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sashaย
updated
a
dataset
2 days ago
Post
4743
Since I published it on GitHub a few days ago,
Hugging Face's new agentic library ๐๐บ๐ผ๐น๐ฎ๐ด๐ฒ๐ป๐๐ has gathered nearly 4k stars ๐คฏ
โก๏ธ But we are just getting started on agents: so we are hiring an ML Engineer to join me and double down on this effort!
The plan is to build GUI agents: agents that can act on your computer with mouse & keyboard, like Claude Computer Use.
We will make it work better, and fully open. โจ
Sounds like something you'd like to do? Apply here ๐ https://apply.workable.com/huggingface/j/AF1D4E3FEB/
Hugging Face's new agentic library ๐๐บ๐ผ๐น๐ฎ๐ด๐ฒ๐ป๐๐ has gathered nearly 4k stars ๐คฏ
โก๏ธ But we are just getting started on agents: so we are hiring an ML Engineer to join me and double down on this effort!
The plan is to build GUI agents: agents that can act on your computer with mouse & keyboard, like Claude Computer Use.
We will make it work better, and fully open. โจ
Sounds like something you'd like to do? Apply here ๐ https://apply.workable.com/huggingface/j/AF1D4E3FEB/
- 3 replies
Post
2276
After 6 years, BERT, the workhorse of encoder models, finally gets a replacement: ๐ช๐ฒ๐น๐ฐ๐ผ๐บ๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐ฟ๐ป๐๐๐ฅ๐ง! ๐ค
We talk a lot about โจGenerative AIโจ, meaning "Decoder version of the Transformers architecture", but this is only one of the ways to build LLMs: encoder models, that turn a sentence in a vector, are maybe even more widely used in industry than generative models.
The workhorse for this category has been BERT since its release in 2018 (that's prehistory for LLMs).
It's not a fancy 100B parameters supermodel (just a few hundred millions), but it's an excellent workhorse, kind of a Honda Civic for LLMs.
Many applications use BERT-family models - the top models in this category cumulate millions of downloads on the Hub.
โก๏ธ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT.
๐ง๐;๐๐ฅ:
๐๏ธ Architecture changes:
โ First, standard modernizations:
- Rotary positional embeddings (RoPE)
- Replace GeLU with GeGLU,
- Use Flash Attention 2
โจ The team also introduced innovative techniques like alternating attention instead of full attention, and sequence packing to get rid of padding overhead.
๐ฅ As a result, the model tops the game of encoder models:
It beats previous standard DeBERTaV3 for 1/5th the memory footprint, and runs 4x faster!
Read the blog post ๐ https://huggingface.co/blog/modernbert
We talk a lot about โจGenerative AIโจ, meaning "Decoder version of the Transformers architecture", but this is only one of the ways to build LLMs: encoder models, that turn a sentence in a vector, are maybe even more widely used in industry than generative models.
The workhorse for this category has been BERT since its release in 2018 (that's prehistory for LLMs).
It's not a fancy 100B parameters supermodel (just a few hundred millions), but it's an excellent workhorse, kind of a Honda Civic for LLMs.
Many applications use BERT-family models - the top models in this category cumulate millions of downloads on the Hub.
โก๏ธ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT.
๐ง๐;๐๐ฅ:
๐๏ธ Architecture changes:
โ First, standard modernizations:
- Rotary positional embeddings (RoPE)
- Replace GeLU with GeGLU,
- Use Flash Attention 2
โจ The team also introduced innovative techniques like alternating attention instead of full attention, and sequence packing to get rid of padding overhead.
๐ฅ As a result, the model tops the game of encoder models:
It beats previous standard DeBERTaV3 for 1/5th the memory footprint, and runs 4x faster!
