Llama 3.3
Collection
This collection hosts the transformers and original repos of the Llama 3.3
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weiguosun
chuangxinlezhi
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14 days ago
Llama 3.3
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chuangxinlezhi's activity
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reacted to
CultriX's
post with ๐
about 2 months ago
Post
2486
Final upgrade to the Multi-Agent Task Completion Space:
CultriX/MultiAgent-CodeTask .
It now includes :
- a live stream of the progress being made on the task (see included video),
- The following components:
1. Automatic prompt optimization
2. An orchestrator deciding which agent to call dynamically including feedback from a human (human-in-the-loop)
3. A coding agent to complete the task
4. A code reviewing agent to iteratively provide feedback to improve the code generated by the coding agent until the code meets the required criteria after which it is approved.
5. A testing agent that tests the approved code or provides information on how to test it.
6. A documentation agent that provides documentation and a help message for the approved and tested code.
It now includes :
- a live stream of the progress being made on the task (see included video),
- The following components:
1. Automatic prompt optimization
2. An orchestrator deciding which agent to call dynamically including feedback from a human (human-in-the-loop)
3. A coding agent to complete the task
4. A code reviewing agent to iteratively provide feedback to improve the code generated by the coding agent until the code meets the required criteria after which it is approved.
5. A testing agent that tests the approved code or provides information on how to test it.
6. A documentation agent that provides documentation and a help message for the approved and tested code.
reacted to
sayakpaul's
post with ๐โ๐คฏ๐ค
2 months ago
Post
1995
We have authored a post to go over the state of video generation in the Diffusers ecosystem ๐งจ
We cover the models supported, the knobs of optims our users can fire, fine-tuning, and more ๐ฅ
5-6GBs for HunyuanVideo, sky is the limit ๐ ๐ค
https://huggingface.co/blog/video_gen
We cover the models supported, the knobs of optims our users can fire, fine-tuning, and more ๐ฅ
5-6GBs for HunyuanVideo, sky is the limit ๐ ๐ค
https://huggingface.co/blog/video_gen
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3 months ago
Update README.md
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#6 opened 3 months ago
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chuangxinlezhi
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3 months ago
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#5 opened 3 months ago
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chuangxinlezhi
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#4 opened 3 months ago
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chuangxinlezhi
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#3 opened 3 months ago
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4 months ago
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4 months ago
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#12 opened 4 months ago
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chuangxinlezhi
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#11 opened 4 months ago
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4 months ago
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#3 opened 4 months ago
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m-ric's
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5 months ago
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1638
๐๐ป๐ฑ๐ฟ๐ผ๐ถ๐ฑ๐๐ฎ๐ฏ: ๐๐ถ๐ฟ๐๐ ๐ฒ๐๐ฒ๐ฟ ๐๐๐๐๐ฒ๐บ๐ฎ๐๐ถ๐ฐ ๐ฏ๐ฒ๐ป๐ฐ๐ต๐บ๐ฎ๐ฟ๐ธ ๐ณ๐ผ๐ฟ ๐๐ป๐ฑ๐ฟ๐ผ๐ถ๐ฑ ๐บ๐ผ๐ฏ๐ถ๐น๐ฒ ๐ฎ๐ด๐ฒ๐ป๐๐ ๐๐ต๐ผ๐๐ ๐๐ต๐ฎ๐ ๐๐บ๐ฎ๐น๐น, ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ฒ๐ฑ ๐ผ๐ฝ๐ฒ๐ป ๐บ๐ผ๐ฑ๐ฒ๐น๐ ๐ฐ๐ฎ๐ป ๐ฝ๐ผ๐๐ฒ๐ฟ ๐ฎ ๐๐๐ฅ๐ฉ๐๐ฆ ๐๐๐๐๐ฒ๐บ ๐ผ๐ป ๐๐ผ๐๐ฟ ๐๐บ๐ฎ๐ฟ๐๐ฝ๐ต๐ผ๐ป๐ฒ ๐ฑ๐ฅ
A team from Tsinghua University just released AndroidLab, the first systematic framework to evaluate and train Android mobile agents that works with both text-only and multimodal models.
They show that fine-tuning small open-source models can significantly boost performance, matching that of much bigger closed models like GPT-4o.
The team built:
๐ย A reproducible benchmark with 138 tasks across 9 apps to evaluate mobile agents systematically
๐๐ฑย A framework supporting both text-only (via XML) and visual (via marked screenshots) interfaces
โ ย An instruction dataset of 10.5k operation traces for training mobile agents
Key insights:
- ๐ Fine-tuning improves performance BY A LOT: Open-source model Llama-3.1-8B improves from 2% to 24% success rate after training, nearly reaching GPT-4o performance although itโs much smaller
- โ๏ธ Text-only agents match multimodal ones: XML-based agents achieve similar performance to screenshot-based multimodal agents.
Read their paper here ๐ AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents (2410.24024)
A team from Tsinghua University just released AndroidLab, the first systematic framework to evaluate and train Android mobile agents that works with both text-only and multimodal models.
They show that fine-tuning small open-source models can significantly boost performance, matching that of much bigger closed models like GPT-4o.
The team built:
๐ย A reproducible benchmark with 138 tasks across 9 apps to evaluate mobile agents systematically
๐๐ฑย A framework supporting both text-only (via XML) and visual (via marked screenshots) interfaces
โ ย An instruction dataset of 10.5k operation traces for training mobile agents
Key insights:
- ๐ Fine-tuning improves performance BY A LOT: Open-source model Llama-3.1-8B improves from 2% to 24% success rate after training, nearly reaching GPT-4o performance although itโs much smaller
- โ๏ธ Text-only agents match multimodal ones: XML-based agents achieve similar performance to screenshot-based multimodal agents.
Read their paper here ๐ AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents (2410.24024)