File size: 52,900 Bytes
2863d55
301567c
 
 
5978aa8
041f1db
 
 
 
98acc8b
041f1db
5978aa8
 
 
2863d55
98acc8b
5f5a111
 
2189f60
795e122
 
 
2189f60
 
 
 
98acc8b
 
4b1c218
 
98acc8b
 
 
 
4b1c218
 
98acc8b
 
 
 
4b1c218
 
98acc8b
 
 
397954e
4b1c218
 
397954e
 
 
2f1256f
 
4b1c218
 
397954e
 
4b1c218
 
 
98acc8b
4b1c218
 
 
98acc8b
2f1256f
4b1c218
5f5a111
cb4157b
4b1c218
 
 
 
 
 
b881162
 
 
 
4b1c218
37f7db9
 
6978906
 
83025b5
9bd3332
83025b5
ba5924d
83025b5
37f7db9
9b63e4a
 
4b1c218
 
 
 
 
 
 
37f7db9
4b1c218
 
 
 
 
83025b5
7ed3ea6
 
4b1c218
 
5f5a111
b881162
 
4b1c218
5f5a111
4b1c218
5f5a111
37f7db9
5f5a111
4b1c218
 
 
 
1a5af72
9b63e4a
 
83025b5
7d66b1c
83025b5
9b63e4a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
83025b5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9b63e4a
83025b5
 
 
4b1c218
83025b5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4b1c218
 
 
b881162
 
 
 
76b645e
37f7db9
b881162
 
 
 
 
 
 
9b63e4a
 
 
 
b881162
37f7db9
b881162
 
 
 
 
 
 
9b63e4a
 
b881162
 
 
 
 
37f7db9
b881162
37f7db9
b881162
37f7db9
b881162
37f7db9
 
 
 
 
 
 
b881162
7d66b1c
9b63e4a
 
 
 
 
 
 
 
 
 
b881162
 
 
4b1c218
37f7db9
 
 
83025b5
6978906
83025b5
 
9bd3332
ba5924d
6978906
37f7db9
9bd3332
 
37f7db9
 
 
 
 
 
 
 
 
9bd3332
37f7db9
 
 
 
 
 
ba5924d
9bd3332
83025b5
37f7db9
9b63e4a
37f7db9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9b63e4a
37f7db9
1a5af72
37f7db9
137bcfc
37f7db9
76b645e
37f7db9
9b63e4a
1a5af72
ba5924d
37f7db9
 
 
 
 
 
 
 
 
ba5924d
37f7db9
 
 
 
 
 
 
ba5924d
37f7db9
 
 
ba5924d
37f7db9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ba5924d
37f7db9
1a5af72
37f7db9
 
 
 
 
 
 
 
 
 
 
7d66b1c
37f7db9
 
 
 
 
 
 
 
 
9b63e4a
 
7d66b1c
9bd3332
9b63e4a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
83025b5
1a5af72
83025b5
 
 
 
 
 
 
 
 
 
 
1a5af72
9b63e4a
 
7d66b1c
9b63e4a
1a5af72
9b63e4a
 
 
 
1a5af72
9b63e4a
 
 
 
 
 
 
 
 
1a5af72
 
9b63e4a
1a5af72
 
9b63e4a
1a5af72
9b63e4a
1a5af72
9b63e4a
 
 
1a5af72
9b63e4a
 
 
 
 
1a5af72
9b63e4a
1a5af72
9b63e4a
1a5af72
9b63e4a
 
 
 
 
1a5af72
9b63e4a
1a5af72
9b63e4a
 
 
 
 
 
 
ba5924d
9b63e4a
 
ba5924d
9b63e4a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9bd3332
9b63e4a
 
9bd3332
9b63e4a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9bd3332
9b63e4a
 
 
 
9bd3332
9b63e4a
 
 
ba5924d
 
 
 
 
 
 
 
 
 
 
 
1a5af72
ba5924d
 
 
 
9bd3332
ba5924d
83025b5
37f7db9
 
 
 
 
 
 
 
 
 
7d66b1c
9b63e4a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
37f7db9
9b63e4a
 
 
37f7db9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7d66b1c
9b63e4a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
37f7db9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7d66b1c
37f7db9
9b63e4a
37f7db9
9b63e4a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
37f7db9
4b1c218
9b63e4a
76b645e
 
9b63e4a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
76b645e
9b63e4a
7d66b1c
9b63e4a
76b645e
9b63e4a
 
 
 
 
 
 
 
 
 
76b645e
9b63e4a
 
 
 
 
 
 
e96a233
9b63e4a
 
 
 
 
4b1c218
37f7db9
 
 
 
 
 
 
 
 
 
9b63e4a
4b1c218
 
d38eb40
4b1c218
d38eb40
4b1c218
 
 
 
 
7ed3ea6
d38eb40
4b1c218
5f5a111
4b1c218
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
37f7db9
 
 
4b1c218
 
 
 
 
 
 
 
 
37f7db9
4b1c218
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7ed3ea6
 
 
0f8db85
4b1c218
7ed3ea6
4b1c218
 
 
 
9b63e4a
4b1c218
9b63e4a
76b645e
 
98acc8b
9bd3332
c2835f6
9b63e4a
7d66b1c
5aa73f3
9b63e4a
 
 
 
5aa73f3
 
9b63e4a
 
 
76b645e
 
5aa73f3
9bd3332
5aa73f3
9b63e4a
7d66b1c
 
 
 
 
 
 
 
 
9b63e4a
5aa73f3
4b1c218
5aa73f3
4b1c218
c2835f6
4b1c218
 
e8e2d3c
4b1c218
 
 
 
f77c5ff
7ed3ea6
 
 
 
f102133
1353969
7ed3ea6
 
4b1c218
5f5a111
98acc8b
 
 
 
 
 
 
 
5f5a111
4b1c218
 
 
5f5a111
98acc8b
9b63e4a
4b1c218
 
ba5924d
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
---
license: other
license_name: yi-license
license_link: LICENSE
widget:
  - example_title: "Yi-34B-Chat"
    text: "hi"
    output:
      text: " Hello! How can I assist you today?"
  - example_title: "Yi-34B"
    text: "There's a place where time stands still. A place of breath taking wonder, but also"
    output:
      text: " an eerie sense that something is just not right…\nBetween the two worlds lies The Forgotten Kingdom - home to creatures long since thought extinct and ancient magic so strong it defies belief! Only here can you find what has been lost for centuries: An Elixir Of Life which will restore youth and vitality if only those who seek its power are brave enough to face up against all manner of dangers lurking in this mysterious land! But beware; some say there may even exist powerful entities beyond our comprehension whose intentions towards humanity remain unclear at best ---- they might want nothing more than destruction itself rather then anything else from their quest after immortality (and maybe someone should tell them about modern medicine)? In any event though – one thing remains true regardless : whether or not success comes easy depends entirely upon how much effort we put into conquering whatever challenges lie ahead along with having faith deep down inside ourselves too ;) So let’s get started now shall We?"
pipeline_tag: text-generation
---

<div align="center">

<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_dark.svg" width="200px">
  <source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="200px"> 
  <img alt="specify theme context for images" src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg">
</picture>

