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Create models.py

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  1. models.py +690 -0
models.py ADDED
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1
+ def return_task_name():
2
+ return ('text2text-generation', "text-generation")
3
+
4
+
5
+ def return_models(task_name):
6
+ if task_name == "text2text-generation":
7
+ models_parent = (
8
+ 'google',
9
+ "mbzuai",
10
+ "bloom",
11
+ "lmsys",
12
+ "facebook"
13
+ )
14
+ else:
15
+ models_parent = (
16
+ 'google',
17
+ "mbzuai",
18
+ "eleutherai",
19
+ "cerebras",
20
+ "open_assistant",
21
+ "databricks",
22
+ "nomic_ai",
23
+ "blinkdl",
24
+ "aisquared",
25
+ "bloom",
26
+ "tiiuae",
27
+ "openlm",
28
+ "stabilityai",
29
+ "lmsys",
30
+ "together_computer",
31
+ "mosaic_ml",
32
+ "h20ai",
33
+ "facebook"
34
+
35
+ )
36
+ return models_parent
37
+
38
+ def return_text2text_generation_models(model_parent, count=False):
39
+ google_models_list = (
40
+ 'google/flan-t5-xl',
41
+ 'google/flan-t5-xxl',
42
+ 'google/flan-t5-large',
43
+ "google/flan-t5-small",
44
+ "google/flan-t5-base",
45
+ "google/byt5-xxl",
46
+ "google/byt5-xl",
47
+ "google/byt5-large",
48
+ "google/byt5-small",
49
+ "google/byt5-base",
50
+ "google/mt5-xxl",
51
+ "google/mt5-xl",
52
+ "google/mt5-large",
53
+ "google/mt5-small",
54
+ "google/long-t5-local-base",
55
+ "google/long-t5-local-large",
56
+ "google/long-t5-tglobal-base",
57
+ "google/long-t5-tglobal-large",
58
+ "google/pegasus-big_patent",
59
+ "google/pegasus-x-base",
60
+ "google/pegasus-x-large",
61
+ "google/pegasus-x-base-arxiv",
62
+ "google/roberta2roberta_L-24_wikisplit",
63
+ "google/roberta2roberta_L-24_discofuse",
64
+ "google/switch-base-8",
65
+ "google/switch-base-16",
66
+ "google/switch-base-32",
67
+ "google/switch-base-64",
68
+ "google/switch-base-128",
69
+ "google/switch-base-256",
70
+ "google/switch-large-128",
71
+ "google/switch-xxl-128",
72
+ "google/switch-c-2048",
73
+ "google/t5-11b-ssm",
74
+ "google/t5-11b-ssm-nq",
75
+ "google/t5-11b-ssm-nqo",
76
+ "google/t5-11b-ssm-tqa",
77
+ "google/t5-11b-ssm-tqao",
78
+ "google/t5-11b-ssm-wq",
79
+ "google/t5-11b-ssm-wqo",
80
+ "google/t5-3b-ssm",
81
+ "google/t5-3b-ssm-nq",
82
+ "google/t5-3b-ssm-nqo",
83
+ "google/t5-base-lm-adapt",
84
+ "google/t5-base-lm-adapt",
85
+ "google/t5-efficient-base",
86
+ "google/t5-efficient-base-dl2",
87
+ "google/t5-efficient-base-dl4",
88
+ "google/t5-efficient-base-dl6",
89
+ "google/t5-efficient-base-dl8",
90
+ "google/t5-efficient-base-dm256",
91
+ "google/t5-efficient-base-dm512",
92
+ "google/t5-efficient-base-dm1000",
93
+ "google/t5-efficient-base-dm2000",
94
+ "google/t5-efficient-base-el2",
95
+ "google/t5-efficient-base-el4",
96
+ "google/t5-efficient-base-el6",
97
+ "google/t5-efficient-base-el8",
98
+ "google/t5-efficient-base-el16",
99
+ "google/t5-efficient-base-nl40",
100
+ "google/t5-efficient-base-nl48",
101
+ "google/t5-efficient-base-nl8",
