-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathindex.html
More file actions
2523 lines (2239 loc) · 104 KB
/
index.html
File metadata and controls
2523 lines (2239 loc) · 104 KB
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
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta
name="description"
content="A practical ablation study: GRPO-style reinforcement learning with LoRA on Arcee Trinity Mini for DrugProt drug-protein relation extraction. We sweep LoRA alpha, learning rate, batch size, max tokens, temperature, and rollout budget to find what actually moves the needle."
/>
<meta property="og:type" content="article" />
<meta property="og:title" content="Post-Training an Open MoE Model to Extract Drug-Protein Relations: Trinity-Mini-DrugProt-Think" />
<meta property="og:description" content="A practical ablation study: GRPO-style reinforcement learning with LoRA on Arcee Trinity Mini for DrugProt drug-protein relation extraction. We sweep LoRA alpha, learning rate, batch size, max tokens, temperature, and rollout budget to find what actually moves the needle." />
<meta property="og:image" content="https://raw.githubusercontent.com/LokaHQ/Trinity-Mini-DrugProt-Think/main/assets/logo.png" />
<meta property="og:image:alt" content="TRINITY: Trinity-Mini-DrugProt-Think cover image." />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:title" content="Post-Training an Open MoE Model to Extract Drug-Protein Relations: Trinity-Mini-DrugProt-Think" />
<meta name="twitter:description" content="A practical ablation study: GRPO-style reinforcement learning with LoRA on Arcee Trinity Mini for DrugProt drug-protein relation extraction. We sweep LoRA alpha, learning rate, batch size, max tokens, temperature, and rollout budget to find what actually moves the needle." />
<meta name="twitter:image" content="https://raw.githubusercontent.com/LokaHQ/Trinity-Mini-DrugProt-Think/main/assets/logo.png" />
<title>Post-Training an Open MoE Model to Extract Drug-Protein Relations: Trinity-Mini-DrugProt-Think</title>
<link rel="icon" type="image/svg+xml" href="https://cdn.prod.website-files.com/6490383845d4c0f51f929ca8/649052c9d3731fb704eea658_favicon.svg" />
<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.7/dist/chart.umd.min.js"></script>
<style>
@font-face {
font-family: "Iowan Old Style BT";
src: url("https://thinkingmachines.ai/fonts/iowan-old-style/iowan-old-style_regular.woff2")
format("woff2");
font-style: normal;
font-weight: 400;
font-display: swap;
}
@font-face {
font-family: "Iowan Old Style BT";
src: url("https://thinkingmachines.ai/fonts/iowan-old-style/iowan-old-style_italic.woff2")
format("woff2");
font-style: italic;
font-weight: 400;
font-display: swap;
}
@font-face {
font-family: "Iowan Old Style BT";
src: url("https://thinkingmachines.ai/fonts/iowan-old-style/iowan-old-style_bold.woff2")
format("woff2");
font-style: normal;
font-weight: 600;
font-display: swap;
}
@font-face {
font-family: "GT America";
src: url("https://thinkingmachines.ai/fonts/gt-america/GT-America-Standard-Regular.woff2")
format("woff2");
font-style: normal;
font-weight: 400;
font-display: swap;
}
@font-face {
font-family: "GT America";
src: url("https://thinkingmachines.ai/fonts/gt-america/GT-America-Standard-Medium.woff2")
format("woff2");
font-style: normal;
font-weight: 500;
font-display: swap;
}
:root {
--bg: #fff;
--bg-soft: #f7f7f7;
--bg-code: #f4f4f3;
--fg: #1a1a1a;
--fg-2: #333;
--fg-3: #666;
--fg-4: #999;
--line: #e5e5e5;
--line-strong: #d0d0d0;
--accent: #1877f2;
--link: #1877f2;
--max-content: 680px;
--max-wide: 900px;
--toc-width: 200px;
--toc-gap: 40px;
--font-serif: "Iowan Old Style BT", Georgia, "Times New Roman", serif;
