Apollo is a new family of open-source video language models by Meta, where 3B model outperforms most 7B models and 7B outperforms most 30B models 🧶 
text
Apollo is a new family of open-source video language models by Meta, where 3B model outperforms most 7B models and 7B outperforms most 30B models 🧶 
new_line
new_line
✨ the models come in 1.5B 
text
✨ the models come in 1.5B 
https://huggingface.co/Apollo-LMMs/Apollo-1_5B-t32
https://huggingface.co/Apollo-LMMs/Apollo-1_5B-t32
link
, 3B 
text
, 3B 
https://huggingface.co/Apollo-LMMs/Apollo-3B-t32
https://huggingface.co/Apollo-LMMs/Apollo-3B-t32
link
 and 7B 
text
 and 7B 
https://huggingface.co/Apollo-LMMs/Apollo-7B-t32
https://huggingface.co/Apollo-LMMs/Apollo-7B-t32
link
 with A2.0 license, based on Qwen1.5 & Qwen2 
text
 with A2.0 license, based on Qwen1.5 & Qwen2 
new_line
✨ the authors also release a benchmark dataset 
text
✨ the authors also release a benchmark dataset 
https://huggingface.co/spaces/Apollo-LMMs/ApolloBench
https://huggingface.co/spaces/Apollo-LMMs/ApolloBench
link
new_line
new_line
The paper has a lot of experiments (they trained 84 models!) about what makes the video LMs work ⏯️
text
The paper has a lot of experiments (they trained 84 models!) about what makes the video LMs work ⏯️
new_line
new_line
Try the demo for best setup here 
text
Try the demo for best setup here 
https://huggingface.co/spaces/Apollo-LMMs/Apollo-3B
https://huggingface.co/spaces/Apollo-LMMs/Apollo-3B
link
new_line
they evaluate sampling strategies, scaling laws for models and datasets, video representation and more!
text
they evaluate sampling strategies, scaling laws for models and datasets, video representation and more!
new_line
> The authors find out that whatever design decision was applied to small models also scale properly when the model and dataset are scaled 📈 scaling dataset has diminishing returns for smaller models
text
> The authors find out that whatever design decision was applied to small models also scale properly when the model and dataset are scaled 📈 scaling dataset has diminishing returns for smaller models
new_line
> They evaluate frame sampling strategies, and find that FPS sampling is better than uniform sampling, and they find 8-32 tokens per frame optimal 
text
> They evaluate frame sampling strategies, and find that FPS sampling is better than uniform sampling, and they find 8-32 tokens per frame optimal 
new_line
> They also compare image encoders, they try a variation of models from shape optimized SigLIP to DINOv2
text
> They also compare image encoders, they try a variation of models from shape optimized SigLIP to DINOv2
new_line
they find 
text
they find 
https://huggingface.co/google/siglip-so400m-patch14-384
google/siglip-so400m-patch14-384
model
resource
https://huggingface.co/google/siglip-so400m-patch14-384
 to be most powerful 🔥
text
 to be most powerful 🔥
new_line
> they also compare freezing different parts of models, training all stages with some frozen parts give the best yield
text
> they also compare freezing different parts of models, training all stages with some frozen parts give the best yield
new_line
new_line
They eventually release three models, where Apollo-3B outperforms most 7B models and Apollo 7B outperforms 30B models 🔥  
text
They eventually release three models, where Apollo-3B outperforms most 7B models and Apollo 7B outperforms 30B models 🔥  
Apollo is a new family of open-source video language models by Meta, where 3B model outperforms most 7B models and 7B outperforms most 30B models 🧶 

✨ the models come in 1.5B https://huggingface.co/Apollo-LMMs/Apollo-1_5B-t32, 3B https://huggingface.co/Apollo-LMMs/Apollo-3B-t32 and 7B https://huggingface.co/Apollo-LMMs/Apollo-7B-t32 with A2.0 license, based on Qwen1.5 & Qwen2 
✨ the authors also release a benchmark dataset https://huggingface.co/spaces/Apollo-LMMs/ApolloBench

The paper has a lot of experiments (they trained 84 models!) about what makes the video LMs work ⏯️

Try the demo for best setup here https://huggingface.co/spaces/Apollo-LMMs/Apollo-3B
they evaluate sampling strategies, scaling laws for models and datasets, video representation and more!
> The authors find out that whatever design decision was applied to small models also scale properly when the model and dataset are scaled 📈 scaling dataset has diminishing returns for smaller models
> They evaluate frame sampling strategies, and find that FPS sampling is better than uniform sampling, and they find 8-32 tokens per frame optimal 
> They also compare image encoders, they try a variation of models from shape optimized SigLIP to DINOv2
they find https://huggingface.co/google/siglip-so400m-patch14-384 to be most powerful 🔥
> they also compare freezing different parts of models, training all stages with some frozen parts give the best yield

They eventually release three models, where Apollo-3B outperforms most 7B models and Apollo 7B outperforms 30B models 🔥  
🚀
John6666 AtAndDev Zmu afeng prithivMLmods martintomov batuergun takarajordan fcakyon
👀
yjernite Aviv-anthonnyolime fcakyon
🤗
mukul54 fcakyon
6588155935f23c0f1c0a273e
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/98GN5Hy5MX4vf4NLYvdbH.jpeg
Sairam Pillai
sairampillai
user
63ba0506efe99543b35416b8
/avatars/f1366de159dda340d9320be412c0f7fe.svg
Straughter Guthrie
jmanhype
user
617e7dbd129c9e67703ffe62
https://cdn-avatars.huggingface.co/v1/production/uploads/617e7dbd129c9e67703ffe62/nJD_rI351t2c2QN_BpUBz.jpeg
Fatih C. Akyon
fcakyon
user
64ed641038eb641abd209a4d
/avatars/de21dc0a6ffb5f79ef4fe8af09ec0341.svg
hellobluedog
hellobluedog
user
624567459dc26c59bae6310f
https://cdn-avatars.huggingface.co/v1/production/uploads/624567459dc26c59bae6310f/Iw0iq3rrLrGfdYHU_iiYJ.png
dinhanhx
dinhanhx
user
662bff30a377af0dd433fda2
/avatars/ab4b6ec57c536f506b75c7692de051a8.svg
Maham Firdous
Moonlight2596
user
66b5f40ae2bd2d87be5b61cd
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/MErn-88HVd2Zs5TrPvh4V.png
Sarthak
FeatheredFiend
user