102
+ "google/t5-efficient-large",
103
+ "google/t5-efficient-large-dl12",
104
+ "google/t5-efficient-large-dl16",
105
+ "google/t5-efficient-large-dl2",
106
+ "google/t5-efficient-large-dl32",
107
+ "google/t5-efficient-large-dl4",
108
+ "google/t5-efficient-large-dl6",
109
+ "google/t5-efficient-large-dl8",
110
+ "google/t5-efficient-large-dm128",
111
+ "google/t5-efficient-large-dm2000",
112
+ "google/t5-efficient-large-dm256",
113
+ "google/t5-efficient-large-dm512",
114
+ "google/t5-efficient-large-dm768",
115
+ "google/t5-efficient-large-el12",
116
+ "google/t5-efficient-large-el2",
117
+ "google/t5-efficient-large-el4",
118
+ "google/t5-efficient-large-el6",
119
+ "google/t5-efficient-large-el8",
120
+ "google/t5-efficient-large-kv128",
121
+ "google/t5-efficient-large-kv16",
122
+ "google/t5-efficient-large-kv256",
123
+ "google/t5-efficient-large-kv32",
124
+ "google/t5-efficient-large-nh12",
125
+ "google/t5-efficient-large-nh2",
126
+ "google/t5-efficient-large-nh24",
127
+ "google/t5-efficient-large-nh32",
128
+ "google/t5-efficient-large-nh4",
129
+ "google/t5-efficient-large-nh8",
130
+ "google/t5-efficient-large-nh8-nl32",
131
+ "google/t5-efficient-large-nl10",
132
+ "google/t5-efficient-large-nl12",
133
+ "google/t5-efficient-large-nl16",
134
+ "google/t5-efficient-large-nl2",
135
+ "google/t5-efficient-large-nl20",
136
+ "google/t5-efficient-large-nl32",
137
+ "google/t5-efficient-large-nl36",
138
+ "google/t5-efficient-large-nl4",
139
+ "google/t5-efficient-large-nl8",
140
+ "google/t5-efficient-mini",
141
+ "google/t5-efficient-mini-nl12",
142
+ "google/t5-efficient-mini-nl24",
143
+ "google/t5-efficient-mini-nl6",
144
+ "google/t5-efficient-mini-nl8",
145
+ "google/t5-efficient-small",
146
+ "google/t5-efficient-small-dl12",
147
+ "google/t5-efficient-small-dl16",
148
+ "google/t5-efficient-small-dl2",
149
+ "google/t5-efficient-small-dl4",
150
+ "google/t5-efficient-small-dl8",
151
+ "google/t5-efficient-small-dm1000",
152
+ "google/t5-efficient-small-dm128",
153
+ "google/t5-efficient-small-dm2000",
154
+ "google/t5-efficient-small-dm256",
155
+ "google/t5-efficient-small-dm768",
156
+ "google/t5-efficient-small-el12",
157
+ "google/t5-efficient-small-el16",
158
+ "google/t5-efficient-small-el16-dl1",
159
+ "google/t5-efficient-small-el16-dl2",
160
+ "google/t5-efficient-small-el16-dl4",
161
+ "google/t5-efficient-small-el16-dl8",
162
+ "google/t5-efficient-small-el2",
163
+ "google/t5-efficient-small-el32",
164
+ "google/t5-efficient-small-el4",
165
+ "google/t5-efficient-small-el48",
166
+ "google/t5-efficient-small-el64",
167
+ "google/t5-efficient-small-el8",
168
+ "google/t5-efficient-small-el8-dl1",
169
+ "google/t5-efficient-small-el8-dl2",
170
+ "google/t5-efficient-small-el8-dl4",
171
+ "google/t5-efficient-small-ff1000",
172
+ "google/t5-efficient-small-ff12000",
173
+ "google/t5-efficient-small-ff3000",
174
+ "google/t5-efficient-small-ff6000",
175