Read the blog post ๐ https://huggingface.co/blog/modernbert
- 1 reply
Post
2463
๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐
๐๐๐ ๐ซ๐๐ฅ๐๐๐ฌ๐๐ฌ ๐๐ข๐๐จ๐ญ๐ซ๐จ๐ง, ๐ ๐ฆ๐ข๐๐ซ๐จ๐ฌ๐๐จ๐ฉ๐ข๐ ๐ฅ๐ข๐ ๐ญ๐ก๐๐ญ ๐ฌ๐จ๐ฅ๐ฏ๐๐ฌ ๐๐๐ ๐ญ๐ซ๐๐ข๐ง๐ข๐ง๐ ๐๐ ๐ฉ๐๐ซ๐๐ฅ๐ฅ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐ฅณ
๐ฐ๏ธ Llama-3.1-405B took 39 million GPU-hours to train, i.e. about 4.5 thousand years.
๐ด๐ป If they had needed all this time, we would have GPU stories from the time of Pharaoh ๐: "Alas, Lord of Two Lands, the shipment of counting-stones arriving from Cathay was lost to pirates, this shall delay the building of your computing temple by many moons "
๐ ๏ธ But instead, they just parallelized the training on 24k H100s, which made it take just a few months.
This required parallelizing across 4 dimensions: data, tensor, context, pipeline.
And it is infamously hard to do, making for bloated code repos that hold together only by magic.
๐ค ๐๐๐ ๐ป๐ผ๐ ๐๐ฒ ๐ฑ๐ผ๐ป'๐ ๐ป๐ฒ๐ฒ๐ฑ ๐ต๐๐ด๐ฒ ๐ฟ๐ฒ๐ฝ๐ผ๐ ๐ฎ๐ป๐๐บ๐ผ๐ฟ๐ฒ! Instead of building mega-training codes, Hugging Face colleagues cooked in the other direction, towards tiny 4D parallelism libs. A team has built Nanotron, already widely used in industry.
And now a team releases Picotron, a radical approach to code 4D Parallelism in just a few hundred lines of code, a real engineering prowess, making it much easier to understand what's actually happening!
โก ๐๐'๐ ๐๐ถ๐ป๐, ๐๐ฒ๐ ๐ฝ๐ผ๐๐ฒ๐ฟ๐ณ๐๐น:
Counting in MFU (Model FLOPs Utilization, how much the model actually uses all the compute potential), this lib reaches ~50% on SmolLM-1.7B model with 8 H100 GPUs, which is really close to what huge libs would reach. (Caution: the team is leading further benchmarks to verify this)
Go take a look ๐ https://github.com/huggingface/picotron/tree/main/picotron
๐ฐ๏ธ Llama-3.1-405B took 39 million GPU-hours to train, i.e. about 4.5 thousand years.
๐ด๐ป If they had needed all this time, we would have GPU stories from the time of Pharaoh ๐: "Alas, Lord of Two Lands, the shipment of counting-stones arriving from Cathay was lost to pirates, this shall delay the building of your computing temple by many moons "
๐ ๏ธ But instead, they just parallelized the training on 24k H100s, which made it take just a few months.
This required parallelizing across 4 dimensions: data, tensor, context, pipeline.
And it is infamously hard to do, making for bloated code repos that hold together only by magic.
๐ค ๐๐๐ ๐ป๐ผ๐ ๐๐ฒ ๐ฑ๐ผ๐ป'๐ ๐ป๐ฒ๐ฒ๐ฑ ๐ต๐๐ด๐ฒ ๐ฟ๐ฒ๐ฝ๐ผ๐ ๐ฎ๐ป๐๐บ๐ผ๐ฟ๐ฒ! Instead of building mega-training codes, Hugging Face colleagues cooked in the other direction, towards tiny 4D parallelism libs. A team has built Nanotron, already widely used in industry.
And now a team releases Picotron, a radical approach to code 4D Parallelism in just a few hundred lines of code, a real engineering prowess, making it much easier to understand what's actually happening!
โก ๐๐'๐ ๐๐ถ๐ป๐, ๐๐ฒ๐ ๐ฝ๐ผ๐๐ฒ๐ฟ๐ณ๐๐น:
Counting in MFU (Model FLOPs Utilization, how much the model actually uses all the compute potential), this lib reaches ~50% on SmolLM-1.7B model with 8 H100 GPUs, which is really close to what huge libs would reach. (Caution: the team is leading further benchmarks to verify this)
Go take a look ๐ https://github.com/huggingface/picotron/tree/main/picotron
- 1 reply
Post
947
Nice to see day 1 support of Falcon 3 on Gaudi with Optimum Habana!