--font-sans: "GT America", -apple-system, "Segoe UI", "Helvetica Neue",
Helvetica, sans-serif;
--font-mono: "SF Mono", "Fira Code", "Fira Mono", "Roboto Mono",
Consolas, monospace;
--c-blue: #1877f2;
--c-red: #ef4444;
--c-green: #22c55e;
--c-amber: #f59e0b;
--c-purple: #8b5cf6;
--c-teal: #14b8a6;
--c-gray: #6b7280;
}
*,
*::before,
*::after {
box-sizing: border-box;
}
html {
scroll-behavior: smooth;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
body {
margin: 0;
background: var(--bg);
color: var(--fg);
font-family: var(--font-serif);
font-size: 17px;
line-height: 1.7;
}
a {
color: var(--link);
}
/* ── Reading progress ── */
.reading-progress {
position: fixed;
top: 0;
left: 0;
width: 0%;
height: 2px;
background: var(--accent);
z-index: 1000;
transition: width 0.1s linear;
}
/* ── Layout ── */
.shell {
min-height: 100vh;
}
#main.post {
max-width: var(--max-wide);
margin: 0 auto;
padding: 0 1.6rem 4rem;
position: relative;
}
/* ── Header ── */
.post-heading {
text-align: center;
max-width: var(--max-content);
margin: 3.5rem auto 0;
}
.post-title {
margin: 0;
font-size: 2.4rem;
line-height: 1.2;
font-weight: 600;
letter-spacing: -0.02em;
}
.post-title-main {
display: block;
}
.post-title-sub {
display: block;
margin-top: 0.35rem;
font-size: 0.92em;
font-weight: 600;
color: var(--fg-2);
}
.authors {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 0.5rem 2rem;
margin: 1.6rem auto 0;
max-width: 420px;
text-align: center;
font-family: var(--font-sans);
}
.author-card {
display: flex;
flex-direction: column;
align-items: center;
gap: 0.1rem;
}
.author-name {
font-size: 1rem;
font-weight: 500;
color: var(--fg);
}
.author-name a {
color: var(--fg);
text-decoration: none;
border-bottom: 1px solid var(--line);
}
.author-name a:hover {
border-bottom-color: var(--fg);
}
.author-email {
font-size: 0.82rem;
color: var(--fg-3);
}
.author-email a {
color: var(--fg-3);
text-decoration: none;
}
.author-email a:hover {
color: var(--accent);
}
.author-affiliation {
margin: 1rem 0 0;
text-align: center;
}
.author-affiliation .loka-logo {
height: 2rem;
vertical-align: middle;
}
.post-date {
margin: 0.8rem 0 0;
font-family: var(--font-sans);
font-size: 0.88rem;
color: var(--fg-4);
}
.resource-links {
margin: 1rem 0 0;
padding: 0;
list-style: none;
display: flex;
justify-content: center;
gap: 1.5rem;
font-family: var(--font-sans);
font-size: 0.88rem;
}
.resource-links a {
color: var(--fg-3);
text-decoration: none;
border-bottom: none;
display: inline-flex;
align-items: center;
gap: 0.35rem;
}
.resource-links a:hover {
color: var(--fg);
}
.resource-links .icon {
display: inline-block;
width: 2rem;
height: 2rem;
}
.resource-links img.icon {
width: auto;
object-fit: contain;
}
/* ── Cover ── */
.post-cover {
max-width: var(--max-wide);
margin: 2.2rem auto 2.5rem;
}
.post-cover img {
width: 100%;
height: auto;
display: block;
border-radius: 24px;
}
/* ── Article ── */
article.content {
max-width: var(--max-content);
margin: 0 auto;
position: relative;
}
/* ── Callout ── */
.callout {
border-left: 3px solid var(--accent);
padding: 0.8rem 1.1rem;
margin-bottom: 1.3rem;
background: var(--bg-soft);
}
.callout strong {
font-family: var(--font-sans);
font-weight: 500;
}
.callout p:first-child {
margin-top: 0;
}
.callout p:last-child,
.callout ul:last-child {
margin-bottom: 0;
}
.callout ul {
margin-left: 1.2rem;
}
.callout li {
margin-bottom: 0.3rem;
}
/* ── Equations ── */
.equation {
margin: 0.9rem 0 1.1rem;
padding: 0.65rem 1rem;
background: var(--bg-soft);
border: 1px solid var(--line);
border-radius: 12px;
text-align: center;
}
.equation .equation-inner {
display: inline-block;
font-size: 1.05em;
}
/* ── Wide figures ── */
.figure-wide {