</br>
</br>

<div style="display: inline-block;">
<a href="https://github.com/01-ai/Yi/actions/workflows/build_docker_image.yml">
  <img src="https://github.com/01-ai/Yi/actions/workflows/build_docker_image.yml/badge.svg">
</a>
</div>

<div style="display: inline-block;">
<a href="https://github.com/01-ai/Yi/blob/main/LICENSE">
  <img src="https://img.shields.io/badge/Code_License-Apache_2.0-lightblue">
</a>
</div>

<div style="display: inline-block;">
<a href="https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt">
  <img src="https://img.shields.io/badge/Model_License-Yi_License-lightblue">
</a>
</div>

<div style="display: inline-block;">
<a href="mailto:[email protected]">
  <img src="https://img.shields.io/badge/✉️[email protected]">
</a>
</div>

</div>

<div align="center">
  <h3 align="center">Building the Next Generation of Open-Source and Bilingual LLMs</h3>
</div>

<p align="center">
🤗 <a href="https://huggingface.co/01-ai" target="_blank">Hugging Face</a> • 🤖 <a href="https://www.modelscope.cn/organization/01ai/" target="_blank">ModelScope</a> • ✡️ <a href="https://wisemodel.cn/organization/01.AI" target="_blank">WiseModel</a>
</p> 

<p align="center">
    👋 Join us 💬 <a href="https://github.com/01-ai/Yi/issues/43#issuecomment-1827285245" target="_blank"> WeChat (Chinese) </a>!
</p> 


<!-- DO NOT REMOVE ME -->

<hr>

<details open>
<summary></b>📕 Table of Contents</b></summary>

- [🟢 What is Yi?](#-what-is-yi)
  - [📌 Introduction](#-introduction)
  - [🎯 Models](#-models)
    - [Chat models](#chat-models)
    - [Base models](#base-models)
    - [Other info](#other-info)
  - [🎉 News](#-news)
- [🟢 How to use Yi?](#-how-to-use-yi)
  - [Quick start](#quick-start)
    - [Choose your path](#choose-your-path)
    - [pip](#quick-start---pip)
    - [docker](#quick-start---docker)
    - [llama.cpp](#quick-start---llamacpp)
    - [conda-lock](#quick-start---conda-lock)
    - [Web demo](#web-demo)
  - [Fine-tuning](#fine-tuning)
  - [Quantization](#quantization)
  - [Deployment](#deployment)
  - [Learning hub](#learning-hub)
- [🟢 Why Yi?](#-why-yi)
  - [🌎 Ecosystem](#-ecosystem)
    - [💦 Upstream](#-upstream)
    - [🌊 Downstream](#-downstream)
      - [🔗 Serving](#-serving)
      - [⚙️ Quantitation](#️-quantitation)
      - [🛠️ Fine-tuning](#️-fine-tuning)
      - [API](#api)
  - [📌 Benchmarks](#-benchmarks)
    - [📊 Base model performance](#-base-model-performance)
    - [📊 Chat model performance](#-chat-model-performance)
- [🟢 Who can use Yi?](#-who-can-use-yi)
- [🟢 Misc.](#-misc)
  - [Acknowledgements](#acknowledgments)
  - [📡 Disclaimer](#-disclaimer)
  - [🪪 License](#-license)

</details>

<hr>

# 🟢 What is Yi?

## 📌 Introduction 

- 🤖 The Yi series models are the next generation of open-source large language models trained from scratch by [01.AI](https://01.ai/).

- 🙌 Targeted as a bilingual language model and trained on 3T multilingual corpus, the Yi series models become one of the strongest LLM worldwide, showing promise in language understanding, commonsense reasoning, reading comprehension, and more. For example,

  - For English language capability, the Yi series models ranked 2nd (just behind GPT-4), outperforming other LLMs (such as LLaMA2-chat-70B, Claude 2, and ChatGPT) on the [AlpacaEval Leaderboard](https://tatsu-lab.github.io/alpaca_eval/) in Dec 2023.
  
  - For Chinese language capability, the Yi series models landed in 2nd place (following GPT-4), surpassing other LLMs (such as Baidu ERNIE, Qwen, and Baichuan) on the [SuperCLUE](https://www.superclueai.com/) in Oct 2023.
  
  - 🙏 (Credits to LLaMA) Thanks to the Transformer and LLaMA open-source communities, as they reducing the efforts required to build from scratch and enabling the utilization of the same tools within the AI ecosystem.  

  <details style="display: inline;"><summary> If you're interested in Yi's adoption of LLaMA architecture and license usage policy, see  <span style="color:  green;">Yi's relation with LLaMA.</span> ⬇️</summary> <ul> <br>
  
> 💡 TL;DR
> 
> The Yi series models adopt the same model architecture as LLaMA but are **NOT** derivatives of LLaMA.

- Both Yi and LLaMA are all based on the Transformer structure, which has been the standard architecture for large language models since 2018.

- Grounded in the Transformer architecture, LLaMA has become a new cornerstone for the majority of state-of-the-art open-source models due to its excellent stability, reliable convergence, and robust compatibility. This positions LLaMA as the recognized foundational framework for models including Yi.

- Thanks to the Transformer and LLaMA architectures, other models can leverage their power, reducing the effort required to build from scratch and enabling the utilization of the same tools within their ecosystems.

- However, the Yi series models are NOT derivatives of LLaMA, as they do not use LLaMA's weights.

  - As LLaMA's structure is employed by the majority of open-source models, the key factors of determining model performance are training datasets, training pipelines, and training infrastructure.

  - Developing in a unique and proprietary way, Yi has independently created its own high-quality training datasets, efficient training pipelines, and robust training infrastructure entirely from the ground up. This effort has led to excellent performance with Yi series models ranking just behind GPT4 and surpassing LLaMA on the [Alpaca Leaderboard in Dec 2023](https://tatsu-lab.github.io/alpaca_eval/). 
</ul>
</details>

<div align="right"> [ <a href="#building-the-next-generation-of-open-source-and-bilingual-llms">Back to top ⬆️ </a> ] </div>

## 🎉 News 

<details open>
  <summary>🎯 <b>2024/01/23</b>: The Yi-VL models, <code><a href="https://huggingface.co/01-ai/Yi-VL-34B">Yi-VL-34B</a></code> and <code><a href="https://huggingface.co/01-ai/Yi-VL-6B">Yi-VL-6B</a></code>, are open-sourced and available to the public.</summary>
  <br>
  <code><a href="https://huggingface.co/01-ai/Yi-VL-34B">Yi-VL-34B</a></code> has ranked <strong>first</strong> among all existing open-source models in the latest benchmarks, including <a href="https://arxiv.org/abs/2311.16502">MMMU</a> and <a href="https://arxiv.org/abs/2401.11944">CMMMU</a> (based on data available up to January 2024).</li>
</details>


<details>
<summary>🎯 <b>2023/11/23</b>: <a href="#chat-models">Chat models</a> are open-sourced and available to the public.</summary>
<br>This release contains two chat models based on previously released base models, two 8-bit models quantized by GPTQ, and two 4-bit models quantized by AWQ.

