File size: 42,501 Bytes
893002e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
base_model: meta-llama/Meta-Llama-3.1-405B
license: llama3.1
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\
  \ Release Date: July 23, 2024\n\"Agreement\" means the terms and conditions for\
  \ use, reproduction, distribution and modification of the  Llama Materials set forth\
  \ herein.\n\"Documentation\" means the specifications, manuals and documentation\
  \ accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.\n\
  \"Licensee\" or \"you\" means you, or your employer or any other person or entity\
  \ (if you are entering into this Agreement on such person or entity’s behalf), of\
  \ the age required under applicable laws, rules or regulations to provide legal\
  \ consent and that has legal authority to bind your employer or such other person\
  \ or entity if you are entering in this Agreement on their behalf.\n\"Llama 3.1\"\
  \ means the foundational large language models and software and algorithms, including\
  \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\
  \ code, fine-tuning enabling code and other elements of the foregoing distributed\
  \ by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means,\
  \ collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion\
  \ thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms\
  \ Ireland Limited (if you are located in or, if you are an entity, your principal\
  \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you\
  \ are located outside of the EEA or Switzerland).\n   \n1. License Rights and Redistribution.\n\
  a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable\
  \ and royalty-free limited license under Meta’s intellectual property or other rights\
  \ owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy,\
  \ create derivative works of, and make modifications to the Llama Materials.\nb.\
  \ Redistribution and Use.\ni. If you distribute or make available the Llama Materials\
  \ (or any derivative works thereof), or a product or service (including another\
  \ AI model) that contains any of them, you shall (A) provide a copy of this Agreement\
  \ with any such Llama Materials; and (B) prominently display “Built with Llama”\
  \ on a related website, user interface, blogpost, about page, or product documentation.\
  \ If you use the Llama Materials or any outputs or results of the Llama Materials\
  \ to create, train, fine tune, or otherwise improve an AI model, which is distributed\
  \ or made available, you shall also include “Llama” at the beginning of any such\
  \ AI model name.\nii. If you receive Llama Materials, or any derivative works thereof,\
  \ from a Licensee as part  of an integrated end user product, then Section 2 of\
  \ this Agreement will not apply to you.\niii. You must retain in all copies of the\
  \ Llama Materials that you distribute the following attribution notice within a\
  \ “Notice” text file distributed as a part of such copies: “Llama 3.1 is licensed\
  \ under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights\
  \ Reserved.”\niv. Your use of the Llama Materials must comply with applicable laws\
  \ and regulations (including trade compliance laws and regulations) and adhere to\
  \ the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3_1/use-policy),\
  \ which is hereby incorporated by reference into this Agreement.\n2. Additional\
  \ Commercial Terms. If, on the Llama 3.1 version release date, the monthly active\
  \ users of the products or services made available by or for Licensee, or Licensee’s\
  \ affiliates, is greater than 700 million monthly active users in the preceding\
  \ calendar month, you must request a license from Meta, which Meta may grant to\
  \ you in its sole discretion, and you are not authorized to exercise any of the\
  \ rights under this Agreement unless or until Meta otherwise expressly grants you\
  \ such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE\
  \ LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS”\
  \ BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY\
  \ KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\
  \ OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.\
  \ YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\
  \ THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA\
  \ MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT\
  \ WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN\
  \ CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS\
  \ AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,\
  \ EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED\
  \ OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No\
  \ trademark licenses are granted under this Agreement, and in connection with the\
  \ Llama Materials, neither Meta nor Licensee may use any name or mark owned by or\
  \ associated with the other or any of its affiliates, except as required for reasonable\
  \ and customary use in describing and redistributing the Llama Materials or as set\
  \ forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the\
  \ “Mark”) solely as required to comply with the last sentence of Section 1.b.i.\
  \ You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/\
  \ ). All goodwill arising out of your use of the Mark will inure to the benefit\
  \ of Meta.\nb. Subject to Meta’s ownership of Llama Materials and derivatives made\
  \ by or for Meta, with respect to any derivative works and modifications of the\
  \ Llama Materials that are made by you, as between you and Meta, you are and will\
  \ be the owner of such derivative works and modifications.\nc. If you institute\
  \ litigation or other proceedings against Meta or any entity (including a cross-claim\
  \ or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs\
  \ or results, or any portion of any of the foregoing, constitutes infringement of\
  \ intellectual property or other rights owned or licensable by you, then any licenses\
  \ granted to you under this Agreement shall terminate as of the date such litigation\
  \ or claim is filed or instituted. You will indemnify and hold harmless Meta from\
  \ and against any claim by any third party arising out of or related to your use\
  \ or distribution of the Llama Materials.\n6. Term and Termination. The term of\
