277 |
| gsarti/flores_101_zho_trad | zho_trad | byte_perplexity β | 2.518 |
| gsarti/flores_101_zul | zul | byte_perplexity β | 8.534 |
| headqa | esp | acc β | 0.264 |
| hellaswag | eng | acc β | 0.412 |
| logiqa | eng | acc β | 0.207 |
| mathqa | eng | acc β | 0.25 |
| mc_taco | eng | em β | 0.119 |
| mnli (Median of 15 prompts) | eng | acc β | 0.355 |
| mnli_mismatched (Median of 15 prompts) | eng | acc β | 0.352 |
| mrpc | eng | acc β | 0.586 |
| multirc (Median of 11 prompts) | eng | acc β | 0.538 |
| openbookqa | eng | acc β | 0.216 |
| piqa | eng | acc β | 0.708 |
| prost | eng | acc β | 0.227 |
| pubmedqa | eng | acc β | 0.616 |
| qnli | eng | acc β | 0.507 |
| qqp (Median of 7 prompts) | eng | acc β | 0.384 |
| race | eng | acc β | 0.352 |
| rte (Median of 6 prompts) | eng | acc β | 0.477 |
| sciq | eng | acc β | 0.892 |
| sst (Median of 6 prompts) | eng | acc β | 0.518 |
| triviaqa | eng | acc β | 0.042 |
| tydiqa_primary (Median of 24 prompts) | eng | acc β | 0.301 |
| webqs | eng | acc β | 0.017 |
| wic (Median of 11 prompts) | eng | acc β | 0.502 |
| winogrande | eng | acc β | 0.586 |
| wnli (Median of 6 prompts) | eng | acc β | 0.472 |
| wsc (Median of 11 prompts) | eng | acc β | 0.442 |
| humaneval | python | pass@1 β | 0.155 |
| humaneval | python | pass@10 β | 0.322 |
| humaneval | python | pass@100 β | 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.
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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.
Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.
Human rights: Includes those rights defined in the Universal Declaration of Human Rights.
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
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Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
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 MuΓ±oz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana IliΔ, GΓ©rard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
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