- `Yi-34B-Chat`
- `Yi-34B-Chat-4bits`
- `Yi-34B-Chat-8bits`
- `Yi-6B-Chat`
- `Yi-6B-Chat-4bits`
- `Yi-6B-Chat-8bits`

You can try some of them interactively at:

- [Hugging Face](https://huggingface.co/spaces/01-ai/Yi-34B-Chat)
- [Replicate](https://replicate.com/01-ai)
</details>

<details>
  <summary>🔔 <b>2023/11/23</b>: The Yi Series Models Community License Agreement is updated to <a href="https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt">v2.1</a>.</summary>
</details>

<details> 
<summary>🔥 <b>2023/11/08</b>: Invited test of Yi-34B chat model.</summary>
<br>Application form:

- [English](https://cn.mikecrm.com/l91ODJf)
- [Chinese](https://cn.mikecrm.com/gnEZjiQ)
</details>

<details>
<summary>🎯 <b>2023/11/05</b>: <a href="#base-models">The base models, </a><code>Yi-6B-200K</code> and <code>Yi-34B-200K</code>, are open-sourced and available to the public.</summary>
<br>This release contains two base models with the same parameter sizes as the previous
release, except that the context window is extended to 200K.
</details>

<details>
<summary>🎯 <b>2023/11/02</b>: <a href="#base-models">The base models, </a><code>Yi-6B</code> and <code>Yi-34B</code>, are open-sourced and available to the public.</summary>
<br>The first public release contains two bilingual (English/Chinese) base models
with the parameter sizes of 6B and 34B.  Both of them are trained with 4K
sequence length and can be extended to 32K during inference time.

</details>

<div align="right"> [ <a href="#building-the-next-generation-of-open-source-and-bilingual-llms">Back to top ⬆️ </a> ] </div>

## 🎯 Models

Yi models come in multiple sizes and cater to different use cases. You can also fine-tune Yi models to meet your specific requirements. 

If you want to deploy Yi models, make sure you meet the [software and hardware requirements](#deployment).

### Chat models

| Model | Download  
|---|---
Yi-34B-Chat	| • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B-Chat)  • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B-Chat/summary)
Yi-34B-Chat-4bits	| • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B-Chat-4bits)  • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B-Chat-4bits/summary)
Yi-34B-Chat-8bits | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B-Chat-8bits) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B-Chat-8bits/summary)
Yi-6B-Chat| • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B-Chat/summary)
Yi-6B-Chat-4bits |	• [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B-Chat-4bits)  • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B-Chat-4bits/summary)
Yi-6B-Chat-8bits	|  • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B-Chat-8bits) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B-Chat-8bits/summary)


<sub><sup> - 4-bit series models are quantized by AWQ. <br> - 8-bit series models are quantized by GPTQ <br> - All quantized models have a low barrier to use since they can be deployed on consumer-grade GPUs (e.g., 3090, 4090). </sup></sub>

### Base models

| Model | Download | 
|---|---|
Yi-34B| • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B)  • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B/summary)
Yi-34B-200K|• [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B-200K)  • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B-200K/summary)
Yi-6B| • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B)  • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B/summary)
Yi-6B-200K	| • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B-200K) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B-200K/summary)

<sub><sup> - 200k is roughly equivalent to 400,000 Chinese characters.  </sup></sub>

### Other info

- For chat and base models:

  - 6B series models are suitable for personal and academic use.

  - 34B series models suitable for personal, academic, and commercial (particularly for small and medium-sized enterprises) purposes. It's a cost-effective solution that's affordable and equipped with emergent ability.

  - The **default context window** is **4k tokens**.
    
  - The pretrained tokens are 3T.
    
  - The training data are up to June 2023.	

- For chat models:
  
  <details style="display: inline;"><summary>For chat model limitations, see the explanations below. ⬇️</summary>
   <ul>
    <br>The released chat model has undergone exclusive training using Supervised Fine-Tuning (SFT). Compared to other standard chat models, our model produces more diverse responses, making it suitable for various downstream tasks, such as creative scenarios. Furthermore, this diversity is expected to enhance the likelihood of generating higher quality responses, which will be advantageous for subsequent Reinforcement Learning (RL) training.

    <br>However, this higher diversity might amplify certain existing issues, including:
      <li>Hallucination: This refers to the model generating factually incorrect or nonsensical information. With the model's responses being more varied, there's a higher chance of hallucination that are not based on accurate data or logical reasoning.</li>
      <li>Non-determinism in re-generation: When attempting to regenerate or sample responses, inconsistencies in the outcomes may occur. The increased diversity can lead to varying results even under similar input conditions.</li>
      <li>Cumulative Error: This occurs when errors in the model's responses compound over time. As the model generates more diverse responses, the likelihood of small inaccuracies building up into larger errors increases, especially in complex tasks like extended reasoning, mathematical problem-solving, etc.</li>
      <li>To achieve more coherent and consistent responses, it is advisable to adjust generation configuration parameters such as temperature, top_p, or top_k. These adjustments can help in the balance between creativity and coherence in the model's outputs.</li>
</ul>
</details>

<div align="right"> [ <a href="#building-the-next-generation-of-open-source-and-bilingual-llms">Back to top ⬆️ </a> ] </div>


# 🟢 How to use Yi?

- [Quick start](#quick-start)
  - [Choose your path](#choose-your-path)
  - [pip](#quick-start---pip)
  - [docker](#quick-start---docker)
  - [conda-lock](#quick-start---conda-lock)
  - [llama.cpp](#quick-start---llamacpp)
  - [Web demo](#web-demo)
- [Fine-tuning](#finetuning)
- [Quantization](#quantization)
- [Deployment](#deployment)
- [Learning hub](#learning-hub)

## Quick start

Getting up and running with Yi models is simple with multiple choices available. 

### Choose your path

Select one of the following paths to begin your journey with Yi!

 ![Quick start - Choose your path](https://github.com/01-ai/Yi/blob/main/assets/img/quick_start_path.png?raw=true)

#### 🎯 Deploy Yi locally

If you prefer to deploy Yi models locally, 

  - 🙋‍♀️ and you have **sufficient** resources (for example, NVIDIA A800 80GB), you can choose one of the following methods:
    - [pip](#pip)
    - [Docker](#quick-start---docker)
    - [conda-lock](#quick-start---conda-lock)

  - 🙋‍♀️ and you have **limited** resources (for example, a MacBook Pro), you can use [llama.cpp](#quick-start---llamacpp)

#### 🎯 Not to deploy Yi locally

If you prefer not to deploy Yi models locally, you can explore Yi's capabilities using any of the following options.

##### 🙋‍♀️ Run Yi with APIs

If you want to explore more features of Yi, you can adopt one of these methods:

- Yi APIs (Yi official)
  - [Early access has been granted](https://x.com/01AI_Yi/status/1735728934560600536?s=20) to some applicants. Stay tuned for the next round of access!