  \ this Agreement will commence upon your acceptance of this Agreement or access\
  \ to the Llama Materials and will continue in full force and effect until terminated\
  \ in accordance with the terms and conditions herein. Meta may terminate this Agreement\
  \ if you are in breach of any term or condition of this Agreement. Upon termination\
  \ of this Agreement, you shall delete and cease use of the Llama Materials. Sections\
  \ 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law\
  \ and Jurisdiction. This Agreement will be governed and construed under the laws\
  \ of the State of California without regard to choice of law principles, and the\
  \ UN Convention on Contracts for the International Sale of Goods does not apply\
  \ to this Agreement. The courts of California shall have exclusive jurisdiction\
  \ of any dispute arising out of this Agreement.\n### Llama 3.1 Acceptable Use Policy\n\
  Meta is committed to promoting safe and fair use of its tools and features, including\
  \ Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy\
  \ (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\n\
  #### Prohibited Uses\nWe want everyone to use Llama 3.1 safely and responsibly.\
  \ You agree you will not use, or allow others to use, Llama 3.1 to:\n 1. Violate\
  \ the law or others’ rights, including to:\n    1. Engage in, promote, generate,\
  \ contribute to, encourage, plan, incite, or further illegal or unlawful activity\
  \ or content, such as:\n        1. Violence or terrorism\n        2. Exploitation\
  \ or harm to children, including the solicitation, creation, acquisition, or dissemination\
  \ of child exploitative content or failure to report Child Sexual Abuse Material\n\
  \        3. Human trafficking, exploitation, and sexual violence\n        4. The\
  \ illegal distribution of information or materials to minors, including obscene\
  \ materials, or failure to employ legally required age-gating in connection with\
  \ such information or materials.\n        5. Sexual solicitation\n        6. Any\
  \ other criminal activity\n    3. Engage in, promote, incite, or facilitate the\
  \ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\
  \    4. Engage in, promote, incite, or facilitate discrimination or other unlawful\
  \ or harmful conduct in the provision of employment, employment benefits, credit,\
  \ housing, other economic benefits, or other essential goods and services\n    5.\
  \ Engage in the unauthorized or unlicensed practice of any profession including,\
  \ but not limited to, financial, legal, medical/health, or related professional\
  \ practices\n    6. Collect, process, disclose, generate, or infer health, demographic,\
  \ or other sensitive personal or private information about individuals without rights\
  \ and consents required by applicable laws\n    7. Engage in or facilitate any action\
  \ or generate any content that infringes, misappropriates, or otherwise violates\
  \ any third-party rights, including the outputs or results of any products or services\
  \ using the Llama Materials\n    8. Create, generate, or facilitate the creation\
  \ of malicious code, malware, computer viruses or do anything else that could disable,\
  \ overburden, interfere with or impair the proper working, integrity, operation\
  \ or appearance of a website or computer system\n2. Engage in, promote, incite,\
  \ facilitate, or assist in the planning or development of activities that present\
  \ a risk of death or bodily harm to individuals, including use of Llama 3.1 related\
  \ to the following:\n    1. Military, warfare, nuclear industries or applications,\
  \ espionage, use for materials or activities that are subject to the International\
  \ Traffic Arms Regulations (ITAR) maintained by the United States Department of\
  \ State\n    2. Guns and illegal weapons (including weapon development)\n    3.\
  \ Illegal drugs and regulated/controlled substances\n    4. Operation of critical\
  \ infrastructure, transportation technologies, or heavy machinery\n    5. Self-harm\
  \ or harm to others, including suicide, cutting, and eating disorders\n    6. Any\
  \ content intended to incite or promote violence, abuse, or any infliction of bodily\
  \ harm to an individual\n3. Intentionally deceive or mislead others, including use\
  \ of Llama 3.1 related to the following:\n    1. Generating, promoting, or furthering\
  \ fraud or the creation or promotion of disinformation\n    2. Generating, promoting,\
  \ or furthering defamatory content, including the creation of defamatory statements,\
  \ images, or other content\n    3. Generating, promoting, or further distributing\
  \ spam\n    4. Impersonating another individual without consent, authorization,\
  \ or legal right\n    5. Representing that the use of Llama 3.1 or outputs are human-generated\n\
  \    6. Generating or facilitating false online engagement, including fake reviews\
  \ and other means of fake online engagement\n4. Fail to appropriately disclose to\
  \ end users any known dangers of your AI system\nPlease report any violation of\
  \ this Policy, software “bug,” or other problems that could lead to a violation\
  \ of this Policy through one of the following means:\n    * Reporting issues with\
  \ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\
  \    * Reporting risky content generated by the model:\n    developers.facebook.com/llama_output_feedback\n\
  \    * Reporting bugs and security concerns: facebook.com/whitehat/info\n    * Reporting\
  \ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com"
extra_gated_fields:
  First Name: text
  Last Name: text
  Date of birth: date_picker
  Country: country
  Affiliation: text
  Job title:
    type: select
    options:
    - Student
    - Research Graduate
    - AI researcher
    - AI developer/engineer
    - Reporter
    - Other
  geo: ip_location
  ? By clicking Submit below I accept the terms of the license and acknowledge that
    the information I provide will be collected stored processed and shared in accordance
    with the Meta Privacy Policy
  : checkbox
extra_gated_description: The information you provide will be collected, stored, processed
  and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
---

