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kz919
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https://kyleliang919.github.io/

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liked a model about 15 hours ago
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kz919's activity

posted an update 2 days ago
reacted to rwightman's post with 🔥🚀 about 1 month ago
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1383
There's a new timm release, v 1.0.12, with a focus on optimizers. The optimizer factory has been refactored, there's now a timm.optim.list_optimizers() and new way to register optimizers and their attributes. As always you can use an timm optimizer like a torch one, just replace torch.optim with timm.optim

New optimizers include:
* AdafactorBigVision - adfactorbv
* ADOPT - adopt / adoptw (decoupled decay)
* MARS - mars
* LaProp - laprop
* Cautious Optimizers - a modification to all of the above, prefix with c as well as cadamw, cnadamw, csgdw, clamb, crmsproptf

I shared some caution comparisons in this model repo: rwightman/timm-optim-caution

For details, references, see the code: https://github.com/huggingface/pytorch-image-models/tree/main/timm/optim

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reacted to qq8933's post with 🔥 about 1 month ago
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3056
The first version of LLaMA-O1 has been uploaded to HF now!Here We Come!
Supervised:
SimpleBerry/LLaMA-O1-Supervised-1129
Base(Pretrain):
SimpleBerry/LLaMA-O1-Base-1127
Supervised Finetune Dataset:
SimpleBerry/OpenLongCoT-SFT
Pretraining Dataset:
SimpleBerry/OpenLongCoT-Pretrain-1202
RLHF is on the way! View our GitHub Repo:
https://github.com/SimpleBerry/LLaMA-O1
Our ongoing related researches:
Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B (2406.07394)
LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning (2410.02884)
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning (2411.18203)
@AdinaY @akhaliq @jwu323
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GGUF:https://huggingface.co/Lyte/LLaMA-O1-Supervised-1129-Q4_K_M-GGUF
online Demo (CPU-only): SimpleBerry/LLaMA-O1-Supervised-1129-Demo
  • 3 replies
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reacted to MonsterMMORPG's post with 👍🤝🤯🧠➕😎🤗❤️👀🚀🔥 3 months ago
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2946
Huge FLUX LoRA vs Fine Tuning / DreamBooth Experiments Completed, Moreover Batch Size 1 vs 7 Fully Tested as Well, Not Only for Realism But Also for Stylization - 15 vs 256 images having datasets compared as well (expressions / emotions tested too) - Used Kohya GUI for training

Full files and article : https://www.patreon.com/posts/112099700

Download images in full resolution to see prompts and model names

All trainings are done with Kohya GUI, perfectly can be done locally on Windows, and all trainings were 1024x1024 pixels

Fine Tuning / DreamBooth works as low as 6 GB GPUs (0 quality degrade totally same as 48 GB config)

Best quality of LoRA requires 48 GB GPUs , 24 GB also works really good and minimum 8 GB GPU is necessary for LoRA (lots of quality degrade)

Full size grids are also shared for the followings: https://www.patreon.com/posts/112099700

Additionally, I have shared full training entire logs that you can see each checkpoint took time. I have shared best checkpoints, their step count and took time according to being either LoRA, Fine Tuning or Batch size 1 or 7 or 15 images or 256 images, so a very detailed article regarding completed.

Check the images to see all shared files in the post.

Furthermore, a very very detailed analysis having article written and all latest DreamBooth / Fine Tuning configs and LoRA configs are shared with Kohya GUI installers for both Windows, Runpod and Massed Compute.

Moreover, I have shared new 28 realism and 37 stylization testing prompts.

Current tutorials are as below:

Windows requirements CUDA, Python, cuDNN, and such : https://youtu.be/DrhUHnYfwC0

How to use SwarmUI : https://youtu.be/HKX8_F1Er_w

How to use FLUX on SwarmUI : https://youtu.be/bupRePUOA18

How to use Kohya GUI for FLUX training : https://youtu.be/nySGu12Y05k

How to use Kohya GUI for FLUX training on Cloud (RunPod and Massed Compute) : https://youtu.be/-uhL2nW7Ddw
reacted to their post with 😎🔥🚀 4 months ago
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1429
Just for the meme.

But the clear lesson I learnt from building these demos are, the more powerful the underlying base model is, the closer you will get to GPT4o1. CoT is nothing more than simply inducing the latent reasoning capability from the model.

kz919/GPT4-O1-Proximas
posted an update 4 months ago
view post
Post
1429
Just for the meme.

But the clear lesson I learnt from building these demos are, the more powerful the underlying base model is, the closer you will get to GPT4o1. CoT is nothing more than simply inducing the latent reasoning capability from the model.

kz919/GPT4-O1-Proximas
posted an update 4 months ago