๐ https://www.intel.com/content/www/us/en/developer/articles/technical/intel-ai-solutions-support-falcon-3-fdn-models.html
๐ https://www.intel.com/content/www/us/en/developer/articles/technical/intel-ai-solutions-support-falcon-3-fdn-models.html
Tradeoff energy bost / performance
1
#2 opened 26 days ago
by
cerisara
Post
2202
๐ฃ๐ผ๐๐ฒ๐ป๐๐ถ๐ฎ๐น ๐ฝ๐ฎ๐ฟ๐ฎ๐ฑ๐ถ๐ด๐บ ๐๐ต๐ถ๐ณ๐ ๐ถ๐ป ๐๐๐ ๐: ๐ป๐ฒ๐ ๐ฝ๐ฎ๐ฝ๐ฒ๐ฟ ๐ฏ๐ ๐ ๐ฒ๐๐ฎ ๐ฐ๐น๐ฎ๐ถ๐บ๐ ๐๐ต๐ฎ๐ ๐๐ฒ ๐ฐ๐ฎ๐ป ๐ด๐ฒ๐ ๐ฟ๐ถ๐ฑ ๐ผ๐ณ ๐๐ผ๐ธ๐ฒ๐ป๐ถ๐๐ฒ๐ฟ๐! ๐ฅณ
Current LLMs process text by first splitting it into tokens. They use a module named "tokenizer", that -spl-it-s- th-e- te-xt- in-to- arbitrary tokens depending on a fixed dictionnary.
On the Hub you can find this dictionary in a model's files under tokenizer.json.
โก๏ธ This process is called BPE tokenization. It is suboptimal, everyone says it. It breaks text into predefined chunks that often fail to capture the nuance of language. But it has been a necessary evil in language models since their inception.
๐ฅ In Byte Latent Transformer (BLT), Meta researchers propose an elegant solution by eliminating tokenization entirely, working directly with raw bytes while maintaining efficiency through dynamic "patches."
This had been tried before with different byte-level tokenizations, but it's the first time that an architecture of this type scales as well as BPE tokenization. And it could mean a real paradigm shift! ๐๐
๐๏ธ ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ:
Instead of a lightweight tokenizer, BLT has a lightweight encoder that process raw bytes into patches. Then the patches are processed by the main heavy-duty transformers as we do normally (but for patches of bytes instead of tokens), before converting back to bytes.
๐งฉ ๐๐๐ป๐ฎ๐บ๐ถ๐ฐ ๐ฃ๐ฎ๐๐ฐ๐ต๐ถ๐ป๐ด:
Instead of fixed tokens, BLT groups bytes based on their predictability (measured by entropy) - using more compute for complex sequences and efficiently handling simple ones. This allows efficient processing while maintaining byte-level understanding.
I hope this breakthrough is confirmed and we can get rid of all the tokenizer stuff, it will make model handling easier!
Read their paper here ๐ https://dl.fbaipublicfiles.com/blt/BLT__Patches_Scale_Better_Than_Tokens.pdf
Current LLMs process text by first splitting it into tokens. They use a module named "tokenizer", that -spl-it-s- th-e- te-xt- in-to- arbitrary tokens depending on a fixed dictionnary.
On the Hub you can find this dictionary in a model's files under tokenizer.json.
โก๏ธ This process is called BPE tokenization. It is suboptimal, everyone says it. It breaks text into predefined chunks that often fail to capture the nuance of language. But it has been a necessary evil in language models since their inception.
๐ฅ In Byte Latent Transformer (BLT), Meta researchers propose an elegant solution by eliminating tokenization entirely, working directly with raw bytes while maintaining efficiency through dynamic "patches."
This had been tried before with different byte-level tokenizations, but it's the first time that an architecture of this type scales as well as BPE tokenization. And it could mean a real paradigm shift! ๐๐
๐๏ธ ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ:
Instead of a lightweight tokenizer, BLT has a lightweight encoder that process raw bytes into patches. Then the patches are processed by the main heavy-duty transformers as we do normally (but for patches of bytes instead of tokens), before converting back to bytes.