- [Yi APIs](https://replicate.com/01-ai/yi-34b-chat/api?tab=nodejs) (Replicate)

##### 🙋‍♀️ Run Yi in playground

If you want to chat with Yi with more customizable options (e.g., system prompt, temperature, repetition penalty, etc.), you can try one of the following options:
  
  - [Yi-34B-Chat-Playground](https://platform.lingyiwanwu.com/prompt/playground) (Yi official)
    - Access is available through a whitelist. Welcome to apply (fill out a form in [English](https://cn.mikecrm.com/l91ODJf) or [Chinese](https://cn.mikecrm.com/gnEZjiQ)).
  
  - [Yi-34B-Chat-Playground](https://replicate.com/01-ai/yi-34b-chat) (Replicate) 

##### 🙋‍♀️ Chat with Yi

 If you want to chat with Yi, you can use one of these online services, which offer a similar user experience:

- [Yi-34B-Chat](https://huggingface.co/spaces/01-ai/Yi-34B-Chat) (Yi official on Hugging Face)
  - No registration is required.

- [Yi-34B-Chat](https://platform.lingyiwanwu.com/) (Yi official beta)
  - Access is available through a whitelist. Welcome to apply (fill out a form in [English](https://cn.mikecrm.com/l91ODJf) or [Chinese](https://cn.mikecrm.com/gnEZjiQ)).

### Quick start - pip

This tutorial guides you through every step of running **Yi-34B-Chat locally on an A800 (80G)** and then performing inference.

#### Step 0: Prerequisites
 
- Make sure Python 3.10 or a later version is installed.

- If you want to run other Yi models, see [software and hardware requirements](#deployment)

#### Step 1: Prepare your environment 

To set up the environment and install the required packages, execute the following command.

```bash
git clone https://github.com/01-ai/Yi.git
cd yi
pip install -r requirements.txt
```

#### Step 2: Download the Yi model

You can download the weights and tokenizer of Yi models from the following sources:

- [Hugging Face](https://huggingface.co/01-ai)
- [ModelScope](https://www.modelscope.cn/organization/01ai/)
- [WiseModel](https://wisemodel.cn/organization/01.AI)

#### Step 3: Perform inference

You can perform inference with Yi chat or base models as below.

##### Perform inference with Yi chat model

1. Create a file named  `quick_start.py` and copy the following content to it.

    ```python
    from transformers import AutoModelForCausalLM, AutoTokenizer

    model_path = '<your-model-path>'

    tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)

    # Since transformers 4.35.0, the GPT-Q/AWQ model can be loaded using AutoModelForCausalLM.
    model = AutoModelForCausalLM.from_pretrained(
        model_path,
        device_map="auto",
        torch_dtype='auto'
    ).eval()

    # Prompt content: "hi"
    messages = [
        {"role": "user", "content": "hi"}
    ]

    input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
    output_ids = model.generate(input_ids.to('cuda'))
    response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

    # Model response: "Hello! How can I assist you today?"
    print(response)
    ```

2. Run `quick_start.py`.

    ```bash
    python quick_start.py
    ```

    Then you can see an output similar to the one below. 🥳

    ```bash
    Hello! How can I assist you today?
    ```

##### Perform inference with Yi base model

The steps are similar to [pip - Perform inference with Yi chat model](#perform-inference-with-yi-chat-model).

You can use the existing file [`text_generation.py`](https://github.com/01-ai/Yi/tree/main/demo).

```bash
python demo/text_generation.py  --model <your-model-path>
```

Then you can see an output similar to the one below. 🥳

<details>

<summary>Output. ⬇️ </summary>

<br>

**Prompt**: Let me tell you an interesting story about cat Tom and mouse Jerry,

**Generation**: Let me tell you an interesting story about cat Tom and mouse Jerry, which happened in my childhood. My father had a big house with two cats living inside it to kill mice. One day when I was playing at home alone, I found one of the tomcats lying on his back near our kitchen door, looking very much like he wanted something from us but couldn’t get up because there were too many people around him! He kept trying for several minutes before finally giving up...

</details>

### Quick start - Docker 
<details>
<summary> Run Yi-34B-chat locally with Docker: a step-by-step guide. ⬇️</summary> 
<br>This tutorial guides you through every step of running <strong>Yi-34B-Chat on an A800 GPU</strong> or <strong>4*4090</strong> locally and then performing inference.
 <h4>Step 0: Prerequisites</h4>
<p>Make sure you've installed <a href="https://docs.docker.com/engine/install/?open_in_browser=true">Docker</a> and <a href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html">nvidia-container-toolkit</a>.</p>

<h4> Step 1: Start Docker </h4>
<pre><code>docker run -it --gpus all \
-v &lt;your-model-path&gt;: /models
ghcr.io/01-ai/yi:latest
</code></pre>
<p>Alternatively, you can pull the Yi Docker image from <code>registry.lingyiwanwu.com/ci/01-ai/yi:latest</code>.</p>

<h4>Step 2: Perform inference</h4>
    <p>You can perform inference with Yi chat or base models as below.</p>
    
<h5>Perform inference with Yi chat model</h5>
    <p>The steps are similar to <a href="#perform-inference-with-yi-chat-model">pip - Perform inference with Yi chat model</a>.</p>
    <p><strong>Note</strong> that the only difference is to set <code>model_path = '&lt;your-model-mount-path&gt;'</code> instead of <code>model_path = '&lt;your-model-path&gt;'</code>.</p>
<h5>Perform inference with Yi base model</h5>
    <p>The steps are similar to <a href="#perform-inference-with-yi-base-model">pip - Perform inference with Yi base model</a>.</p>
    <p><strong>Note</strong> that the only difference is to set <code>--model &lt;your-model-mount-path&gt;'</code> instead of <code>model &lt;your-model-path&gt;</code>.</p>
</details>

### Quick start - conda-lock

<details>
<summary>You can use <code><a href="https://github.com/conda/conda-lock">conda-lock</a></code> to generate fully reproducible lock files for conda environments. ⬇️</summary>
<br>
You can refer to <a href="https://github.com/01-ai/Yi/blob/ebba23451d780f35e74a780987ad377553134f68/conda-lock.yml">conda-lock.yml</a>  for the exact versions of the dependencies. Additionally, you can utilize <code><a href="https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html">micromamba</a></code> for installing these dependencies.
<br>
To install the dependencies, follow these steps:

1. Install micromamba by following the instructions available <a href="https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html">here</a>.

2. Execute <code>micromamba install -y -n yi -f conda-lock.yml</code> to create a conda environment named <code>yi</code> and install the necessary dependencies.
</details>

### Quick start - llama.cpp
<details>
<summary> Run Yi-chat-6B-2bits locally with llama.cpp: a step-by-step guide. ⬇️</summary> 
<br>This tutorial guides you through every step of running a quantized model (<a href="https://huggingface.co/XeIaso/yi-chat-6B-GGUF/tree/main">Yi-chat-6B-2bits</a>) locally and then performing inference.</p>

- [Step 0: Prerequisites](#step-0-prerequisites)
- [Step 1: Download llama.cpp](#step-1-download-llamacpp)
- [Step 2: Download Yi model](#step-2-download-yi-model)
- [Step 3: Perform inference](#step-3-perform-inference)

#### Step 0: Prerequisites 

- This tutorial assumes you use a MacBook Pro with 16GB of memory and an Apple M2 Pro chip.
  
- Make sure [`git-lfs`](https://git-lfs.com/) is installed on your machine.
  