max-width: var(--max-wide);
margin-left: calc((var(--max-content) - var(--max-wide)) / 2);
margin-right: calc((var(--max-content) - var(--max-wide)) / 2);
}
.figure-wide canvas {
width: 100% !important;
height: auto !important;
}
/* ── Left TOC ── */
.left-toc {
position: sticky;
top: 2rem;
float: left;
margin-left: calc(-1 * var(--toc-width) - var(--toc-gap));
width: var(--toc-width);
font-size: 0.85rem;
line-height: 1.45;
max-height: calc(100vh - 4rem);
overflow-y: auto;
}
.toc-title {
margin: 0 0 0.5rem;
font-family: var(--font-sans);
font-size: 0.7rem;
letter-spacing: 0.1em;
text-transform: uppercase;
color: var(--fg-4);
font-weight: 500;
}
.toc-list {
margin: 0;
padding: 0;
list-style: none;
border-left: 1px solid var(--line);
}
.toc-list li {
margin: 0;
}
.toc-list a {
display: block;
padding: 0.2rem 0 0.2rem 0.85rem;
text-decoration: none;
color: var(--fg-4);
border-left: 2px solid transparent;
margin-left: -1px;
transition: color 0.15s ease, border-color 0.15s ease;
}
.toc-list a:hover {
color: var(--fg-2);
}
.toc-list a.active {
color: var(--accent);
border-left-color: var(--accent);
font-weight: 500;
}
/* ── Content typography ── */
.content p,
.content ul,
.content ol,
.content pre,
.content blockquote,
.content table,
.content figure {
margin: 0 0 1.3rem;
}
.content h2,
.content h3,
.content h4 {
line-height: 1.3;
position: relative;
}
.content h2 {
margin: 2.8rem 0 0.9rem;
font-size: 1.5rem;
font-weight: 600;
letter-spacing: -0.01em;
}
.content h3 {
margin: 2rem 0 0.7rem;
font-size: 1.15rem;
font-style: italic;
font-weight: 400;
color: var(--fg-2);
}
.content h4 {
margin: 1.6rem 0 0.5rem;
font-size: 1.05rem;
font-style: italic;
font-weight: 400;
color: var(--fg-2);
}
.heading-anchor {
position: absolute;
left: -1.2rem;
top: 50%;
transform: translateY(-50%);
opacity: 0;
text-decoration: none;
color: var(--fg-4);
font-weight: 400;
font-size: 0.85em;
transition: opacity 0.15s ease;
}
.content h2:hover .heading-anchor,
.content h3:hover .heading-anchor,
.content h4:hover .heading-anchor,
.heading-anchor:focus {
opacity: 1;
}
.content ul {
margin-left: 1.5rem;
padding: 0;
list-style: disc;
}
.content ol {
margin-left: 1.7rem;
}
.content li {
margin-bottom: 0.25rem;
}
.content a {
text-decoration-color: var(--line-strong);
text-underline-offset: 2px;
transition: text-decoration-color 0.15s ease;
}
.content a:hover {
text-decoration-color: var(--fg);
}
.content blockquote {
margin-left: 0;
padding: 0.5rem 1.1rem;
border-left: 3px solid var(--line-strong);
color: var(--fg-2);
}
.content code {
font-family: var(--font-mono);
font-size: 0.84em;
background: var(--bg-code);
padding: 0.1rem 0.3rem;
border-radius: 3px;
}
.content pre {
background: var(--bg-code);
border-radius: 4px;
overflow-x: auto;
padding: 0.9rem 1rem;
border: 1px solid var(--line);
}
.content pre code {
padding: 0;
background: none;
display: block;
font-size: 0.8rem;
line-height: 1.5;
border-radius: 0;
}
/* ── Tables ── */
.content table {
width: 100%;
border-collapse: collapse;
font-size: 0.9rem;
}
.content th,
.content td {
padding: 0.5rem 0.65rem;
text-align: left;
vertical-align: top;
border-bottom: 1px solid var(--line);
}
.content th {
font-family: var(--font-sans);
font-weight: 500;
font-size: 0.82rem;
color: var(--fg-3);
border-bottom: 2px solid var(--line-strong);
}
.content tbody tr:last-child td {
border-bottom: none;
}
.content td code {
font-size: 0.8rem;
}
/* ── Figures ── */
.content figure {
margin-top: 1.6rem;
}
.content figure img,
.content figure svg {
display: block;
width: 100%;
height: auto;
}
.content figcaption {
margin-top: 0.5rem;
font-size: 0.88rem;
color: var(--fg-3);
font-style: italic;
text-align: center;
}
/* ── Citation ── */
.citation-block {