## Model Information

The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

**Model developer**: Meta

**Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. 


<table>
  <tr>
   <td>
   </td>
   <td><strong>Training Data</strong>
   </td>
   <td><strong>Params</strong>
   </td>
   <td><strong>Input modalities</strong>
   </td>
   <td><strong>Output modalities</strong>
   </td>
   <td><strong>Context length</strong>
   </td>
   <td><strong>GQA</strong>
   </td>
   <td><strong>Token count</strong>
   </td>
   <td><strong>Knowledge cutoff</strong>
   </td>
  </tr>
  <tr>
   <td rowspan="3" >Llama 3.1 (text only)
   </td>
   <td rowspan="3" >A new mix of publicly available online data.
   </td>
   <td>8B
   </td>
   <td>Multilingual Text
   </td>
   <td>Multilingual Text and code
   </td>
   <td>128k
   </td>
   <td>Yes
   </td>
   <td rowspan="3" >15T+
   </td>
   <td rowspan="3" >December 2023
   </td>
  </tr>
  <tr>
   <td>70B
   </td>
   <td>Multilingual Text
   </td>
   <td>Multilingual Text and code
   </td>
   <td>128k
   </td>
   <td>Yes
   </td>
  </tr>
  <tr>
   <td>405B
   </td>
   <td>Multilingual Text
   </td>
   <td>Multilingual Text and code
   </td>
   <td>128k
   </td>
   <td>Yes
   </td>
  </tr>
</table>


**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.

**Model Release Date:** July 23, 2024.

**Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.

**License:** A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)

Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes). 


## Intended Use

**Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases. 

**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.

**<span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.


## Hardware and Software

**Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.

**Training utilized a cumulative of** 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. 


**Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.


<table>
  <tr>
   <td>
   </td>
   <td><strong>Training Time (GPU hours)</strong>
   </td>
   <td><strong>Training Power Consumption (W)</strong>
   </td>
   <td><strong>Training Location-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
   </td>
   <td><strong>Training Market-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
   </td>
  </tr>
  <tr>
   <td>Llama 3.1 8B
   </td>
   <td>1.46M
   </td>
   <td>700
   </td>
   <td>420
   </td>
   <td>0
   </td>
  </tr>
  <tr>
   <td>Llama 3.1 70B
   </td>
   <td>7.0M
   </td>
   <td>700
   </td>
   <td>2,040
   </td>
   <td>0
   </td>
  </tr>
  <tr>
   <td>Llama 3.1 405B
   </td>
   <td>30.84M
   </td>
   <td>700
   </td>
   <td>8,930
   </td>
   <td>0
   </td>
  </tr>
  <tr>
   <td>Total
   </td>
   <td>39.3M
   <td>
<ul>

</ul>
   </td>
   <td>11,390
   </td>
   <td>0
   </td>
  </tr>
</table>



The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149).  Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions  will not be incurred by others.


## Training Data

**Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples. 

**Data Freshness:** The pretraining data has a cutoff of December 2023.


## Benchmark scores

In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. 