#### Step 1: Download `llama.cpp`

To clone the [`llama.cpp`](https://github.com/ggerganov/llama.cpp) repository, run the following command.

```bash
git clone [email protected]:ggerganov/llama.cpp.git
```

#### Step 2: Download Yi model

2.1 To clone [XeIaso/yi-chat-6B-GGUF](https://huggingface.co/XeIaso/yi-chat-6B-GGUF/tree/main) with just pointers, run the following command.

```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/XeIaso/yi-chat-6B-GGUF
```

2.2 To download a quantized Yi model ([yi-chat-6b.Q2_K.gguf](https://huggingface.co/XeIaso/yi-chat-6B-GGUF/blob/main/yi-chat-6b.Q2_K.gguf)), run the following command.

```bash
git-lfs pull --include yi-chat-6b.Q2_K.gguf
```

#### Step 3: Perform inference

To perform inference with the Yi model, you can use one of the following methods.

- [Method 1: Perform inference in terminal](#method-1-perform-inference-in-terminal)
  
- [Method 2: Perform inference in web](#method-2-perform-inference-in-web)

##### Method 1: Perform inference in terminal

To compile `llama.cpp` using 4 threads and then conduct inference, navigate to the `llama.cpp` directory, and run the following command.

> ##### Tips
> 
> - Replace `/Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf` with the actual path of your model.
>
> - By default, the model operates in completion mode.
> 
> - For additional output customization options (for example, system prompt, temperature, repetition penalty, etc.), run `./main -h` to check detailed descriptions and usage.

```bash
make -j4 && ./main -m /Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf -p "How do you feed your pet fox? Please answer this question in 6 simple steps:\nStep 1:" -n 384 -e

...

How do you feed your pet fox? Please answer this question in 6 simple steps:

Step 1: Select the appropriate food for your pet fox. You should choose high-quality, balanced prey items that are suitable for their unique dietary needs. These could include live or frozen mice, rats, pigeons, or other small mammals, as well as fresh fruits and vegetables.

Step 2: Feed your pet fox once or twice a day, depending on the species and its individual preferences. Always ensure that they have access to fresh water throughout the day.

Step 3: Provide an appropriate environment for your pet fox. Ensure it has a comfortable place to rest, plenty of space to move around, and opportunities to play and exercise.

Step 4: Socialize your pet with other animals if possible. Interactions with other creatures can help them develop social skills and prevent boredom or stress.

Step 5: Regularly check for signs of illness or discomfort in your fox. Be prepared to provide veterinary care as needed, especially for common issues such as parasites, dental health problems, or infections.

Step 6: Educate yourself about the needs of your pet fox and be aware of any potential risks or concerns that could affect their well-being. Regularly consult with a veterinarian to ensure you are providing the best care.

...

```

Now you have successfully asked a question to the Yi model and got an answer! 🥳

##### Method 2: Perform inference in web

1. To initialize a lightweight and swift chatbot, run the following command.

    ```bash
    cd llama.cpp
    ./server --ctx-size 2048 --host 0.0.0.0 --n-gpu-layers 64 --model /Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf
    ```

    Then you can get an output like this:


    ```bash
    ...

    llama_new_context_with_model: n_ctx      = 2048
    llama_new_context_with_model: freq_base  = 5000000.0
    llama_new_context_with_model: freq_scale = 1
    ggml_metal_init: allocating
    ggml_metal_init: found device: Apple M2 Pro
    ggml_metal_init: picking default device: Apple M2 Pro
    ggml_metal_init: ggml.metallib not found, loading from source
    ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
    ggml_metal_init: loading '/Users/yu/llama.cpp/ggml-metal.metal'
    ggml_metal_init: GPU name:   Apple M2 Pro
    ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)
    ggml_metal_init: hasUnifiedMemory              = true
    ggml_metal_init: recommendedMaxWorkingSetSize  = 11453.25 MB
    ggml_metal_init: maxTransferRate               = built-in GPU
    ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size =   128.00 MiB, ( 2629.44 / 10922.67)
    llama_new_context_with_model: KV self size  =  128.00 MiB, K (f16):   64.00 MiB, V (f16):   64.00 MiB
    ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size =     0.02 MiB, ( 2629.45 / 10922.67)
    llama_build_graph: non-view tensors processed: 676/676
    llama_new_context_with_model: compute buffer total size = 159.19 MiB
    ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size =   156.02 MiB, ( 2785.45 / 10922.67)
    Available slots:
    -> Slot 0 - max context: 2048

    llama server listening at http://0.0.0.0:8080
    ```

2. To access the chatbot interface, open your web browser and enter `http://0.0.0.0:8080` into the address bar. 
   
    ![Yi model chatbot interface - llama.cpp](https://github.com/01-ai/Yi/blob/main/assets/img/yi_llama_cpp1.png?raw=true)


3. Enter a question, such as "How do you feed your pet fox? Please answer this question in 6 simple steps" into the prompt window, and you will receive a corresponding answer.

    ![Ask a question to Yi model - llama.cpp](https://github.com/01-ai/Yi/blob/main/assets/img/yi_llama_cpp2.png?raw=true)

</ul>
</details>

### Web demo

You can build a web UI demo for Yi **chat** models (note that Yi base models are not supported in this senario).

[Step 1: Prepare your environment](#step-1-prepare-your-environment). 

[Step 2: Download the Yi model](#step-2-download-the-yi-model).

Step 3. To start a web service locally, run the following command.

```bash
python demo/web_demo.py -c <your-model-path>
```

You can access the web UI by entering the address provided in the console into your browser. 

 ![Quick start - web demo](https://github.com/01-ai/Yi/blob/main/assets/img/yi_34b_chat_web_demo.gif?raw=true)

### Fine-tuning

```bash
bash finetune/scripts/run_sft_Yi_6b.sh
```

Once finished, you can compare the finetuned model and the base model with the following command:

```bash
bash finetune/scripts/run_eval.sh
```
<details style="display: inline;"><summary>For advanced usage (like fine-tuning based on your custom data), see the explanations below. ⬇️ </summary> <ul>

### Finetune code for Yi 6B and 34B

#### Preparation

##### From Image

By default, we use a small dataset from [BAAI/COIG](https://huggingface.co/datasets/BAAI/COIG) to finetune the base model.
You can also prepare your customized dataset in the following `jsonl` format:

```json
{ "prompt": "Human: Who are you? Assistant:", "chosen": "I'm Yi." }
```

And then mount them in the container to replace the default ones:

```bash
docker run -it \
    -v /path/to/save/finetuned/model/:/finetuned-model \
    -v /path/to/train.jsonl:/yi/finetune/data/train.json \
    -v /path/to/eval.jsonl:/yi/finetune/data/eval.json \
    ghcr.io/01-ai/yi:latest \
    bash finetune/scripts/run_sft_Yi_6b.sh
```

##### From Local Server

Make sure you have conda. If not, use

```bash
mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm -rf ~/miniconda3/miniconda.sh
~/miniconda3/bin/conda init bash
source ~/.bashrc
```

Then, create a conda env:

```bash
conda create -n dev_env python=3.10 -y
conda activate dev_env
pip install torch==2.0.1 deepspeed==0.10 tensorboard transformers datasets sentencepiece accelerate ray==2.7
```

#### Hardware Setup

For the Yi-6B model, a node with 4 GPUs, each has GPU mem larger than 60GB is recommended.