+ "google/t5-efficient-small-ff9000",
176
+ "google/t5-efficient-small-kv128",
177
+ "google/t5-efficient-small-kv16",
178
+ "google/t5-efficient-small-kv256",
179
+ "google/t5-efficient-small-kv32",
180
+ "google/t5-efficient-small-nl16",
181
+ "google/t5-efficient-small-nl2",
182
+ "google/t5-efficient-small-nl20",
183
+ "google/t5-efficient-small-nl22",
184
+ "google/t5-efficient-small-nl24",
185
+ "google/t5-efficient-small-nl32",
186
+ "google/t5-efficient-small-nl36",
187
+ "google/t5-efficient-small-nl4",
188
+ "google/t5-efficient-small-nl40",
189
+ "google/t5-efficient-small-nl48",
190
+ "google/t5-efficient-small-nl8",
191
+ "google/t5-efficient-tiny",
192
+ "google/t5-efficient-tiny-dl2",
193
+ "google/t5-efficient-tiny-dl6",
194
+ "google/t5-efficient-tiny-dl8",
195
+ "google/t5-efficient-tiny-el12",
196
+ "google/t5-efficient-tiny-el2",
197
+ "google/t5-efficient-tiny-el6",
198
+ "google/t5-efficient-tiny-el8",
199
+ "google/t5-efficient-tiny-ff12000",
200
+ "google/t5-efficient-tiny-ff2000",
201
+ "google/t5-efficient-tiny-ff3000",
202
+ "google/t5-efficient-tiny-ff6000",
203
+ "google/t5-efficient-tiny-ff9000",
204
+ "google/t5-efficient-tiny-nh1",
205
+ "google/t5-efficient-tiny-nh16",
206
+ "google/t5-efficient-tiny-nh32",
207
+ "google/t5-efficient-tiny-nh8",
208
+ "google/t5-efficient-tiny-nl12",
209
+ "google/t5-efficient-tiny-nl16",
210
+ "google/t5-efficient-tiny-nl2",
211
+ "google/t5-efficient-tiny-nl24",
212
+ "google/t5-efficient-tiny-nl32",
213
+ "google/t5-efficient-tiny-nl6",
214
+ "google/t5-efficient-tiny-nl8",
215
+ "google/t5-efficient-xl",
216
+ "google/t5-efficient-xl-nl12",
217
+ "google/t5-efficient-xl-nl16",
218
+ "google/t5-efficient-xl-nl2",
219
+ "google/t5-efficient-xl-nl28",
220
+ "google/t5-efficient-xl-nl4",
221
+ "google/t5-efficient-xl-nl6",
222
+ "google/t5-efficient-xl-nl8",
223
+ "google/t5-efficient-xxl",
224
+ "google/t5-efficient-xxl-nl4",
225
+ "google/t5-large-lm-adapt",
226
+ "google/t5-large-ssm",
227
+ "google/t5-large-ssm-nq",
228
+ "google/t5-large-ssm-nqo",
229
+ "google/t5-small-lm-adapt",
230
+ "google/t5-small-ssm",
231
+ "google/t5-small-ssm-nq",
232
+ "google/t5-v1_1-base",
233
+ "google/t5-v1_1-large",
234
+ "google/t5-v1_1-small",
235
+ "google/t5-v1_1-xl",
236
+ "google/t5-v1_1-xxl",
237
+ "google/t5-xl-lm-adapt",
238
+ "google/t5-xl-ssm-nq",
239
+ "google/t5-xxl-lm-adapt",
240
+ "google/t5-xxl-ssm",
241
+ "google/t5-xxl-ssm-nq",
242
+ "google/t5-xxl-ssm-nqo",
243
+ "google/t5-xxl-ssm-tqa",
244
+ "google/t5-xxl-ssm-tqao",
245
+ "google/t5-xxl-ssm-wq",
246
+ "google/t5-xxl-ssm-wqo",
247
+ "google/t5_11b_trueteacher_and_anli",
248
+ "google/ul2",
249
+ "google/umt5-base",
250
+ "google/umt5-small",
251
+ "google/umt5-xl",
252
+ "google/umt5-xxl",
253
+ )
254
+
255
+ mbzuai_models_list = (
256
+ "MBZUAI/LaMini-Flan-T5-783M",
257
+ "MBZUAI/LaMini-Flan-T5-248M",
258
+ "MBZUAI/LaMini-Flan-T5-77M",
259
+ "MBZUAI/LaMini-T5-738M",
260
+ "MBZUAI/LaMini-T5-223M",