### Base pretrained models


<table>
  <tr>
   <td><strong>Category</strong>
   </td>
   <td><strong>Benchmark</strong>
   </td>
   <td><strong># Shots</strong>
   </td>
   <td><strong>Metric</strong>
   </td>
   <td><strong>Llama 3 8B</strong>
   </td>
   <td><strong>Llama 3.1 8B</strong>
   </td>
   <td><strong>Llama 3 70B</strong>
   </td>
   <td><strong>Llama 3.1 70B</strong>
   </td>
   <td><strong>Llama 3.1 405B</strong>
   </td>
  </tr>
  <tr>
   <td rowspan="7" >General
   </td>
   <td>MMLU
   </td>
   <td>5
   </td>
   <td>macro_avg/acc_char
   </td>
   <td>66.7
   </td>
   <td>66.7
   </td>
   <td>79.5
   </td>
   <td>79.3
   </td>
   <td>85.2
   </td>
  </tr>
  <tr>
   <td>MMLU-Pro (CoT)
   </td>
   <td>5
   </td>
   <td>macro_avg/acc_char
   </td>
   <td>36.2
   </td>
   <td>37.1
   </td>
   <td>55.0
   </td>
   <td>53.8
   </td>
   <td>61.6
   </td>
  </tr>
  <tr>
   <td>AGIEval English
   </td>
   <td>3-5
   </td>
   <td>average/acc_char
   </td>
   <td>47.1
   </td>
   <td>47.8
   </td>
   <td>63.0
   </td>
   <td>64.6
   </td>
   <td>71.6
   </td>
  </tr>
  <tr>
   <td>CommonSenseQA
   </td>
   <td>7
   </td>
   <td>acc_char
   </td>
   <td>72.6
   </td>
   <td>75.0
   </td>
   <td>83.8
   </td>
   <td>84.1
   </td>
   <td>85.8
   </td>
  </tr>
  <tr>
   <td>Winogrande
   </td>
   <td>5
   </td>
   <td>acc_char
   </td>
   <td>-
   </td>
   <td>60.5
   </td>
   <td>-
   </td>
   <td>83.3
   </td>
   <td>86.7
   </td>
  </tr>
  <tr>
   <td>BIG-Bench Hard (CoT)
   </td>
   <td>3
   </td>
   <td>average/em
   </td>
   <td>61.1
   </td>
   <td>64.2
   </td>
   <td>81.3
   </td>
   <td>81.6
   </td>
   <td>85.9
   </td>
  </tr>
  <tr>
   <td>ARC-Challenge
   </td>
   <td>25
   </td>
   <td>acc_char
   </td>
   <td>79.4
   </td>
   <td>79.7
   </td>
   <td>93.1
   </td>
   <td>92.9
   </td>
   <td>96.1
   </td>
  </tr>
  <tr>
   <td>Knowledge reasoning
   </td>
   <td>TriviaQA-Wiki
   </td>
   <td>5
   </td>
   <td>em
   </td>
   <td>78.5
   </td>
   <td>77.6
   </td>
   <td>89.7
   </td>
   <td>89.8
   </td>
   <td>91.8
   </td>
  </tr>
  <tr>
   <td rowspan="4" >Reading comprehension
   </td>
   <td>SQuAD
   </td>
   <td>1
   </td>
   <td>em
   </td>
   <td>76.4
   </td>
   <td>77.0
   </td>
   <td>85.6
   </td>
   <td>81.8
   </td>
   <td>89.3
   </td>
  </tr>
  <tr>
   <td>QuAC (F1)
   </td>
   <td>1
   </td>
   <td>f1
   </td>
   <td>44.4
   </td>
   <td>44.9
   </td>
   <td>51.1
   </td>
   <td>51.1
   </td>
   <td>53.6
   </td>
  </tr>
  <tr>
   <td>BoolQ
   </td>
   <td>0
   </td>
   <td>acc_char
   </td>
   <td>75.7
   </td>
   <td>75.0
   </td>
   <td>79.0
   </td>
   <td>79.4
   </td>
   <td>80.0
   </td>
  </tr>
  <tr>
   <td>DROP (F1)
   </td>
   <td>3
   </td>
   <td>f1
   </td>
   <td>58.4
   </td>
   <td>59.5
   </td>
   <td>79.7
   </td>
   <td>79.6
   </td>
   <td>84.8
   </td>
  </tr>
</table>