๐งฉ ๐๐๐ป๐ฎ๐บ๐ถ๐ฐ ๐ฃ๐ฎ๐๐ฐ๐ต๐ถ๐ป๐ด:
Instead of fixed tokens, BLT groups bytes based on their predictability (measured by entropy) - using more compute for complex sequences and efficiently handling simple ones. This allows efficient processing while maintaining byte-level understanding.
I hope this breakthrough is confirmed and we can get rid of all the tokenizer stuff, it will make model handling easier!
Read their paper here ๐ https://dl.fbaipublicfiles.com/blt/BLT__Patches_Scale_Better_Than_Tokens.pdf
- 2 replies
Post
2099
๐ช๐บ Policy Thoughts in the EU AI Act Implementation ๐ช๐บ
There is a lot to like in the first draft of the EU GPAI Code of Practice, especially as regards transparency requirements. The Systemic Risks part, on the other hand, is concerning for both smaller developers and for external stakeholders.
I wrote more on this topic ahead of the next draft. TLDR: more attention to immediate large-scale risks and to collaborative solutions supported by evidence can help everyone - as long as developers disclose sufficient information about their design choices and deployment contexts.
Full blog here, based on our submitted response with @frimelle and @brunatrevelin :
https://huggingface.co/blog/yjernite/eu-draft-cop-risks#on-the-proposed-taxonomy-of-systemic-risks
There is a lot to like in the first draft of the EU GPAI Code of Practice, especially as regards transparency requirements. The Systemic Risks part, on the other hand, is concerning for both smaller developers and for external stakeholders.
I wrote more on this topic ahead of the next draft. TLDR: more attention to immediate large-scale risks and to collaborative solutions supported by evidence can help everyone - as long as developers disclose sufficient information about their design choices and deployment contexts.
Full blog here, based on our submitted response with @frimelle and @brunatrevelin :
https://huggingface.co/blog/yjernite/eu-draft-cop-risks#on-the-proposed-taxonomy-of-systemic-risks
- 2 replies
Post
2557
๐ฅ ๐๐ผ๐ผ๐ด๐น๐ฒ ๐ฟ๐ฒ๐น๐ฒ๐ฎ๐๐ฒ๐ ๐๐ฒ๐บ๐ถ๐ป๐ถ ๐ฎ.๐ฌ, ๐๐๐ฎ๐ฟ๐๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐ฎ ๐๐น๐ฎ๐๐ต ๐บ๐ผ๐ฑ๐ฒ๐น ๐๐ต๐ฎ๐ ๐๐๐ฒ๐ฎ๐บ๐ฟ๐ผ๐น๐น๐ ๐๐ฃ๐ง-๐ฐ๐ผ ๐ฎ๐ป๐ฑ ๐๐น๐ฎ๐๐ฑ๐ฒ-๐ฏ.๐ฒ ๐ฆ๐ผ๐ป๐ป๐ฒ๐! And they start a huge effort on agentic capabilities.
๐ The performance improvements are crazy for such a fast model:
โฃ Gemini 2.0 Flash outperforms the previous 1.5 Pro model at twice the speed
โฃ Now supports both input AND output of images, video, audio and text
โฃ Can natively use tools like Google Search and execute code
โก๏ธ If the price is on par with previous Flash iteration ($0.30 / M tokens, to compare with GPT-4o's $1.25) the competition will have a big problem with this 4x cheaper model that gets better benchmarks ๐คฏ
๐ค What about the agentic capabilities?
โฃ Project Astra: A universal AI assistant that can use Google Search, Lens and Maps
โฃ Project Mariner: A Chrome extension that can complete complex web tasks (83.5% success rate on WebVoyager benchmark, this is really impressive!)
โฃ Jules: An AI coding agent that integrates with GitHub workflows
I'll be eagerly awaiting further news from Google!