261
+ "MBZUAI/LaMini-T5-61M",
262
+ )
263
+ bloom_models_list = (
264
+ "bigscience/T0_3B",
265
+ "bigscience/T0_original_task_only",
266
+ "bigscience/T0_single_prompt",
267
+ "bigscience/T0p",
268
+ "bigscience/T0",
269
+ "bigscience/T0pp",
270
+ "bigscience/mt0-xxl-p3",
271
+ "bigscience/mt0-xxl",
272
+ "bigscience/mt0-large",
273
+ "bigscience/mt0-base",
274
+ "bigscience/mt0-small",
275
+ "bigscience/mt0-xxl-mt",
276
+ )
277
+
278
+ lmsys_models_list = (
279
+ "lmsys/fastchat-t5-3b-v1.0",
280
+ )
281
+
282
+ facebook_models_list = (
283
+ 'facebook/mbart-large-50-many-to-many-mmt',
284
+ 'facebook/musicgen-small',
285
+ 'facebook/musicgen-medium',
286
+ "facebook/musicgen-large",
287
+ 'facebook/m2m100_418M',
288
+ 'facebook/mbart-large-50-one-to-many-mmt',
289
+ 'facebook/mbart-large-50-many-to-one-mmt',
290
+ 'facebook/mbart-large-50',
291
+ 'facebook/mgenre-wiki',
292
+ 'facebook/genre-linking-aidayago2',
293
+ 'facebook/genre-linking-blink',
294
+ 'facebook/genre-kilt',
295
+ 'facebook/m2m100-12B-avg-10-ckpt',
296
+ 'facebook/m2m100-12B-avg-5-ckpt',
297
+ 'facebook/m2m100-12B-last-ckpt',
298
+ 'facebook/m2m100_1.2B'
299
+ )
300
+
301
+ model_dict = {
302
+ "google": google_models_list,
303
+ "mbzuai": mbzuai_models_list,
304
+ "bloom": bloom_models_list,
305
+ "lmsys": lmsys_models_list,
306
+ "facebook": facebook_models_list
307
+ }
308
+ if count is True:
309
+ models_count = 0
310
+ for i in model_dict:
311
+ models_count += len(model_dict[i])
312
+ return models_count
313
+
314
+ return model_dict[model_parent]
315
+
316
+
317
+ # Text Generation Models
318
+ def return_text_generation_models(model_parent, count=False):
319
+ google_models_list = (
320
+ "google/reformer-enwik8",
321
+ "google/reformer-crime-and-punishment",
322
+ )
323
+ mbzuai_models_list = (
324
+ "MBZUAI/LaMini-Cerebras-111M",
325
+ "MBZUAI/LaMini-Cerebras-256M",
326
+ "MBZUAI/LaMini-Cerebras-590M",
327
+ "MBZUAI/LaMini-Cerebras-1.3B",
328
+ "MBZUAI/LaMini-GPT-774M",
329
+ "MBZUAI/LaMini-GPT-124M",
330
+ "MBZUAI/LaMini-GPT-1.5B",
331
+ "MBZUAI/LaMini-Neo-125M",
332
+ "MBZUAI/LaMini-Neo-1.3B",
333
+ )
334
+
335
+ eleutherai_models_list=(
336
+ "EleutherAI/pythia-14m",
337
+ "EleutherAI/pythia-31m",
338
+ "EleutherAI/pythia-1b-deduped",
339
+ "EleutherAI/pythia-2.8b-v0",
340
+ "EleutherAI/pythia-1b-v0",
341
+ "EleutherAI/pythia-410m-v0",
342
+ "EleutherAI/pythia-70m-deduped-v0",
343
+ "EleutherAI/pythia-2.8b-deduped-v0",
344
+ "EleutherAI/pythia-1b-deduped-v0",
345
+ "EleutherAI/pythia-410m-deduped-v0",
346
+ "EleutherAI/pythia-160m-deduped-v0",
347
+ "EleutherAI/pythia-6.9b-deduped-v0",
348
+ "EleutherAI/pythia-70m-deduped",
349
+ "EleutherAI/pythia-70m",
350
+ "EleutherAI/pythia-2.8b-deduped",
351
+ "EleutherAI/pythia-1b",
352
+ "EleutherAI/pythia-410m-deduped",
353
+ "EleutherAI/pythia-160m-deduped",
354
+ "EleutherAI/pythia-160m-v0",
355
+ "EleutherAI/pythia-1.4b-deduped-v0",
356
+ "EleutherAI/pythia-1.4b",
357
