23\ Info - pages - a 21world Collection

21world 's Collections

45\ AI SIMULATOR

44\ 24 layers(pcb) X 2048(?h_dim?) neurons [*_________]

43\ in text/static.video -> out static.video - LCM - MOD

42\ in static.video -> out text

41\ IN TEXT -> OUT AUDIO

40\ IN TEXT -> OUT TEXT , language translation.

39\ UPSCALE RESOLUTION

38\ in text,static video -> out static video |for cpu usage

37\ Sd model weights (ai data logic for static.video gen..)

36\ in text -> out speech

35\ in speech -> out text

34\ speech <-> text translate universal

32\ video creation

31\ LLM Best Models

30\ Interesting.what is this ? how it works?

29\ OK

28\ 48 layers X 4096 (?dim?)neurons [*****_____]

27\ 96 layers X 8192 (?dim?)neurons [*******___]

26\ 32 layers X 2560 (?dim?)neurons [__________]

25\ 48 layers X 8192 (?dim?)neurons [******____]

24\ 32 layers X 4096 (?dim?)neurons [*****_____]

23\ Info - pages

21\ 60 layers X 7168 (?dim?)neurons [******____]

20\ 80 layers X 8192 (?dim?)neurons [*******___]

19\ 22 layers X 2048(?h_dim?) neurons [*_________]

18\ other models

17\ ABSOLUT Perfect Bulgarian !

16\ Strange thinking

15\ 48 layers X 4096 neurons [*_________]

14\ 40 layers X 6144 neurons [*_________]

13\ 40 layers X 6144 neurons [***_______]

12\ 48 layers X 4096 neurons [**________]

11\ 32 layers X 2560 neurons [***_______]

10\ 32 layers X 3072 neurons [***_______]

9\ 32 layers X 4096 neurons [******____]

8\ 27 layers X 2048 neurons [*******___]

7\ Video observers

6\ 32 layers X 4096 neurons [*****_____]

4\ 2d to 3d - Video

3\ 2D to 3D CAD

2\ ?2 layers X ?2048 neurons X?? boards 8/64ex.[***'______]

23\ Info - pages

updated about 2 hours ago

1. TinyGSM: achieving >80% on GSM8k with small language models

TinyGSM: achieving >80% on GSM8k with small language models

Paper • 2312.09241 • Published Dec 14, 2023 • 37

Note 2. ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Paper • 2403.03853 • Published Mar 6 • 62

Note 3. Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction
Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction

Paper • 2403.18795 • Published Mar 27 • 18

Note 4. Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models
Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models

Paper • 2404.04478 • Published Apr 6 • 12

Note 5. Rho-1: Not All Tokens Are What You Need
Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 84

Note 6. Universal Guidance for Diffusion Models
Universal Guidance for Diffusion Models

Paper • 2302.07121 • Published Feb 14, 2023

Note 7. 2BP: 2-Stage Backpropagation
2BP: 2-Stage Backpropagation

Paper • 2405.18047 • Published May 28 • 23

Note 8. LinFusion: 1 GPU, 1 Minute, 16K Image
LinFusion: 1 GPU, 1 Minute, 16K Image

Paper • 2409.02097 • Published Sep 3 • 32

Note 9. LVCD: Reference-based Lineart Video Colorization with Diffusion Models
LVCD: Reference-based Lineart Video Colorization with Diffusion Models

Paper • 2409.12960 • Published Sep 19 • 22

Note 10. GRIN: GRadient-INformed MoE
GRIN: GRadient-INformed MoE

Paper • 2409.12136 • Published Sep 18 • 15

Note 11. Addition is All You Need for Energy-efficient Language Models
Addition is All You Need for Energy-efficient Language Models

Paper • 2410.00907 • Published Oct 1 • 144

Note 12. Reinforcement Learning Textbook
Reinforcement Learning Textbook

Paper • 2201.09746 • Published Jan 19, 2022