Read their blogpost here ๐ https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/
๐ The performance improvements are crazy for such a fast model:
โฃ Gemini 2.0 Flash outperforms the previous 1.5 Pro model at twice the speed
โฃ Now supports both input AND output of images, video, audio and text
โฃ Can natively use tools like Google Search and execute code
โก๏ธ If the price is on par with previous Flash iteration ($0.30 / M tokens, to compare with GPT-4o's $1.25) the competition will have a big problem with this 4x cheaper model that gets better benchmarks ๐คฏ
๐ค What about the agentic capabilities?
โฃ Project Astra: A universal AI assistant that can use Google Search, Lens and Maps
โฃ Project Mariner: A Chrome extension that can complete complex web tasks (83.5% success rate on WebVoyager benchmark, this is really impressive!)
โฃ Jules: An AI coding agent that integrates with GitHub workflows
I'll be eagerly awaiting further news from Google!
Read their blogpost here ๐ https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/
Post
1813
๐๐๐๐ฅ๐ข๐ง๐ ๐ฅ๐๐ฐ๐ฌ ๐๐ซ๐ ๐ง๐จ๐ญ ๐๐๐๐ ๐ฒ๐๐ญ! New blog post suggests Anthropic might have an extremely strong Opus-3.5 already available, but is not releasing it to keep their edge over the competition. ๐ง
โSince the release of Opus-3.5 has been delayed indefinitely, there have been lots of rumors and articles about LLMs plateauing. Scaling laws, the main powering factor of the LLM competence increase, could have stopped, according to these rumors, being the cause of this stalling of progress.
These rumors were quickly denied by many people at the leading LLM labs, including OpenAI and Anthropic. But these people would be expected to hype the future of LLMs even if scaling laws really plateaued, so the jury is still out.
๐๏ธ This new article by Semianalysis (generally a good source, specifically on hardware) provides a counter-rumor that I find more convincing:
โก๏ธ Maybe scaling laws still work, Opus-3.5 is ready and as good as planned, but they just don't release it because the synthetic data it helps provide can bring cheaper/smaller models Claude and Haiku up in performance, without risking to leak this precious high-quality synthetic data to competitors.
Time will tell! I feel like we'll know more soon.
Read the article: https://semianalysis.com/2024/12/11/scaling-laws-o1-pro-architecture-reasoning-infrastructure-orion-and-claude-3-5-opus-failures/
โSince the release of Opus-3.5 has been delayed indefinitely, there have been lots of rumors and articles about LLMs plateauing. Scaling laws, the main powering factor of the LLM competence increase, could have stopped, according to these rumors, being the cause of this stalling of progress.
These rumors were quickly denied by many people at the leading LLM labs, including OpenAI and Anthropic. But these people would be expected to hype the future of LLMs even if scaling laws really plateaued, so the jury is still out.
๐๏ธ This new article by Semianalysis (generally a good source, specifically on hardware) provides a counter-rumor that I find more convincing:
โก๏ธ Maybe scaling laws still work, Opus-3.5 is ready and as good as planned, but they just don't release it because the synthetic data it helps provide can bring cheaper/smaller models Claude and Haiku up in performance, without risking to leak this precious high-quality synthetic data to competitors.
Time will tell! I feel like we'll know more soon.
Read the article: https://semianalysis.com/2024/12/11/scaling-laws-o1-pro-architecture-reasoning-infrastructure-orion-and-claude-3-5-opus-failures/
- 1 reply
Post
2238
Last week was crazy in OS AI, with important models and datasets releases every day.
Here are the most important ones I've pinned:
๐ Cohere relased GLobal-MMLU, a multilingual version of MMLU, to evaluate AI models' world knowledge in many languages!
๐ฆ Meta released Llama-3.3-70B-Instruct, a 70B model that's on par with Llama-3.1-405B-Instruct, GPT-4o and Claude. Probably my new go-to for agentic workflows.
๐ FishAudio released fish-speech-1.5, multilingual text to speech model
๐จ Microsoft Research released TRELLIS, an extremely impressive image-to-3D model, which you can try here: JeffreyXiang/TRELLIS
๐ Yesterday, Hugging Face release FineWeb 2, a new version that extends the previous FineWeb to over 1000 languages, including extended coverage in Russina, Mandarin, German, Japanese, Spanish, French, so a huge, high-quality dataset of > 3 trillion words! HuggingFaceFW/fineweb-2
Now let's go build to make this week as productive as last one!