For the Yi-34B model, because the usage of zero-offload technique takes a lot CPU memory, please be careful to limit the GPU numbers in 34B finetune training. Please use CUDA_VISIBLE_DEVICES to limit the GPU number (as shown in scripts/run_sft_Yi_34b.sh).

A typical hardware setup for finetuning 34B model is a node with 8GPUS (limit to 4 in running by CUDA_VISIBLE_DEVICES=0,1,2,3), each has GPU mem larger than 80GB, with total CPU mem larger than 900GB.

#### Quick Start

Download a LLM-base model to MODEL_PATH (6B and 34B). A typical folder of models is like:

```bash
|-- $MODEL_PATH
|   |-- config.json
|   |-- pytorch_model-00001-of-00002.bin
|   |-- pytorch_model-00002-of-00002.bin
|   |-- pytorch_model.bin.index.json
|   |-- tokenizer_config.json
|   |-- tokenizer.model
|   |-- ...
```

Download a dataset from huggingface to local storage DATA_PATH, e.g. Dahoas/rm-static.

```bash
|-- $DATA_PATH
|   |-- data
|   |   |-- train-00000-of-00001-2a1df75c6bce91ab.parquet
|   |   |-- test-00000-of-00001-8c7c51afc6d45980.parquet
|   |-- dataset_infos.json
|   |-- README.md
```

`finetune/yi_example_dataset` has example datasets, which are modified from [BAAI/COIG](https://huggingface.co/datasets/BAAI/COIG)

```bash
|-- $DATA_PATH
    |--data
        |-- train.jsonl
        |-- eval.jsonl
```

`cd` into the scripts folder, copy and paste the script, and run. For example:

```bash
cd finetune/scripts

bash run_sft_Yi_6b.sh
```

For the Yi-6B base model, setting training_debug_steps=20 and num_train_epochs=4 can output a chat model, which takes about 20 minutes.

For the Yi-34B base model, it takes a relatively long time for initialization. Please be patient.

#### Evaluation

```bash
cd finetune/scripts

bash run_eval.sh
```

Then you'll see the answer from both the base model and the finetuned model
</ul>
</details>

### Quantization

#### GPT-Q
```bash
python quantization/gptq/quant_autogptq.py \
  --model /base_model                      \
  --output_dir /quantized_model            \
  --trust_remote_code
```

Once finished, you can then evaluate the resulting model as follows:

```bash
python quantization/gptq/eval_quantized_model.py \
  --model /quantized_model                       \
  --trust_remote_code
```

<details style="display: inline;"><summary>For a more detailed explanation, see the explanations below. ⬇️</summary> <ul>

#### GPT-Q quantization

[GPT-Q](https://github.com/IST-DASLab/gptq) is a PTQ(Post-Training Quantization)
method. It's memory saving and provides potential speedups while retaining the accuracy
of the model. 

Yi models can be GPT-Q quantized without a lot of efforts. 
We provide a step-by-step tutorial below.

To run GPT-Q, we will use [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) and
[exllama](https://github.com/turboderp/exllama).
And the huggingface transformers has integrated optimum and auto-gptq to perform
GPTQ quantization on language models.

##### Do Quantization

The `quant_autogptq.py` script is provided for you to perform GPT-Q quantization:

```bash
python quant_autogptq.py --model /base_model \
    --output_dir /quantized_model --bits 4 --group_size 128 --trust_remote_code
```


##### Run Quantized Model

You can run a quantized model using the `eval_quantized_model.py`:

```bash
python eval_quantized_model.py --model /quantized_model --trust_remote_code
```
</ul>
</details>

#### AWQ
```bash
python quantization/awq/quant_autoawq.py \
  --model /base_model                      \
  --output_dir /quantized_model            \
  --trust_remote_code
```

Once finished, you can then evaluate the resulting model as follows:

```bash
python quantization/awq/eval_quantized_model.py \
  --model /quantized_model                       \
  --trust_remote_code
```
<details style="display: inline;"><summary>For detailed explanations, see the explanations below. ⬇️</summary> <ul>

#### AWQ quantization

[AWQ](https://github.com/mit-han-lab/llm-awq) is a PTQ(Post-Training Quantization)
method. It's an efficient and accurate low-bit weight quantization (INT3/4) for LLMs.

Yi models can be AWQ quantized without a lot of efforts. 
We provide a step-by-step tutorial below.

To run AWQ, we will use [AutoAWQ](https://github.com/casper-hansen/AutoAWQ).

##### Do Quantization

The `quant_autoawq.py` script is provided for you to perform AWQ quantization:

```bash
python quant_autoawq.py --model /base_model \
    --output_dir /quantized_model --bits 4 --group_size 128 --trust_remote_code
```

##### Run Quantized Model

You can run a quantized model using the `eval_quantized_model.py`:

```bash
python eval_quantized_model.py --model /quantized_model --trust_remote_code
```


</ul>
</details>
<div align="right"> [ <a href="#building-the-next-generation-of-open-source-and-bilingual-llms">Back to top ⬆️ </a> ] </div>

### Deployment

If you want to deploy Yi models, make sure you meet the software and hardware requirements. 

#### Software requirements

Before using Yi quantized models, make sure you've installed the correct software listed below.

| Model | Software
|---|---
Yi 4-bit quantized models | [AWQ and CUDA](https://github.com/casper-hansen/AutoAWQ?tab=readme-ov-file#install-from-pypi)
Yi 8-bit quantized models |  [GPTQ and CUDA](https://github.com/PanQiWei/AutoGPTQ?tab=readme-ov-file#quick-installation)

#### Hardware requirements

Before deploying Yi in your environment, make sure your hardware meets the following requirements.

##### Chat models

| Model                | Minimum VRAM |        Recommended GPU Example       |
|----------------------|--------------|:-------------------------------------:|
| Yi-6B-Chat           | 15 GB         | RTX 3090 <br> RTX 4090 <br>  A10 <br> A30             |
| Yi-6B-Chat-4bits     | 4 GB          | RTX 3060 <br>  RTX 4060                     |
| Yi-6B-Chat-8bits     | 8 GB          | RTX 3070 <br> RTX 4060                     |
| Yi-34B-Chat          | 72 GB         | 4 x RTX 4090 <br> A800 (80GB)               |
| Yi-34B-Chat-4bits    | 20 GB         | RTX 3090  <br> RTX 4090 <br> A10 <br> A30 <br> A100 (40GB) |
| Yi-34B-Chat-8bits    | 38 GB         | 2 x RTX 3090  <br> 2 x RTX 4090 <br> A800  (40GB) |

Below are detailed minimum VRAM requirements under different batch use cases.

