Post
2860
Anyone using AI and ML to help neurodivergent people? I'd love to hear what you're doing.
- 4 replies
Kaizhao Liang PRO
kz919
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reacted to
nomadicsynth's
post with ๐
5 months ago
reacted to
maxiw's
post with ๐
7 months ago
Post
2859
You can now try out computer use models from the hub to automate your local machine with https://github.com/askui/vision-agent. ๐ป
Currently these models are integrated with Gradio Spaces API. Also planning to add local inference soon!
Currently supported:
- Qwen/Qwen2-VL-7B-Instruct
- Qwen/Qwen2-VL-2B-Instruct
- AskUI/PTA-1
- OS-Copilot/OS-Atlas-Base-7B
import time
from askui import VisionAgent
with VisionAgent() as agent:
agent.tools.webbrowser.open_new("http://www.google.com")
time.sleep(0.5)
agent.click("search field in the center of the screen", model_name="Qwen/Qwen2-VL-7B-Instruct")
agent.type("cats")
agent.keyboard("enter")
time.sleep(0.5)
agent.click("text 'Images'", model_name="AskUI/PTA-1")
time.sleep(0.5)
agent.click("second cat image", model_name="OS-Copilot/OS-Atlas-Base-7B")Currently these models are integrated with Gradio Spaces API. Also planning to add local inference soon!
Currently supported:
- Qwen/Qwen2-VL-7B-Instruct
- Qwen/Qwen2-VL-2B-Instruct
- AskUI/PTA-1
- OS-Copilot/OS-Atlas-Base-7B
- 3 replies
reacted to
maxiw's
post with ๐ค๐
9 months ago
Post
2859
You can now try out computer use models from the hub to automate your local machine with https://github.com/askui/vision-agent. ๐ป
Currently these models are integrated with Gradio Spaces API. Also planning to add local inference soon!
Currently supported:
- Qwen/Qwen2-VL-7B-Instruct
- Qwen/Qwen2-VL-2B-Instruct
- AskUI/PTA-1
- OS-Copilot/OS-Atlas-Base-7B
import time
from askui import VisionAgent
with VisionAgent() as agent:
agent.tools.webbrowser.open_new("http://www.google.com")
time.sleep(0.5)
agent.click("search field in the center of the screen", model_name="Qwen/Qwen2-VL-7B-Instruct")
agent.type("cats")
agent.keyboard("enter")
time.sleep(0.5)
agent.click("text 'Images'", model_name="AskUI/PTA-1")
time.sleep(0.5)
agent.click("second cat image", model_name="OS-Copilot/OS-Atlas-Base-7B")Currently these models are integrated with Gradio Spaces API. Also planning to add local inference soon!
Currently supported:
- Qwen/Qwen2-VL-7B-Instruct
- Qwen/Qwen2-VL-2B-Instruct
- AskUI/PTA-1
- OS-Copilot/OS-Atlas-Base-7B
-
-
-
- 3 replies
reacted to
m-ric's
post with โ๐ค๐โค๏ธ๐ฅ
9 months ago
Post
4131
๐ง๐ต๐ฒ ๐๐๐ฏ ๐๐ฒ๐น๐ฐ๐ผ๐บ๐ฒ๐ ๐ฒ๐
๐๐ฒ๐ฟ๐ป๐ฎ๐น ๐ถ๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ ๐ฝ๐ฟ๐ผ๐๐ถ๐ฑ๐ฒ๐ฟ๐!
โ Hosting our own inference was not enough: now the Hub 4 new inference providers: fal, Replicate, SambaNova Systems, & Together AI.
Check model cards on the Hub: you can now, in 1 click, use inference from various providers (cf video demo)
Their inference can also be used through our Inference API client. There, you can use either your custom provider key, or your HF token, then billing will be handled directly on your HF account, as a way to centralize all expenses.
๐ธ Also, PRO users get 2$ inference credits per month!
Read more in the announcement ๐ https://huggingface.co/blog/inference-providers
โ Hosting our own inference was not enough: now the Hub 4 new inference providers: fal, Replicate, SambaNova Systems, & Together AI.
Check model cards on the Hub: you can now, in 1 click, use inference from various providers (cf video demo)
Their inference can also be used through our Inference API client. There, you can use either your custom provider key, or your HF token, then billing will be handled directly on your HF account, as a way to centralize all expenses.
๐ธ Also, PRO users get 2$ inference credits per month!
Read more in the announcement ๐ https://huggingface.co/blog/inference-providers
- 1 reply
reacted to
their
post with ๐
10 months ago
Post
1693
Mini-QwQ an edge device friendly reasoning model distilled from QwQ-32B
๐ค: kz919/QwQ-0.5B-Distilled-SFT
๐ฌ ๐ฌ ๐บ ๐ซ: kz919/QwQ-0.5B-Distilled-SFT-gguf
๐ค: kz919/Mini-QwQ
๐ค: kz919/QwQ-0.5B-Distilled-SFT
๐ฌ ๐ฌ ๐บ ๐ซ: kz919/QwQ-0.5B-Distilled-SFT-gguf
๐ค: kz919/Mini-QwQ
posted
an
update
10 months ago
Post
1693
Mini-QwQ an edge device friendly reasoning model distilled from QwQ-32B
๐ค: kz919/QwQ-0.5B-Distilled-SFT
๐ฌ ๐ฌ ๐บ ๐ซ: kz919/QwQ-0.5B-Distilled-SFT-gguf
๐ค: kz919/Mini-QwQ
๐ค: kz919/QwQ-0.5B-Distilled-SFT
๐ฌ ๐ฌ ๐บ ๐ซ: kz919/QwQ-0.5B-Distilled-SFT-gguf
๐ค: kz919/Mini-QwQ
reacted to
rwightman's
post with ๐ฅ๐
11 months ago
Post
1487
There's a new
New optimizers include:
* AdafactorBigVision -
* ADOPT -
* MARS -
* LaProp -
* Cautious Optimizers - a modification to all of the above, prefix with
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
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.optimNew 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
- 3 replies
reacted to
di-zhang-fdu's
post with ๐ฅ
11 months ago
Post
3097
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
------
GGUF:https://huggingface.co/Lyte/LLaMA-O1-Supervised-1129-Q4_K_M-GGUF
online Demo (CPU-only): SimpleBerry/LLaMA-O1-Supervised-1129-Demo
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
------
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
reacted to
MonsterMMORPG's
post with ๐๐ค๐คฏ๐ง โ๐
about 1 year ago
Post
2975
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
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