+ "EleutherAI/pythia-410m",
358
+ "EleutherAI/pythia-intervention-410m-deduped",
359
+ "EleutherAI/gpt-neo-125m",
360
+ "EleutherAI/gpt-neo-2.7B",
361
+ "EleutherAI/gpt-neo-1.3B",
362
+ "EleutherAI/pythia-160m",
363
+ "EleutherAI/gpt-neox-20b",
364
+ "EleutherAI/gpt-j-6b",
365
+ "EleutherAI/pythia-2.8b",
366
+ "EleutherAI/pythia-12b-deduped",
367
+ "EleutherAI/pythia-6.9b-deduped",
368
+ "EleutherAI/pythia-1.4b-deduped",
369
+ "EleutherAI/pythia-12b",
370
+ "EleutherAI/pythia-6.9b",
371
+ "EleutherAI/polyglot-ko-12.8b",
372
+ "EleutherAI/polyglot-ko-5.8b",
373
+ "EleutherAI/polyglot-ko-3.8b",
374
+ "EleutherAI/polyglot-ko-1.3b",
375
+ "EleutherAI/pythia-intervention-6.9b-deduped",
376
+ "EleutherAI/pythia-intervention-1.4b-deduped",
377
+ "EleutherAI/pythia-intervention-70m-deduped",
378
+ "EleutherAI/pythia-intervention-long-1.4b-deduped",
379
+ "EleutherAI/pythia-70m-v0",
380
+ "EleutherAI/pythia-1.4b-v0",
381
+ "EleutherAI/pythia-6.9b-v0",
382
+ "EleutherAI/pythia-12b-deduped-v0",
383
+ "EleutherAI/pythia-12b-v0",
384
+ "EleutherAI/pythia-160m-seed3",
385
+ "EleutherAI/pythia-160m-seed2",
386
+ "EleutherAI/pythia-160m-seed1",
387
+ "EleutherAI/neox-ckpt-pythia-6.9b-deduped",
388
+ "EleutherAI/pythia-160m-hiddendropout",
389
+ "EleutherAI/pythia-160m-attndropout",
390
+ "EleutherAI/pythia-160m-alldropout",
391
+ "EleutherAI/pythia-6.9b-deduped-v0-seed42",
392
+ )
393
+ cerebras_models_list = (
394
+ "cerebras/btlm-3b-8k-base",
395
+ "cerebras/cerebras-GPT-13B",
396
+ "cerebras/cerebras-GPT-6.7B",
397
+ "cerebras/cerebras-GPT-2.7B",
398
+ "cerebras/cerebras-GPT-1.3B",
399
+ "cerebras/cerebras-GPT-590M",
400
+ "cerebras/cerebras-GPT-256M",
401
+ "cerebras/cerebras-GPT-111M",
402
+ )
403
+ open_assistant_models_list = (
404
+ "OpenAssistant/codellama-13b-oasst-sft-v10",
405
+ "OpenAssistant/llama2-70b-oasst-sft-v10",
406
+ "OpenAssistant/llama2-13b-megacode2-oasst",
407
+ "OpenAssistant/falcon-40b-megacode2-oasst",
408
+ "OpenAssistant/pythia-12b-sft-v8-rlhf-2k-steps",
409
+ "OpenAssistant/llama2-13b-orca-8k-3319",
410
+ "OpenAssistant/falcon-7b-sft-mix-2000",
411
+ "OpenAssistant/falcon-7b-sft-top1-696",
412
+ "OpenAssistant/falcon-40b-sft-mix-1226",
413
+ "OpenAssistant/falcon-40b-sft-top1-560",
414
+ "OpenAssistant/pythia-12b-sft-v8-2.5k-steps",
415
+ "OpenAssistant/pythia-12b-sft-v8-7k-steps",
416
+ "OpenAssistant/pythia-12b-pre-v8-12.5k-steps",
417
+ "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
418
+ "OpenAssistant/stablelm-7b-sft-v7-epoch-3",
419
+ "OpenAssistant/oasst-sft-1-pythia-12b",
420
+ "OpenAssistant/galactica-6.7b-finetuned",
421
+ )
422
+
423
+ databricks_models_list = (
424
+
425
+ "databricks/dolly-v2-7b",
426
+ "databricks/dolly-v2-3b",
427
+ "databricks/dolly-v2-12b",
428
+ "databricks/dolly-v1-6b",
429
+ )
430
+
431
+ nomic_ai_models_list = (
432
+ "nomic-ai/gpt4all-falcon",
433
+ "nomic-ai/gpt4all-j",
434
+ "nomic-ai/gpt4all-mpt",
435
+ "nomic-ai/gpt4all-13b-snoozy",
436