Here are the most important ones I've pinned:
๐ Cohere relased GLobal-MMLU, a multilingual version of MMLU, to evaluate AI models' world knowledge in many languages!
๐ฆ Meta released Llama-3.3-70B-Instruct, a 70B model that's on par with Llama-3.1-405B-Instruct, GPT-4o and Claude. Probably my new go-to for agentic workflows.
๐ FishAudio released fish-speech-1.5, multilingual text to speech model
๐จ Microsoft Research released TRELLIS, an extremely impressive image-to-3D model, which you can try here: JeffreyXiang/TRELLIS
๐ Yesterday, Hugging Face release FineWeb 2, a new version that extends the previous FineWeb to over 1000 languages, including extended coverage in Russina, Mandarin, German, Japanese, Spanish, French, so a huge, high-quality dataset of > 3 trillion words! HuggingFaceFW/fineweb-2
Now let's go build to make this week as productive as last one!
sashaย
updated
a
dataset
about 1 month ago
m-ricย
updated
a
Space
about 1 month ago
Expanding AI Energy Score Research to other GPUs
1
#1 opened about 1 month ago
by
PranavViswanath
Post
1484
๐ฆ๐ต๐ผ๐๐จ๐: ๐ฎ ๐๐บ๐ฎ๐น๐น ๐ฒ๐ป๐ฑ-๐๐ผ-๐ฒ๐ป๐ฑ ๐ฎ๐ด๐ฒ๐ป๐ ๐๐ต๐ฎ๐ ๐ฐ๐ฎ๐ป ๐ป๐ฎ๐๐ถ๐ด๐ฎ๐๐ฒ ๐ฎ๐ป๐ ๐จ๐ ๐ฎ๐ป๐ฑ ๐ผ๐๐๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ ๐บ๐๐ฐ๐ต ๐ฏ๐ถ๐ด๐ด๐ฒ๐ฟ ๐๐๐๐๐ฒ๐บ๐! ๐ฒ
A team from NUS and Microsoft just released an agent that can act on any UI (Desktop, Android, Web) without needing additional text information. It works extremely well : they applied their method on a tiny Qwen2-VL-2B, and they managed to beat methods that use either much more powerful vision models (like GPT-4V) without using any additional info (e.g. leveraging the DOM of a webpage) like previous methods did ! ๐๐
They started from the idea that most existing methods rely heavily on text, which makes them less generalizable, while letting aside rich UI structure that user actually rely on when navigating this interfaces.
โ๏ธ They put several good ideas to work:
๐ก Simplify screenshots to the max:
They prune a lot the heavy visual content of UI screenshots, by removing cloned image patches (like any vast patch of the same color will be reduced to a small patch, while maintaining positional embeddings), then group patches from the same GUI elements together to simplify even further
๐ก Build a truly generalist dataset:
To train a general UI agent, you need trajectories from each possible UI, and express them in a common language. Authors merge datasets like OmniAct for Desktop, Mind2Web for websites, AMEX for Android trajectories to create a high-quality and diverse dataset.
โก๏ธ Nice results ensued:
They fine-tune a tiny Qwen-2-VL-2B on their method, and it reaches SOTA on several task (element identification, web navigation), even beating methods that either use additional info from the DOM or use much bigger VLMS like GPT-4v! ๐
And performance could certainly jump with a slightly bigger vision model. Let's hope the community builds this soon! ๐
Paper added to my "Agents" collection ๐ m-ric/agents-65ba776fbd9e29f771c07d4e
A team from NUS and Microsoft just released an agent that can act on any UI (Desktop, Android, Web) without needing additional text information. It works extremely well : they applied their method on a tiny Qwen2-VL-2B, and they managed to beat methods that use either much more powerful vision models (like GPT-4V) without using any additional info (e.g. leveraging the DOM of a webpage) like previous methods did ! ๐๐
They started from the idea that most existing methods rely heavily on text, which makes them less generalizable, while letting aside rich UI structure that user actually rely on when navigating this interfaces.