### Instruction tuned models


<table>
  <tr>
   <td><strong>Category</strong>
   </td>
   <td><strong>Benchmark</strong>
   </td>
   <td><strong># Shots</strong>
   </td>
   <td><strong>Metric</strong>
   </td>
   <td><strong>Llama 3 8B Instruct</strong>
   </td>
   <td><strong>Llama 3.1 8B Instruct</strong>
   </td>
   <td><strong>Llama 3 70B Instruct</strong>
   </td>
   <td><strong>Llama 3.1 70B Instruct</strong>
   </td>
   <td><strong>Llama 3.1 405B Instruct</strong>
   </td>
  </tr>
  <tr>
   <td rowspan="4" >General
   </td>
   <td>MMLU
   </td>
   <td>5
   </td>
   <td>macro_avg/acc
   </td>
   <td>68.5
   </td>
   <td>69.4
   </td>
   <td>82.0
   </td>
   <td>83.6
   </td>
   <td>87.3
   </td>
  </tr>
  <tr>
   <td>MMLU (CoT)
   </td>
   <td>0
   </td>
   <td>macro_avg/acc
   </td>
   <td>65.3
   </td>
   <td>73.0
   </td>
   <td>80.9
   </td>
   <td>86.0
   </td>
   <td>88.6
   </td>
  </tr>
  <tr>
   <td>MMLU-Pro (CoT)
   </td>
   <td>5
   </td>
   <td>micro_avg/acc_char
   </td>
   <td>45.5
   </td>
   <td>48.3
   </td>
   <td>63.4
   </td>
   <td>66.4
   </td>
   <td>73.3
   </td>
  </tr>
  <tr>
   <td>IFEval
   </td>
   <td>
   </td>
   <td>
   </td>
   <td>76.8
   </td>
   <td>80.4
   </td>
   <td>82.9
   </td>
   <td>87.5
   </td>
   <td>88.6
   </td>
  </tr>
  <tr>
   <td rowspan="2" >Reasoning
   </td>
   <td>ARC-C
   </td>
   <td>0
   </td>
   <td>acc
   </td>
   <td>82.4
   </td>
   <td>83.4
   </td>
   <td>94.4
   </td>
   <td>94.8
   </td>
   <td>96.9
   </td>
  </tr>
  <tr>
   <td>GPQA
   </td>
   <td>0
   </td>
   <td>em
   </td>
   <td>34.6
   </td>
   <td>30.4
   </td>
   <td>39.5
   </td>
   <td>46.7
   </td>
   <td>50.7
   </td>
  </tr>
  <tr>
   <td rowspan="4" >Code
   </td>
   <td>HumanEval
   </td>
   <td>0
   </td>
   <td>pass@1
   </td>
   <td>60.4
   </td>
   <td>72.6
   </td>
   <td>81.7
   </td>
   <td>80.5
   </td>
   <td>89.0
   </td>
  </tr>
  <tr>
   <td>MBPP ++ base version
   </td>
   <td>0
   </td>
   <td>pass@1
   </td>
   <td>70.6
   </td>
   <td>72.8
   </td>
   <td>82.5
   </td>
   <td>86.0
   </td>
   <td>88.6
   </td>
  </tr>
  <tr>
   <td>Multipl-E HumanEval
   </td>
   <td>0
   </td>
   <td>pass@1
   </td>
   <td>-
   </td>
   <td>50.8
   </td>
   <td>-
   </td>
   <td>65.5
   </td>
   <td>75.2
   </td>
  </tr>
  <tr>
   <td>Multipl-E MBPP
   </td>
   <td>0
   </td>
   <td>pass@1
   </td>
   <td>-
   </td>
   <td>52.4
   </td>
   <td>-
   </td>
   <td>62.0
   </td>
   <td>65.7
   </td>
  </tr>
  <tr>
   <td rowspan="2" >Math
   </td>
   <td>GSM-8K (CoT)
   </td>
   <td>8
   </td>
   <td>em_maj1@1
   </td>
   <td>80.6
   </td>
   <td>84.5
   </td>
   <td>93.0
   </td>
   <td>95.1
   </td>
   <td>96.8
   </td>
  </tr>
  <tr>
   <td>MATH (CoT)
   </td>
   <td>0
   </td>
   <td>final_em
   </td>
   <td>29.1
   </td>
   <td>51.9
   </td>
   <td>51.0
   </td>
   <td>68.0
   </td>
   <td>73.8
   </td>
  </tr>
  <tr>
   <td rowspan="4" >Tool Use
   </td>
   <td>API-Bank
   </td>
   <td>0
   </td>
   <td>acc
   </td>
   <td>48.3
   </td>
   <td>82.6
   </td>
   <td>85.1
   </td>
   <td>90.0
   </td>
   <td>92.0
   </td>
  </tr>
  <tr>
   <td>BFCL
   </td>
   <td>0
   </td>
   <td>acc
   </td>
   <td>60.3
   </td>
   <td>76.1
   </td>
   <td>83.0
   </td>
   <td>84.8
   </td>
   <td>88.5
   </td>
  </tr>
  <tr>
   <td>Gorilla Benchmark API Bench
   </td>
   <td>0
   </td>
   <td>acc
   </td>
   <td>1.7
   </td>
   <td>8.2
   </td>
   <td>14.7
   </td>
   <td>29.7
   </td>
   <td>35.3
   </td>
  </tr>
  <tr>
   <td>Nexus (0-shot)
   </td>
   <td>0
   </td>
   <td>macro_avg/acc
   </td>
   <td>18.1
   </td>
   <td>38.5
   </td>
   <td>47.8
   </td>
   <td>56.7
   </td>
   <td>58.7
   </td>
  </tr>
  <tr>
   <td>Multilingual
   </td>
   <td>Multilingual MGSM (CoT)
   </td>
   <td>0
   </td>
   <td>em
   </td>
   <td>-
   </td>
   <td>68.9
   </td>
   <td>-
   </td>
   <td>86.9
   </td>
   <td>91.6
   </td>
  </tr>
</table>

