kanaria007/agi-structural-intelligence-protocols
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m-ric's
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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
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davanstrien's
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First dataset for the new Hugging Face Bluesky community organisation: https://huggingface.co/datasets/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!
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8 months ago
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2701
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
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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?
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KingNish's
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10 months ago
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3806
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