|  Model                  | batch=1 | batch=4 | batch=16 | batch=32 |
| ----------------------- | ------- | ------- | -------- | -------- |
| Yi-6B-Chat              | 12 GB   | 13 GB   | 15 GB    | 18 GB    |
| Yi-6B-Chat-4bits  | 4 GB    | 5 GB    | 7 GB     | 10 GB    |
| Yi-6B-Chat-8bits  | 7 GB    | 8 GB    | 10 GB    | 14 GB    |
| Yi-34B-Chat       | 65 GB   | 68 GB   | 76 GB    | > 80 GB   |
| Yi-34B-Chat-4bits | 19 GB   | 20 GB   | 30 GB    | 40 GB    |
| Yi-34B-Chat-8bits | 35 GB   | 37 GB   | 46 GB    | 58 GB    |

##### Base models

| Model                | Minimum VRAM |        Recommended GPU Example       |
|----------------------|--------------|:-------------------------------------:|
| Yi-6B                | 15 GB         | RTX3090 <br> RTX4090 <br> A10 <br> A30               |
| Yi-6B-200K           | 50 GB         | A800 (80 GB)                            |
| Yi-34B               | 72 GB         | 4 x RTX 4090 <br> A800 (80 GB)               |
| Yi-34B-200K          | 200 GB        | 4 x A800 (80 GB)                        |

### Learning hub

<details>
<summary> If you want to learn Yi, you can find a wealth of helpful educational resources here. ⬇️</summary> 
<br> 
  
Welcome to the Yi learning hub! 

Whether you're a seasoned developer or a newcomer, you can find a wealth of helpful educational resources to enhance your understanding and skills with Yi models, including insightful blog posts, comprehensive video tutorials, hands-on guides, and more.  

The content you find here has been generously contributed by knowledgeable Yi experts and passionate enthusiasts. We extend our heartfelt gratitude for your invaluable contributions! 

At the same time, we also warmly invite you to join our collaborative effort by contributing to Yi. If you have already made contributions to Yi, please don't hesitate to showcase your remarkable work in the table below.

With all these resources at your fingertips, you're ready to start your exciting journey with Yi. Happy learning! 🥳

#### Tutorials

| Type        | Deliverable                                            |      Date      |     Author     |
|-------------|--------------------------------------------------------|----------------|----------------|
| Blog        | [本地运行零一万物 34B 大模型,使用 Llama.cpp & 21G 显存](https://zhuanlan.zhihu.com/p/668921042)                  |  2023-11-26  |  [苏洋](https://github.com/soulteary)  |
| Blog        | [Running Yi-34B-Chat locally using LlamaEdge](https://www.secondstate.io/articles/yi-34b/)                   |  2023-11-30  |  [Second State](https://github.com/second-state)  |
| Blog        | [零一万物模型折腾笔记:官方 Yi-34B 模型基础使用](https://zhuanlan.zhihu.com/p/671387298)                           | 2023-12-10 |  [苏洋](https://github.com/soulteary)  |
| Blog        | [CPU 混合推理,非常见大模型量化方案:“二三五六” 位量化方案](https://zhuanlan.zhihu.com/p/671698216)                  | 2023-12-12 |  [苏洋](https://github.com/soulteary)  |
| Blog        | [零一万物开源Yi-VL多模态大模型,魔搭社区推理&微调最佳实践来啦!](https://zhuanlan.zhihu.com/p/680098411) | 2024-01-26  |  [ModelScope](https://github.com/modelscope)  |
| Video       | [只需 24G 显存,用 vllm 跑起来 Yi-34B 中英双语大模型](https://www.bilibili.com/video/BV17t4y1f7Ee/)               | 2023-12-28 |  漆妮妮  |
| Video       | [Install Yi 34B Locally - Chinese English Bilingual LLM](https://www.youtube.com/watch?v=CVQvj4Wrh4w&t=476s) | 2023-11-05  |  Fahd Mirza  |
</details>


# 🟢 Why Yi? 

  - [🌎 Ecosystem](#-ecosystem)
    - [💦 Upstream](#-upstream)
    - [🌊 Downstream](#-downstream)
      - [🔗 Serving](#-serving)
      - [⚙️ Quantitation](#️-quantitation)
      - [🛠️ Fine-tuning](#️-fine-tuning)
      - [API](#api)
  - [📌 Benchmarks](#-benchmarks)
    - [📊 Chat model performance](#-chat-model-performance)
    - [📊 Base model performance](#-base-model-performance)
 
## 🌎 Ecosystem

Yi has a comprehensive ecosystem, offering a range of tools, services, and models to enrich your experiences and maximize productivity.

- [💦 Upstream](#-upstream)
- [🌊 Downstream](#-downstream)
  - [🔗 Serving](#-serving)
  - [⚙️ Quantitation](#️-quantitation)
  - [🛠️ Fine-tuning](#️-fine-tuning)
  - [API](#api)

### 💦 Upstream

The Yi series models follow the same model architecture as LLaMA. By choosing Yi, you can leverage existing tools, libraries, and resources within the LLaMA ecosystem, eliminating the need to create new tools and enhancing development efficiency.

For example, the Yi series models are saved in the format of the LLaMA model. You can directly use `LLaMAForCausalLM` and `LLaMATokenizer` to load the model. For more information, see [Use the chat model](#31-use-the-chat-model).

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-34b", use_fast=False)