#### Multilingual benchmarks

<table>
  <tr>
   <td><strong>Category</strong>
   </td>
   <td><strong>Benchmark</strong>
   </td>
   <td><strong>Language</strong>
   </td>
   <td><strong>Llama 3.1 8B</strong>
   </td>
   <td><strong>Llama 3.1 70B</strong>
   </td>
   <td><strong>Llama 3.1 405B</strong>
   </td>
  </tr>
  <tr>
   <td rowspan="9" ><strong>General</strong>
   </td>
   <td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong>
   </td>
   <td>Portuguese
   </td>
   <td>62.12
   </td>
   <td>80.13
   </td>
   <td>84.95
   </td>
  </tr>
  <tr>
   <td>Spanish
   </td>
   <td>62.45
   </td>
   <td>80.05
   </td>
   <td>85.08
   </td>
  </tr>
  <tr>
   <td>Italian
   </td>
   <td>61.63
   </td>
   <td>80.4
   </td>
   <td>85.04
   </td>
  </tr>
  <tr>
   <td>German
   </td>
   <td>60.59
   </td>
   <td>79.27
   </td>
   <td>84.36
   </td>
  </tr>
  <tr>
   <td>French
   </td>
   <td>62.34
   </td>
   <td>79.82
   </td>
   <td>84.66
   </td>
  </tr>
  <tr>
   <td>Hindi
   </td>
   <td>50.88
   </td>
   <td>74.52
   </td>
   <td>80.31
   </td>
  </tr>
  <tr>
   <td>Thai
   </td>
   <td>50.32
   </td>
   <td>72.95
   </td>
   <td>78.21
   </td>
  </tr>
</table>

### Tool use support

LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).

Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers. 
Here is a quick example showing a single simple tool:

```python
# First, define a tool
def get_current_temperature(location: str) -> float:
    """
    Get the current temperature at a location.
    
    Args:
        location: The location to get the temperature for, in the format "City, Country"
    Returns:
        The current temperature at the specified location in the specified units, as a float.
    """
    return 22.  # A real function should probably actually get the temperature!

# Next, create a chat and apply the chat template
messages = [
  {"role": "system", "content": "You are a bot that responds to weather queries."},
  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]

inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
```

You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:

```python
tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
```

and then call the tool and append the result, with the `tool` role, like so:

```python
messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
```

After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information,
see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).


## Responsibility & Safety

As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:



* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. 
* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
* Provide protections for the community to help prevent the misuse of our models.


### Responsible deployment 

Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more. 


#### Llama 3.1 instruct 

Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper. 

**Fine-tuning data**

We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. 

**Refusals and Tone**

Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow  tone guidelines. 


#### Llama 3.1 systems

**Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. 

As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. 


#### New capabilities 

Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.

**Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards. 

**Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide. 


### Evaluations

We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. 

Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.

**Red teaming**

For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. 

We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity.  The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.


### Critical and other risks 

We specifically focused our efforts on mitigating the following critical risk areas:

**1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**

To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. 


**2. Child Safety**

Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development.  For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 

**3. Cyber attack enablement**

Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.

Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.

Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more.


### Community 

Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). 

We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). 

Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.


## Ethical Considerations and Limitations

The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. 

But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.