margin-top: 0.5rem;
}
.citation-block pre {
background: var(--bg-soft);
border: 1px solid var(--line);
border-radius: 4px;
padding: 0.8rem 1rem;
font-size: 0.78rem;
line-height: 1.5;
overflow-x: auto;
}
.citation-block pre code {
font-family: var(--font-mono);
background: none;
padding: 0;
color: var(--fg-2);
}
/* ── References ── */
.references-list {
margin-left: 1.4rem;
padding: 0;
list-style: decimal;
}
.references-list li {
margin-bottom: 1rem;
font-size: 0.9rem;
line-height: 1.65;
color: var(--fg-2);
}
.references-list li a {
display: inline-block;
margin-top: 0.1rem;
font-family: var(--font-sans);
font-size: 0.82rem;
color: var(--fg-3);
}
/* ── Paginator ── */
.paginator {
max-width: var(--max-content);
margin: 2.5rem auto 0;
padding-top: 1.2rem;
border-top: 1px solid var(--line);
display: flex;
justify-content: center;
gap: 2rem;
font-family: var(--font-sans);
font-size: 0.88rem;
}
.paginator a {
text-decoration: none;
color: var(--fg-3);
transition: color 0.15s ease;
}
.paginator a:hover {
color: var(--fg);
}
/* ── Back to top ── */
.back-to-top {
position: fixed;
bottom: 2rem;
right: 2rem;
width: 36px;
height: 36px;
border-radius: 50%;
background: var(--bg);
color: var(--fg-3);
border: 1px solid var(--line);
cursor: pointer;
display: flex;
align-items: center;
justify-content: center;
opacity: 0;
transform: translateY(8px);
transition: opacity 0.25s ease, transform 0.25s ease,
color 0.15s ease, border-color 0.15s ease;
z-index: 100;
font-size: 16px;
line-height: 1;
}
.back-to-top.visible {
opacity: 1;
transform: translateY(0);
}
.back-to-top:hover {
color: var(--accent);
border-color: var(--accent);
}
/* ── Loka logo ── */
.loka-logo {
display: inline-block;
height: 1.15em;
width: auto;
vertical-align: -0.15em;
margin-right: 0.15em;
}
.site-footer .loka-logo {
height: 1em;
vertical-align: -0.1em;
}
/* ── Footer ── */
.site-footer {
max-width: var(--max-content);
margin: 0 auto;
padding: 1.5rem 1.6rem 2rem;
font-family: var(--font-sans);
font-size: 0.82rem;
color: var(--fg-4);
text-align: center;
border-top: 1px solid var(--line);
}
.site-footer a {
color: var(--fg-3);
text-decoration: none;
}
.site-footer a:hover {
color: var(--fg);
}
/* ── Output comparison ── */
.output-comparison {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 1rem;
margin: 1.3rem 0;
}
.output-box {
border: 1px solid var(--line);
border-radius: 6px;
overflow: hidden;
}
.output-box-header {
font-family: var(--font-sans);
font-size: 0.78rem;
font-weight: 500;
padding: 0.5rem 0.8rem;
border-bottom: 1px solid var(--line);
background: var(--bg-soft);
}
.output-box-header.good {
color: #166534;
background: #f0fdf4;
border-bottom-color: #bbf7d0;
}
.output-box-header.bad {
color: #991b1b;
background: #fef2f2;
border-bottom-color: #fecaca;
}
.output-box-body {
padding: 0.8rem;
font-size: 0.82rem;
line-height: 1.55;
max-height: 300px;
overflow-y: auto;
}
.output-box-body code {
font-size: 0.78rem;
}
/* ── Responsive ── */
@media (max-width: 1100px) {
.left-toc {
display: none;
}
.heading-anchor {
display: none;
}
.figure-wide {
max-width: var(--max-content);
margin-left: 0;
margin-right: 0;
}
}
@media (max-width: 768px) {
body {
font-size: 16px;
}
#main.post {
padding: 0 1.1rem 3rem;
}
.post-title {
font-size: 1.7rem;
}
.byline {
font-size: 0.92rem;
}
.resource-links {
gap: 1rem;
font-size: 0.82rem;
}
.content h2 {
font-size: 1.3rem;
}
.callout {
padding: 0.7rem 0.9rem;
}
.content table {
font-size: 0.8rem;
}
.content th,
.content td {
padding: 0.4rem 0.45rem;
}
.back-to-top {
bottom: 1.2rem;
right: 1.2rem;
}
.output-comparison {
grid-template-columns: 1fr;
}
}
/* ── Print ── */
@media print {
.reading-progress,
.left-toc,
.back-to-top,
.paginator {