model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-34b", device_map="auto")
```

### 🌊 Downstream

> 💡 Tip
> 
> - Feel free to create a PR and share the fantastic work you've built using the Yi series models.
>
> - To help others quickly understand your work, it is recommended to use the format of `<model-name>: <model-intro> + <model-highlights>`.

#### 🔗 Serving 

If you want to get up with Yi in a few minutes, you can use the following services built upon Yi.

- Yi-34B-Chat: you can chat with Yi using one of the following platforms:
  - [Yi-34B-Chat | Hugging Face](https://huggingface.co/spaces/01-ai/Yi-34B-Chat)
  - [Yi-34B-Chat | Yi Platform](https://platform.lingyiwanwu.com/): **Note** that currently it's available through a whitelist. Welcome to apply (fill out a form in [English](https://cn.mikecrm.com/l91ODJf) or [Chinese](https://cn.mikecrm.com/gnEZjiQ)) and experience it firsthand!
  
- [Yi-6B-Chat (Replicate)](https://replicate.com/01-ai): you can use this model with more options by setting additional parameters and calling APIs.
  
- [ScaleLLM](https://github.com/vectorch-ai/ScaleLLM#supported-models): you can use this service to run Yi models locally with added flexibility and customization.
  
#### ⚙️ Quantitation

If you have limited computational capabilities, you can use Yi's quantized models as follows. 

These quantized models have reduced precision but offer increased efficiency, such as faster inference speed and smaller RAM usage.

- [TheBloke/Yi-34B-GPTQ](https://huggingface.co/TheBloke/Yi-34B-GPTQ) 
- [TheBloke/Yi-34B-GGUF](https://huggingface.co/TheBloke/Yi-34B-GGUF)
- [TheBloke/Yi-34B-AWQ](https://huggingface.co/TheBloke/Yi-34B-AWQ)
  
#### 🛠️ Fine-tuning

If you're seeking to explore the diverse capabilities within Yi's thriving family, you can delve into Yi's fine-tuned models as below.

- [TheBloke Models](https://huggingface.co/TheBloke): this site hosts numerous fine-tuned models derived from various LLMs including Yi. 
  
  This is not an exhaustive list for Yi, but to name a few sorted on downloads:
  - [TheBloke/dolphin-2_2-yi-34b-AWQ](https://huggingface.co/TheBloke/dolphin-2_2-yi-34b-AWQ)
  - [TheBloke/Yi-34B-Chat-AWQ](https://huggingface.co/TheBloke/Yi-34B-Chat-AWQ)
  - [TheBloke/Yi-34B-Chat-GPTQ](https://huggingface.co/TheBloke/Yi-34B-Chat-GPTQ)
  
- [SUSTech/SUS-Chat-34B](https://huggingface.co/SUSTech/SUS-Chat-34B): this model ranked first among all models below 70B and outperformed the twice larger deepseek-llm-67b-chat. You can check the result on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
  
- [OrionStarAI/OrionStar-Yi-34B-Chat-Llama](https://huggingface.co/OrionStarAI/OrionStar-Yi-34B-Chat-Llama): this model excelled beyond other models (such as GPT-4, Qwen-14B-Chat, Baichuan2-13B-Chat) in C-Eval and CMMLU evaluations on the [OpenCompass LLM Leaderboard](https://opencompass.org.cn/leaderboard-llm). 
  
- [NousResearch/Nous-Capybara-34B](https://huggingface.co/NousResearch/Nous-Capybara-34B): this model is trained with 200K context length and 3 epochs on the Capybara dataset. 

#### API

- [amazing-openai-api](https://github.com/soulteary/amazing-openai-api): this tool converts Yi model APIs into the OpenAI API format out of the box.
- [LlamaEdge](https://www.secondstate.io/articles/yi-34b/#create-an-openai-compatible-api-service-for-the-yi-34b-chat-model): this tool builds an OpenAI-compatible API server for Yi-34B-Chat using a portable Wasm (WebAssembly) file, powered by Rust.

<div align="right"> [ <a href="#building-the-next-generation-of-open-source-and-bilingual-llms">Back to top ⬆️ </a> ] </div>

## 📌 Benchmarks 

- [📊 Chat model performance](#-chat-model-performance)
- [📊 Base model performance](#-base-model-performance)

### 📊 Chat model performance

Yi-34B-Chat model demonstrates exceptional performance, ranking first among all existing open-source models in the benchmarks including MMLU, CMMLU, BBH, GSM8k, and more.

![Chat model performance](https://github.com/01-ai/Yi/blob/main/assets/img/benchmark_chat.png?raw=true) 

<details>
<summary> Evaluation methods and challenges. ⬇️ </summary>

- **Evaluation methods**: we evaluated various benchmarks using both zero-shot and few-shot methods, except for TruthfulQA.
- **Zero-shot vs. few-shot**: in chat models, the zero-shot approach is more commonly employed.
- **Evaluation strategy**: our evaluation strategy involves generating responses while following instructions explicitly or implicitly (such as using few-shot examples). We then isolate relevant answers from the generated text.
- **Challenges faced**: some models are not well-suited to produce output in the specific format required by instructions in few datasets, which leads to suboptimal results.

<strong>*</strong>: C-Eval results are evaluated on the validation datasets
</details>

### 📊 Base model performance

The Yi-34B and Yi-34B-200K models stand out as the top performers among open-source models, especially excelling in MMLU, CMML, common-sense reasoning, reading comprehension, and more.

![Base model performance](https://github.com/01-ai/Yi/blob/main/assets/img/benchmark_base.png?raw=true)

<details>
<summary> Evaluation methods. ⬇️</summary>

- **Disparity in results**: while benchmarking open-source models, a disparity has been noted between results from our pipeline and those reported by public sources like OpenCompass.
- **Investigation findings**: a deeper investigation reveals that variations in prompts, post-processing strategies, and sampling techniques across models may lead to significant outcome differences.
- **Uniform benchmarking process**: our methodology aligns with the original benchmarks—consistent prompts and post-processing strategies are used, and greedy decoding is applied during evaluations without any post-processing for the generated content.
- **Efforts to retrieve unreported scores**: for scores that were not reported by the original authors (including scores reported with different settings), we try to get results with our pipeline.
- **Extensive model evaluation**: to evaluate the model’s capability extensively, we adopted the methodology outlined in Llama2. Specifically, we included PIQA, SIQA, HellaSwag, WinoGrande, ARC, OBQA, and CSQA to assess common sense reasoning. SquAD, QuAC, and BoolQ were incorporated to evaluate reading comprehension.
- **Special configurations**: CSQA was exclusively tested using a 7-shot setup, while all other tests were conducted with a 0-shot configuration. Additionally, we introduced GSM8K (8-shot@1), MATH (4-shot@1), HumanEval (0-shot@1), and MBPP (3-shot@1) under the category "Math & Code".
- **Falcon-180B caveat**: Falcon-180B was not tested on QuAC and OBQA due to technical constraints. Its performance score is an average from other tasks, and considering the generally lower scores of these two tasks, Falcon-180B's capabilities are likely not underestimated.
</details>

# 🟢 Who can use Yi?

Everyone! 🙌 ✅

- The Yi series models are free for personal usage, academic purposes, and commercial use. All usage must adhere to the [Yi Series Models Community License Agreement 2.1](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt)
  
- For free commercial use, you only need to [complete this form](https://www.lingyiwanwu.com/yi-license) to get a Yi Model Commercial License.

<div align="right"> [ <a href="#building-the-next-generation-of-open-source-and-bilingual-llms">Back to top ⬆️ </a> ] </div>

# 🟢 Misc.

### Acknowledgments

A heartfelt thank you to each of you who have made contributions to the Yi community! You have helped Yi not just a project, but a vibrant, growing home for innovation.

[![yi contributors](https://contrib.rocks/image?repo=01-ai/yi&max=2000&columns=15)](https://github.com/01-ai/yi/graphs/contributors)

<div align="right"> [ <a href="#building-the-next-generation-of-open-source-and-bilingual-llms">Back to top ⬆️ </a> ] </div>

### 📡 Disclaimer

We use data compliance checking algorithms during the training process, to
ensure the compliance of the trained model to the best of our ability. Due to
complex data and the diversity of language model usage scenarios, we cannot
guarantee that the model will generate correct, and reasonable output in all
scenarios. Please be aware that there is still a risk of the model producing
problematic outputs. We will not be responsible for any risks and issues
resulting from misuse, misguidance, illegal usage, and related misinformation,
as well as any associated data security concerns.

<div align="right"> [ <a href="#building-the-next-generation-of-open-source-and-bilingual-llms">Back to top ⬆️ </a> ] </div>

### 🪪 License

The source code in this repo is licensed under the [Apache 2.0
license](https://github.com/01-ai/Yi/blob/main/LICENSE). The Yi series models are fully open for academic research and free for commercial use, with automatic permission granted upon application. All usage must adhere to the [Yi Series Models Community License Agreement 2.1](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt).
For free commercial use, you only need to send an email to [get official commercial permission](https://www.lingyiwanwu.com/yi-license).

<div align="right"> [ <a href="#building-the-next-generation-of-open-source-and-bilingual-llms">Back to top ⬆️ </a> ] </div>