+ "nomic-ai/gpt4all-j-lora",
437
+ )
438
+
439
+ blinkdl_models_list = (
440
+ "BlinkDL/rwkv-5-world",
441
+ "BlinkDL/rwkv-4-world",
442
+ "BlinkDL/rwkv-4-raven",
443
+ "BlinkDL/rwkv-4-pile-7b",
444
+ "BlinkDL/rwkv-4-pile-14b",
445
+ "BlinkDL/rwkv-4-novel",
446
+ "BlinkDL/rwkv-4-pileplus",
447
+ "BlinkDL/rwkv-4-pile-430m",
448
+ "BlinkDL/rwkv-4-pile-3b",
449
+ "BlinkDL/rwkv-4-pile-1b5",
450
+ "BlinkDL/rwkv-4-pile-169m",
451
+ "BlinkDL/rwkv-3-pile-1b5",
452
+ "BlinkDL/rwkv-3-pile-430m",
453
+ "BlinkDL/rwkv-2-pile-430m",
454
+ "BlinkDL/rwkv-3-pile-169m",
455
+ )
456
+
457
+ ai_squared_models_list = (
458
+ "aisquared/dlite-dais-2023",
459
+ "aisquared/chopt-1_3b",
460
+ "aisquared/chopt-350m",
461
+ "aisquared/chopt-125m",
462
+ "aisquared/chopt-2_7b",
463
+ "aisquared/dlite-v2-1_5b",
464
+ "aisquared/dlite-v2-774m",
465
+ "aisquared/dlite-v2-355m",
466
+ "aisquared/dlite-v2-124m",
467
+ "aisquared/dlite-v1-355m",
468
+ "aisquared/dlite-v1-774m",
469
+ "aisquared/dlite-v1-1_5b",
470
+ "aisquared/dlite-v1-124m",
471
+ "aisquared/chopt-research-350m",
472
+ "aisquared/chopt-research-125m",
473
+ "aisquared/chopt-research-2_7b",
474
+ "aisquared/chopt-research-1_3b",
475
+ )
476
+
477
+ bloom_models_list = (
478
+ "bigscience/bloom-3b-intermediate",
479
+ "bigscience/bloom",
480
+ "bigscience/bloomz-p3",
481
+ "bigscience/bloomz-mt",
482
+ "bigscience/bloomz-7b1-mt",
483
+ "bigscience/bloom-1b7-intermediate",
484
+ "bigscience/bloom-560m-intermediate",
485
+ "bigscience/bloomz-560m",
486
+ "bigscience/bloomz-1b1",
487
+ "bigscience/bloomz-1b7",
488
+ "bigscience/bloomz-3b",
489
+ "bigscience/bloomz-7b1",
490
+ 'bigscience/bloomz',
491
+ "bigscience/bloom-1b7",
492
+ "bigscience/bloom-560m",
493
+ "bigscience/bloom-3b",
494
+ "bigscience/bigscience-small-testing",
495
+ "bigscience/distill-bloom-1b3",
496
+ "bigscience/bloom-1b1",
497
+ "bigscience/distill-bloom-1b3-10x",
498
+ "bigscience/test-bloomd",
499
+ "bigscience/test-bloomd-6b3",
500
+ "bigscience/bloom-7b1",
501
+ "bigscience/bloom-petals",
502
+ "bigscience/bloom-1b1-intermediate",
503
+ "bigscience/bloom-7b1-intermediate",
504
+ "bigscience/bloom-7b1-petals",
505
+ "bigscience/bloomz-petals",
506
+ "bigscience/bloomz-7b1-p3",
507
+ )
508
+ tiiuae_models_list = (
509
+ "tiiuae/falcon-180B",
510
+ "tiiuae/falcon-180B-chat",
511
+ "tiiuae/falcon-40b",
512
+ "tiiuae/falcon-7b",
513
+ "tiiuae/falcon-7b-instruct",
514
+ "tiiuae/falcon-40b-instruct",
515
+ "tiiuae/falcon-rw-7b",
516
+ "tiiuae/falcon-rw-1b",
517
+ )
518
+
519
+ openlm_models_list = (
520
+ "openlm-research/open_llama_3b_v2",
521
+ "openlm-research/open_llama_7b_v2",
522
+ "openlm-research/open_llama_13b",
523
+ "openlm-research/open_llama_7b",
524
+ "openlm-research/open_llama_3b",
525
+ )
526
+ stabilityai_models_list = (
527
+ "stabilityai/StableBeluga-7B",
528
+ "stabilityai/StableBeluga-13B",
529
+ "stabilityai/StableBeluga2",
530
+ "stabilityai/stablelm-base-alpha-3b-v2",
531