display: none !important;
}
body {
font-size: 11pt;
}
.figure-wide {
max-width: 100%;
margin-left: 0;
margin-right: 0;
}
a {
color: inherit;
}
}
</style>
</head>
<body>
<div class="reading-progress" id="reading-progress"></div>
<a id="top"></a>
<div class="shell">
<main id="main" class="post">
<section class="post-heading">
<h1 class="post-title">
<span class="post-title-main">Post-Training an Open MoE Model to Extract Drug-Protein Relations:</span>
<span class="post-title-sub">Trinity-Mini-DrugProt-Think</span>
</h1>
<div class="authors">
<div class="author-card">
<span class="author-name"><a href="https://github.com/Shekswess">Bojan Jakimovski</a></span>
<span class="author-email"><a href="mailto:bojan.jakimovski@loka.com">bojan.jakimovski@loka.com</a></span>
</div>
<div class="author-card">
<span class="author-name"><a href="https://github.com/petarkalinovski">Petar Kalinovski</a></span>
<span class="author-email"><a href="mailto:petar.kalinovski@loka.com">petar.kalinovski@loka.com</a></span>
</div>
</div>
<div class="author-affiliation">
<a href="https://www.loka.com/" aria-label="Loka">
<img src="https://cdn.worldvectorlogo.com/logos/loka-1.svg" alt="Loka" class="loka-logo" />
</a>
</div>
<p class="post-date">
<time datetime="2026-02-23">February 23, 2026</time>
</p>
<ul class="resource-links">
<li><a href="https://github.com/LokaHQ/Trinity-Mini-DrugProt-Think" aria-label="GitHub"><svg class="icon" viewBox="0 0 16 16" fill="currentColor"><path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27s1.36.09 2 .27c1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.01 8.01 0 0 0 16 8c0-4.42-3.58-8-8-8z"/></svg></a></li>
<li><a href="https://huggingface.co/lokahq/Trinity-Mini-DrugProt-Think" aria-label="HuggingFace"><img class="icon" src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="HuggingFace" /></a></li>
<li><a href="https://medium.com/loka-engineering/deploying-trinity-mini-drugprot-think-on-amazon-sagemaker-ai-9e1c1c430ce9" aria-label="Deployment guide" title="Deployment guide"><img class="icon" src="https://www.sysgroup.com/wp-content/uploads/2025/02/Amazon_Web_Services-Logo.wine_.png" alt="AWS deployment guide" /></a></li>
</ul>
</section>
<figure class="post-cover">
<img
src="assets/logo.png"
alt="Cover image: TRINITY, Trinity-Mini-DrugProt-Think."
/>
</figure>
<article class="content">
<nav class="left-toc" aria-label="Table of contents">
<ul class="toc-list">
<li><a href="#introduction">Introduction</a></li>
<li><a href="#task-model">The task & the model</a></li>
<li><a href="#experiments">Experiments & results</a></li>
<li><a href="#findings">Key findings</a></li>
<li><a href="#next-steps">Next steps</a></li>
<li><a href="#conclusion">Conclusion</a></li>
<li><a href="#acknowledgements">Acknowledgements</a></li>
<li><a href="#citation">Citation</a></li>
<li><a href="#references">Further reading</a></li>
</ul>
</nav>
<!-- ═══════════════════ 1. INTRODUCTION ═══════════════════ -->
<h2 id="introduction">
Introduction
<a class="heading-anchor" href="#introduction" aria-label="Link to section">#</a>
</h2>
<p>
In the past two years,
Chinese labs have come to dominate open-weight model development in a way that
few in the West anticipated.
<a href="https://www.technologyreview.com/2026/02/12/1132811/whats-next-for-chinese-open-source-ai/">Alibaba’s
Qwen family has overtaken Meta’s Llama in cumulative Hugging Face downloads</a>,
and by mid-2025 Qwen-derived models accounted for over 40% of new language model
uploads on the platform. <a href="https://arxiv.org/abs/2501.12948">DeepSeek R1</a>
briefly narrowed the frontier gap to
<a href="https://epoch.ai/data-insights/us-vs-china-eci">as little as four months</a>.