โ๏ธ They put several good ideas to work:
๐ก Simplify screenshots to the max:
They prune a lot the heavy visual content of UI screenshots, by removing cloned image patches (like any vast patch of the same color will be reduced to a small patch, while maintaining positional embeddings), then group patches from the same GUI elements together to simplify even further
๐ก Build a truly generalist dataset:
To train a general UI agent, you need trajectories from each possible UI, and express them in a common language. Authors merge datasets like OmniAct for Desktop, Mind2Web for websites, AMEX for Android trajectories to create a high-quality and diverse dataset.
โก๏ธ Nice results ensued:
They fine-tune a tiny Qwen-2-VL-2B on their method, and it reaches SOTA on several task (element identification, web navigation), even beating methods that either use additional info from the DOM or use much bigger VLMS like GPT-4v! ๐
And performance could certainly jump with a slightly bigger vision model. Let's hope the community builds this soon! ๐
Paper added to my "Agents" collection ๐ m-ric/agents-65ba776fbd9e29f771c07d4e
m-ricย
updated
a
dataset
about 1 month ago
Post
1218
Need a measurement for traction of a GitHub repo, a more reliable one than Github star history? (which is a bit too hype-driven) ๐
โก๏ธ I've made a Space to visualize PyPI downloads.
Try it here ๐ m-ric/package-download-history
โก๏ธ I've made a Space to visualize PyPI downloads.
Try it here ๐ m-ric/package-download-history
- 1 reply
Post
1280
๐ค ๐๐ฑ๐ผ๐ฏ๐ฒ'๐ ๐ฐ๐ผ๐ฑ๐ฒ-๐ด๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐ป๐ด ๐ฎ๐ด๐ฒ๐ป๐ ๐ฟ๐ฒ๐ฎ๐ฐ๐ต๐ฒ๐ ๐๐ต๐ฒ ๐๐ผ๐ฝ ๐ผ๐ณ ๐๐๐๐ ๐น๐ฒ๐ฎ๐ฑ๐ฒ๐ฟ๐ฏ๐ผ๐ฎ๐ฟ๐ฑ - and their paper cites my work!
๐ก Reminder:ย In short, Agentic systems are a vehicle in which you put your LLM to allow it access to the outside world.
โก๏ธ The team of researchers at Adobe started from the idea that current agentic systems lack the ability to define their own tools. So they decided to make an agent that writes actions as code, thus allowing it to write python functions that can be re-used later as tools!
Here's what the LLM generations can look like with the proper prompt:
Thought: I need to access the excel file using a different method.
Action:
Then your system executes this and appends the observation to the agent's memory.
Why is this code formulation better than classical tool use formulation as JSON? The paper explains:
"Most existing work uses text or JSON as the representation of actions, which significantly lacks the two criteria mentioned earlier: generality and composability. In contrast, DynaSaur can utilize available actions or create new ones if necessary, using code as a unified representation. In principle, acting with code enables agents to solve any Turing-complete problem."
The idea of using code is not new: in fact, we do it in transformers.agents (thus the citation that I got). They implementation adds further refinements, like using RAG to retrieve relevant functions before generating an action, which increases performance further.
And they observe that code agents perform much better, reaching the top of GAIA leaderboard! ๐ฅ
Go take a look, it's really clear and informative!
Paper added to my agents collection ๐ m-ric/agents-65ba776fbd9e29f771c07d4e
๐ก Reminder:ย In short, Agentic systems are a vehicle in which you put your LLM to allow it access to the outside world.
โก๏ธ The team of researchers at Adobe started from the idea that current agentic systems lack the ability to define their own tools. So they decided to make an agent that writes actions as code, thus allowing it to write python functions that can be re-used later as tools!
Here's what the LLM generations can look like with the proper prompt:
Thought: I need to access the excel file using a different method.
Action:
def access_excel_file(file_path)
... # rest of the code (the agent does writes it, but I don't have room in this post)
return rowsThen your system executes this and appends the observation to the agent's memory.