+ "stabilityai/stablelm-base-alpha-7b-v2",
532
+ "stabilityai/japanese-stablelm-instruct-alpha-7b",
533
+ "stabilityai/japanese-stablelm-base-alpha-7b",
534
+ "stabilityai/stablecode-completion-alpha-3b-4k",
535
+ "stabilityai/stablecode-instruct-alpha-3b",
536
+ "stabilityai/stablecode-completion-alpha-3b",
537
+ "stabilityai/StableBeluga1-Delta",
538
+ "stabilityai/stablelm-base-alpha-3b",
539
+ "stabilityai/stablelm-base-alpha-7b",
540
+ "stabilityai/stablelm-tuned-alpha-3b",
541
+ "stabilityai/stablelm-tuned-alpha-7b",
542
+ )
543
+
544
+ lmsys_models_list = (
545
+ "lmsys/vicuna-13b-v1.5-16k",
546
+ "lmsys/vicuna-13b-v1.5",
547
+ "lmsys/vicuna-7b-v1.5-16k",
548
+ "lmsys/longchat-7b-v1.5-32k",
549
+ "lmsys/vicuna-7b-v1.5",
550
+ "lmsys/vicuna-7b-v1.3",
551
+ "lmsys/vicuna-13b-v1.3",
552
+ "lmsys/vicuna-7b-v1.1",
553
+ "lmsys/vicuna-13b-v1.1",
554
+ "lmsys/vicuna-13b-delta-v0",
555
+ "lmsys/vicuna-7b-delta-v0",
556
+ "lmsys/vicuna-13b-delta-v1.1",
557
+ "lmsys/vicuna-7b-delta-v1.1",
558
+ "lmsys/vicuna-33b-v1.3",
559
+ "lmsys/longchat-13b-16k",
560
+ 'lmsys/longchat-7b-16k',
561
+ )
562
+
563
+ togethercomputer_models_list = (
564
+ 'togethercomputer/Llama-2-7B-32K-Instruct',
565
+ 'togethercomputer/RedPajama-INCITE-7B-Instruct',
566
+ 'togethercomputer/LLaMA-2-7B-32K',
567
+ 'togethercomputer/RedPajama-INCITE-7B-Base',
568
+ 'togethercomputer/RedPajama-INCITE-7B-Chat',
569
+ 'togethercomputer/RedPajama-INCITE-Chat-3B-v1',
570
+ 'togethercomputer/RedPajama-INCITE-Instruct-3B-v1',
571
+ 'togethercomputer/RedPajama-INCITE-Base-3B-v1',
572
+ 'togethercomputer/GPT-NeoXT-Chat-Base-20B',
573
+ 'togethercomputer/Pythia-Chat-Base-7B',
574
+ 'togethercomputer/GPT-JT-Moderation-6B',
575
+ 'togethercomputer/GPT-JT-6B-v1',
576
+ 'togethercomputer/GPT-JT-6B-v0'
577
+ )
578
+ mosaic_models_list = (
579
+ 'mosaicml/mpt-7b-chat',
580
+ 'mosaicml/mpt-30b-chat',
581
+ 'mosaicml/mpt-7b-8k-chat',
582
+ 'mosaicml/mpt-7b-instruct',
583
+ 'mosaicml/mpt-7b-8k-instruct',
584
+ 'mosaicml/mpt-7b-8k',
585
+ 'mosaicml/mpt-30b',
586
+ 'mosaicml/mpt-7b',
587
+ 'mosaicml/mpt-7b-storywriter',
588
+ 'mosaicml/mpt-30b-instruct',
589
+ 'mosaicml/mpt-1b-redpajama-200b',
590
+ 'mosaicml/mpt-1b-redpajama-200b-dolly'
591
+ )
592
+
593
+ h20ai_models_list = (
594
+ 'h2oai/h2ogpt-16k-codellama-7b-python',
595
+ 'h2oai/h2ogpt-16k-codellama-7b-instruct',
596
+ 'h2oai/h2ogpt-16k-codellama-7b',
597
+ 'h2oai/h2ogpt-16k-codellama-34b-python',
598
+ 'h2oai/h2ogpt-16k-codellama-34b-instruct',
599
+ 'h2oai/h2ogpt-16k-codellama-13b-python',
600
+ 'h2oai/h2ogpt-16k-codellama-13b-instruct',
601
+ 'h2oai/h2ogpt-16k-codellama-13b',
602
+ 'h2oai/h2ogpt-16k-codellama-34b',
603
+ 'h2oai/h2ogpt-4096-llama2-13b-chat',
604
+ 'h2oai/h2ogpt-4096-llama2-70b-chat',
605
+ 'h2oai/h2ogpt-4096-llama2-7b-chat',
606
+ 'h2oai/h2ogpt-4096-llama2-13b',
607
+ 'h2oai/h2ogpt-4096-llama2-7b',
608
+ 'h2oai/h2ogpt-4096-llama2-70b',
609
+ 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2',
