Running
506
๐ป
Open Source Ai Year In Review 2024
What happened in open-source AI this year, and whatโs next?
George Duan PRO
cctuan
ยท
AI & ML interests
None yet
Recent Activity
liked
a Space
17 days ago
huggingface/open-source-ai-year-in-review-2024
liked
a Space
27 days ago
multimodalart/flux-lora-lab
updated
a model
27 days ago
cctuan/gys1217
Organizations
cctuan's activity
liked
a
Space
17 days ago
liked
a
Space
27 days ago
updated
a
model
27 days ago
updated
a
model
29 days ago
liked
a
Space
about 1 month ago
reacted to
m-ric's
post with โค๏ธ
about 1 month ago
Post
2381
Single most important thing to do today: ๐ด๐ผ ๐๐ฟ๐ ๐ค๐๐ค ๐ผ๐ป ๐๐๐ด๐ด๐ถ๐ป๐ด ๐๐ต๐ฎ๐!
๐ https://huggingface.co./chat/models/Qwen/QwQ-32B-Preview
๐ https://huggingface.co./chat/models/Qwen/QwQ-32B-Preview
- 2 replies
reacted to
davanstrien's
post with โค๏ธ
about 1 month ago
Post
2485
First dataset for the new Hugging Face Bluesky community organisation:
bluesky-community/one-million-bluesky-posts ๐ฆ
๐ 1M public posts from Bluesky's firehose API
๐ Includes text, metadata, and language predictions
๐ฌ Perfect to experiment with using ML for Bluesky ๐ค
Excited to see people build more open tools for a more open social media platform!
๐ 1M public posts from Bluesky's firehose API
๐ Includes text, metadata, and language predictions
๐ฌ Perfect to experiment with using ML for Bluesky ๐ค
Excited to see people build more open tools for a more open social media platform!
reacted to
maxiw's
post with ๐
about 2 months ago
Post
2071
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
liked
2
Spaces
2 months ago
liked
a
Space
3 months ago
reacted to
singhsidhukuldeep's
post with ๐
3 months ago
Post
2163
While Google's Transformer might have introduced "Attention is all you need," Microsoft and Tsinghua University are here with the DIFF Transformer, stating, "Sparse-Attention is all you need."
The DIFF Transformer outperforms traditional Transformers in scaling properties, requiring only about 65% of the model size or training tokens to achieve comparable performance.
The secret sauce? A differential attention mechanism that amplifies focus on relevant context while canceling out noise, leading to sparser and more effective attention patterns.
How?
- It uses two separate softmax attention maps and subtracts them.
- It employs a learnable scalar ฮป for balancing the attention maps.
- It implements GroupNorm for each attention head independently.
- It is compatible with FlashAttention for efficient computation.
What do you get?
- Superior long-context modeling (up to 64K tokens).
- Enhanced key information retrieval.
- Reduced hallucination in question-answering and summarization tasks.
- More robust in-context learning, less affected by prompt order.
- Mitigation of activation outliers, opening doors for efficient quantization.
Extensive experiments show DIFF Transformer's advantages across various tasks and model sizes, from 830M to 13.1B parameters.
This innovative architecture could be a game-changer for the next generation of LLMs. What are your thoughts on DIFF Transformer's potential impact?
The DIFF Transformer outperforms traditional Transformers in scaling properties, requiring only about 65% of the model size or training tokens to achieve comparable performance.
The secret sauce? A differential attention mechanism that amplifies focus on relevant context while canceling out noise, leading to sparser and more effective attention patterns.
How?
- It uses two separate softmax attention maps and subtracts them.
- It employs a learnable scalar ฮป for balancing the attention maps.
- It implements GroupNorm for each attention head independently.
- It is compatible with FlashAttention for efficient computation.
What do you get?
- Superior long-context modeling (up to 64K tokens).
- Enhanced key information retrieval.
- Reduced hallucination in question-answering and summarization tasks.
- More robust in-context learning, less affected by prompt order.
- Mitigation of activation outliers, opening doors for efficient quantization.
Extensive experiments show DIFF Transformer's advantages across various tasks and model sizes, from 830M to 13.1B parameters.
This innovative architecture could be a game-changer for the next generation of LLMs. What are your thoughts on DIFF Transformer's potential impact?
- 1 reply
liked
a
Space
3 months ago
reacted to
KingNish's
post with โค๏ธ
4 months ago
Post
3188
A super good and fast image inpainting demo is here.
Its' super cool and realistic.
Demo by @OzzyGT (Must try):
OzzyGT/diffusers-fast-inpaint
Its' super cool and realistic.
Demo by @OzzyGT (Must try):
OzzyGT/diffusers-fast-inpaint
updated
2
models
4 months ago
liked
a
Space
4 months ago
liked
2
Spaces
5 months ago
reacted to
MonsterMMORPG's
post with ๐ฅ
6 months ago
Post
6448
Kling AI Video is FINALLY Public (All Countries), Free to Use and MIND BLOWING - Full Tutorial > https://youtu.be/zcpqAxYV1_w
You probably seen those mind blowing AI made videos. And the day has arrived. The famous Kling AI is now worldwide available for free. In this tutorial video I will show you how to register for free with just email to Kling AI and use its mind blowing text to video animation, image to video animation and text to image, and image to image capabilities. This video will show you non-cherry pick results so you will know the actual quality and capability of the model unlike those extremely cherry pick example demos. Still, #KlingAI is the only #AI model that competes with OpenAI's #SORA and it is real to use.
๐ Kling AI Official Website โคต๏ธ
โถ๏ธ https://www.klingai.com/
๐ SECourses Discord Channel to Get Full Support โคต๏ธ
โถ๏ธ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
๐ Our GitHub Repository โคต๏ธ
โถ๏ธ https://github.com/FurkanGozukara/Stable-Diffusion
๐ Our Reddit โคต๏ธ
โถ๏ธ https://www.reddit.com/r/SECourses/
You probably seen those mind blowing AI made videos. And the day has arrived. The famous Kling AI is now worldwide available for free. In this tutorial video I will show you how to register for free with just email to Kling AI and use its mind blowing text to video animation, image to video animation and text to image, and image to image capabilities. This video will show you non-cherry pick results so you will know the actual quality and capability of the model unlike those extremely cherry pick example demos. Still, #KlingAI is the only #AI model that competes with OpenAI's #SORA and it is real to use.
๐ Kling AI Official Website โคต๏ธ
โถ๏ธ https://www.klingai.com/
๐ SECourses Discord Channel to Get Full Support โคต๏ธ
โถ๏ธ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
๐ Our GitHub Repository โคต๏ธ
โถ๏ธ https://github.com/FurkanGozukara/Stable-Diffusion
๐ Our Reddit โคต๏ธ
โถ๏ธ https://www.reddit.com/r/SECourses/
- 6 replies