<a href="https://huggingface.co/zai-org/GLM-5">GLM-5</a>,
<a href="https://github.com/MoonshotAI/Kimi-K2.5">Kimi K2.5</a>, and
<a href="https://huggingface.co/MiniMaxAI/MiniMax-M2.5">MiniMax M2.5</a>, all
released as open weights under permissive licenses in early 2026, are
<a href="https://www.latent.space/p/ainews-zai-glm-5-new-sota-open-weights">frontier-adjacent
at a fraction of the inference cost</a>. Despite hardware constraints that leave
Chinese labs working around export controls on cutting-edge chips, they have
executed a remarkably effective strategy: flood the world with competitive
open-weight models and let the ecosystem do the rest.
</p>
<p>
Western open-source, by contrast, is thin.
AI2 ships <a href="https://arxiv.org/abs/2512.13961">OLMo3</a>,
Hugging Face has <a href="https://huggingface.co/blog/smollm3">SmolLM3</a>,
and Mistral has <a href="https://mistral.ai/">its lineup</a>.
Beyond that, the list of Western labs releasing serious open-weight
foundation models is short. This is a strange asymmetry: Western labs hold a
<a href="https://techblog.comsoc.org/2026/01/27/chinas-open-source-ai-models-to-capture-a-larger-share-of-2026-global-ai-market/">nearly
tenfold advantage in compute infrastructure</a> (U.S. hyperscaler capex alone
exceeded $400 billion in 2025, versus roughly $57 billion across
China’s major platforms), yet produce far fewer open-weight models. China
is not winning open-source because of a technical edge in hardware. They are
winning because almost nobody in the West is trying.
</p>
<p>
That matters because open-weight models are not just a convenience; they are
infrastructure. Researchers, startups, and institutions that cannot afford
per-token API pricing or cannot send sensitive data to a third-party endpoint
depend on models they can run locally. If the only competitive options come from
Chinese labs, that shapes who builds what, on whose stack, and on whose terms.
</p>
<p>
<a href="https://www.arcee.ai">Arcee AI</a> is one of the Western labs taking
this challenge seriously, and the name of their model family gives away exactly
how: <strong>Trinity</strong> is a three-way collaboration.
Arcee AI led the model architecture and training.
<a href="https://www.primeintellect.ai">Prime Intellect</a> contributed their
distributed training infrastructure, a platform that lets researchers train large models
across decentralised GPU clusters without needing a hyperscaler budget.
<a href="https://datalogy.ai">Datalogy</a> curated the training data.
Together they released a full family: <strong>Trinity Nano</strong> for
edge and on-device deployment, <strong>Trinity Mini</strong> as the practical mid-range option, and
<strong>Trinity Large</strong> at the frontier scale, competitive with
<a href="https://www.arcee.ai/blog/trinity-large">GLM-4.5 base-to-base</a>.
All three are fully open-weight under a permissive license. We use Trinity Mini
for this study because its sparse MoE keeps inference cost low even when generating
thousands of rollouts per step, which is exactly what RLVR post-training demands.
</p>
<p>
The open-weight advantage is not just about what a model scores at release; it is about
what you can build on top of it. Targeted RLVR post-training can unlock domain-specific
capabilities that general benchmarks do not capture, and that is where open-source models
have real room to close the gap. The question we set out to answer is simple:
<strong>Can domain-specific RLVR post-training turn Trinity Mini into a meaningfully better
model for a real workload?</strong>
</p>
<p>
The task we chose is biomedical relation extraction. At
<a href="https://loka.com">Loka</a>, healthcare and life sciences projects make
up a significant part of our client work, which is part of why this domain
appealed to us. Drug discovery, clinical decision support, and pharmacovigilance
all depend on extracting structured knowledge from unstructured text. PubMed
indexes over 36 million citations, and the relationships buried in those
abstracts (which drug inhibits which protein, which compound is a substrate for
which enzyme) are the raw material for computational pharmacology.
<a href="https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/">DrugProt</a>
(BioCreative VII) is a benchmark for exactly this: 13 types of drug-protein
interactions, annotated on real PubMed abstracts.
</p>
<p>
The method is <strong>Reinforcement Learning from Verifiable Rewards
(RLVR)</strong>: rather than training a separate reward model, the environment
provides a ground-truth accuracy signal directly (correct label = 1, wrong = 0).
<a href="https://arxiv.org/abs/2402.03300">GRPO (Group Relative Policy
Optimization)</a> makes this tractable by estimating policy gradients from groups of sampled