610
+ 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3',
611
+ 'h2oai/h2ogpt-research-oasst1-llama-65b',
612
+ 'h2oai/h2ogpt-gm-oasst1-en-xgen-7b-8k',
613
+ 'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-3b',
614
+ 'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b',
615
+ 'h2oai/h2ogpt-oasst1-falcon-40b',
616
+ 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1',
617
+ 'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-13b',
618
+ 'h2oai/h2ogpt-oig-oasst1-falcon-40b',
619
+ 'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b',
620
+ 'h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b',
621
+ 'h2oai/h2ogpt-oasst1-512-12b',
622
+ 'h2oai/h2ogpt-oig-oasst1-256-6_9b',
623
+ 'h2oai/h2ogpt-oig-oasst1-512-6_9b',
624
+ 'h2oai/h2ogpt-research-oig-oasst1-512-30b',
625
+ 'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-700bt',
626
+ 'h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt',
627
+ 'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2',
628
+ 'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt',
629
+ 'h2oai/h2ogpt-gm-oasst1-en-1024-12b',
630
+ 'h2oai/h2ogpt-gm-oasst1-en-1024-20b',
631
+ 'h2oai/h2ogpt-gm-oasst1-multilang-1024-20b',
632
+ 'h2oai/h2ogpt-oasst1-512-20b'
633
+ )
634
+
635
+ facebook_models_list = (
636
+ 'facebook/xglm-4.5B',
637
+ 'facebook/galactica-125m',
638
+ 'facebook/opt-iml-1.3b',
639
+ 'facebook/opt-iml-max-1.3b',
640
+ 'facebook/opt-iml-max-30b',
641
+ 'facebook/opt-iml-30b',
642
+ 'facebook/galactica-120b',
643
+ 'facebook/galactica-30b',
644
+ 'facebook/galactica-6.7b',
645
+ 'facebook/galactica-1.3b',
646
+ 'facebook/opt-66b',
647
+ 'facebook/opt-30b',
648
+ 'facebook/opt-13b',
649
+ 'facebook/opt-6.7b',
650
+ 'facebook/opt-2.7b',
651
+ 'facebook/opt-1.3b',
652
+ 'facebook/opt-350m',
653
+ 'facebook/opt-125m',
654
+ 'facebook/incoder-1B',
655
+ 'facebook/incoder-6B',
656
+ 'facebook/xglm-7.5B',
657
+ 'facebook/xglm-564M',
658
+ 'facebook/xglm-2.9B',
659
+ 'facebook/xglm-1.7B'
660
+ )
661
+
662
+ model_dict = {
663
+ "google": google_models_list,
664
+ "mbzuai": mbzuai_models_list,
665
+ "eleutherai": eleutherai_models_list,
666
+ "cerebras": cerebras_models_list,
667
+ "open_assistant": open_assistant_models_list,
668
+ "databricks": databricks_models_list,
669
+ "nomic_ai": nomic_ai_models_list,
670
+ "blinkdl": blinkdl_models_list,
671
+ "aisquared": ai_squared_models_list,
672
+ "bloom": bloom_models_list,
673
+ "tiiuae": tiiuae_models_list,
674
+ "openlm": openlm_models_list,
675
+ "stabilityai": stabilityai_models_list,
676
+ "lmsys": lmsys_models_list,
677
+ "together_computer": togethercomputer_models_list,
678
+ "mosaic_ml": mosaic_models_list,
679
+ "h20ai": h20ai_models_list,
680
+ "facebook": facebook_models_list
681
+ }
682
+
683
+ if count is True:
684
+ models_count = 0
685
+ for i in model_dict:
686
+ models_count += len(model_dict[i])
687
+ return models_count
688
+
689
+ return model_dict[model_parent]
690
+