Why is this code formulation better than classical tool use formulation as JSON? The paper explains:
"Most existing work uses text or JSON as the representation of actions, which significantly lacks the two criteria mentioned earlier: generality and composability. In contrast, DynaSaur can utilize available actions or create new ones if necessary, using code as a unified representation. In principle, acting with code enables agents to solve any Turing-complete problem."
The idea of using code is not new: in fact, we do it in transformers.agents (thus the citation that I got). They implementation adds further refinements, like using RAG to retrieve relevant functions before generating an action, which increases performance further.
And they observe that code agents perform much better, reaching the top of GAIA leaderboard! ๐ฅ
Go take a look, it's really clear and informative!
Paper added to my agents collection ๐ m-ric/agents-65ba776fbd9e29f771c07d4e
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
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1083
๐๏ธ ๐ฆ๐๐ฎ๐๐ฒ ๐ผ๐ณ ๐๐ป๐๐ฒ๐ฟ๐ฝ๐ฟ๐ถ๐๐ฒ ๐๐ ๐ฎ๐ฌ๐ฎ๐ฐ: ๐๐ป๐๐ต๐ฟ๐ผ๐ฝ๐ถ๐ฐ ๐ฒ๐ฎ๐๐ถ๐ป๐ด ๐๐ฝ ๐ข๐ฝ๐ฒ๐ป๐๐, ๐๐ด๐ฒ๐ป๐๐ ๐ฟ๐ฎ๐บ๐ฝ ๐๐ฝ ๐๐ผ ๐ญ๐ฎ% ๐ผ๐ณ ๐๐๐ฒ-๐ฐ๐ฎ๐๐ฒ๐, ๐ผ๐ฝ๐ฒ๐ป ๐บ๐ผ๐ฑ๐ฒ๐น๐ ๐บ๐ฎ๐ธ๐ฒ ๐ญ๐ต% ๐ผ๐ณ ๐๐๐ฎ๐ด๐ฒ
Menlo Ventures surveyed 600 enterprise IT decision-makers for their 2024 report. They reveal that AI spending surged to $13.8 billion this year, more than 6x the $2.3 billion spent in 2023!
Companies are shifting from experimentation to serious implementation.
๐ท Top enterprise use cases by adoption:
โฃ Code copilots (51%)
- GitHub Copilot hit $300M revenue run rate
โฃ Support chatbots (31%)
โฃ RAG (28%)
โฃ Data extraction/transformation (27%)
โฃ Meeting summarization (25%)
๐ Market dynamics:
โฃ OpenAI's enterprise share dropped from 50% to 34% ๐
โฃ Anthropic doubled presence from 12% to 24% ๐
โฃ Open-source makes up 19% of usage ๐ค
๐ฌ Implementation challenges:
โฃ 26% failed due to unexpected implementation costs
โฃ 21% failed due to data privacy issues
โฃ 18% failed due to disappointing ROI
โฃ 15% failed due to hallucinations
Read the full report here ๐ https://menlovc.com/2024-the-state-of-generative-ai-in-the-enterprise/
Menlo Ventures surveyed 600 enterprise IT decision-makers for their 2024 report. They reveal that AI spending surged to $13.8 billion this year, more than 6x the $2.3 billion spent in 2023!
Companies are shifting from experimentation to serious implementation.
๐ท Top enterprise use cases by adoption:
โฃ Code copilots (51%)
- GitHub Copilot hit $300M revenue run rate
โฃ Support chatbots (31%)
โฃ RAG (28%)
โฃ Data extraction/transformation (27%)
โฃ Meeting summarization (25%)
๐ Market dynamics:
โฃ OpenAI's enterprise share dropped from 50% to 34% ๐
โฃ Anthropic doubled presence from 12% to 24% ๐
โฃ Open-source makes up 19% of usage ๐ค
๐ฌ Implementation challenges:
โฃ 26% failed due to unexpected implementation costs
โฃ 21% failed due to data privacy issues
โฃ 18% failed due to disappointing ROI
โฃ 15% failed due to hallucinations
Read the full report here ๐ https://menlovc.com/2024-the-state-of-generative-ai-in-the-enterprise/