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MoritzLaurerย 
posted an update 5 days ago
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2260
Quite excited by the ModernBERT release! 0.15/0.4B small, 2T modern pre-training data and tokenizer with code, 8k context window, great efficient model for embeddings & classification!

This will probably be the basis for many future SOTA encoders! And I can finally stop using DeBERTav3 from 2021 :D

Congrats @answerdotai , @LightOnIO and collaborators like @tomaarsen !

Paper and models here ๐Ÿ‘‡https://huggingface.co./collections/answerdotai/modernbert-67627ad707a4acbf33c41deb
m-ricย 
posted an update 6 days ago
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1613
After 6 years, BERT, the workhorse of encoder models, finally gets a replacement: ๐—ช๐—ฒ๐—น๐—ฐ๐—ผ๐—บ๐—ฒ ๐— ๐—ผ๐—ฑ๐—ฒ๐—ฟ๐—ป๐—•๐—˜๐—ฅ๐—ง! ๐Ÿค—

We talk a lot about โœจGenerative AIโœจ, 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.

โžก๏ธ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT.

๐—ง๐—Ÿ;๐——๐—ฅ:
๐Ÿ›๏ธ Architecture changes:
โ‡’ First, standard modernizations:
- Rotary positional embeddings (RoPE)
- Replace GeLU with GeGLU,
- Use Flash Attention 2
โœจ The team also introduced innovative techniques like alternating attention instead of full attention, and sequence packing to get rid of padding overhead.

๐Ÿฅ‡ 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 ๐Ÿ‘‰ https://huggingface.co./blog/modernbert
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m-ricย 
posted an update 6 days ago
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2013
๐‡๐ฎ๐ ๐ ๐ข๐ง๐  ๐…๐š๐œ๐ž ๐ซ๐ž๐ฅ๐ž๐š๐ฌ๐ž๐ฌ ๐๐ข๐œ๐จ๐ญ๐ซ๐จ๐ง, ๐š ๐ฆ๐ข๐œ๐ซ๐จ๐ฌ๐œ๐จ๐ฉ๐ข๐œ ๐ฅ๐ข๐› ๐ญ๐ก๐š๐ญ ๐ฌ๐จ๐ฅ๐ฏ๐ž๐ฌ ๐‹๐‹๐Œ ๐ญ๐ซ๐š๐ข๐ง๐ข๐ง๐  ๐Ÿ’๐ƒ ๐ฉ๐š๐ซ๐š๐ฅ๐ฅ๐ž๐ฅ๐ข๐ณ๐š๐ญ๐ข๐จ๐ง ๐Ÿฅณ

๐Ÿ•ฐ๏ธ Llama-3.1-405B took 39 million GPU-hours to train, i.e. about 4.5 thousand years.

๐Ÿ‘ด๐Ÿป 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 "

๐Ÿ› ๏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.

๐Ÿค ๐—•๐˜‚๐˜ ๐—ป๐—ผ๐˜„ ๐˜„๐—ฒ ๐—ฑ๐—ผ๐—ป'๐˜ ๐—ป๐—ฒ๐—ฒ๐—ฑ ๐—ต๐˜‚๐—ด๐—ฒ ๐—ฟ๐—ฒ๐—ฝ๐—ผ๐˜€ ๐—ฎ๐—ป๐˜†๐—บ๐—ผ๐—ฟ๐—ฒ! 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!

โšก ๐—œ๐˜'๐˜€ ๐˜๐—ถ๐—ป๐˜†, ๐˜†๐—ฒ๐˜ ๐—ฝ๐—ผ๐˜„๐—ฒ๐—ฟ๐—ณ๐˜‚๐—น:
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 ๐Ÿ‘‰ https://github.com/huggingface/picotron/tree/main/picotron
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MoritzLaurerย 
posted an update 8 days ago
m-ricย 
posted an update 12 days ago
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2152
๐—ฃ๐—ผ๐˜๐—ฒ๐—ป๐˜๐—ถ๐—ฎ๐—น ๐—ฝ๐—ฎ๐—ฟ๐—ฎ๐—ฑ๐—ถ๐—ด๐—บ ๐˜€๐—ต๐—ถ๐—ณ๐˜ ๐—ถ๐—ป ๐—Ÿ๐—Ÿ๐— ๐˜€: ๐—ป๐—ฒ๐˜„ ๐—ฝ๐—ฎ๐—ฝ๐—ฒ๐—ฟ ๐—ฏ๐˜† ๐— ๐—ฒ๐˜๐—ฎ ๐—ฐ๐—น๐—ฎ๐—ถ๐—บ๐˜€ ๐˜๐—ต๐—ฎ๐˜ ๐˜„๐—ฒ ๐—ฐ๐—ฎ๐—ป ๐—ด๐—ฒ๐˜ ๐—ฟ๐—ถ๐—ฑ ๐—ผ๐—ณ ๐˜๐—ผ๐—ธ๐—ฒ๐—ป๐—ถ๐˜‡๐—ฒ๐—ฟ๐˜€! ๐Ÿฅณ

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.

โžก๏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.

๐Ÿ’ฅ 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! ๐Ÿ‘๐Ÿ‘

๐Ÿ—๏ธ ๐—”๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ:
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.

๐Ÿงฉ ๐——๐˜†๐—ป๐—ฎ๐—บ๐—ถ๐—ฐ ๐—ฃ๐—ฎ๐˜๐—ฐ๐—ต๐—ถ๐—ป๐—ด:
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 ๐Ÿ‘‰ https://dl.fbaipublicfiles.com/blt/BLT__Patches_Scale_Better_Than_Tokens.pdf
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MoritzLaurerย 
posted an update 13 days ago
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I've been building a small library for working with prompt templates on the HF hub: pip install prompt-templates. Motivation:

The community currently shares prompt templates in a wide variety of formats: in datasets, in model cards, as strings in .py files, as .txt/.yaml/.json/.jinja2 files etc. This makes sharing and working with prompt templates unnecessarily complicated.

Prompt templates are currently the main hyperparameter that people tune when building complex LLM systems or agents. If we don't have a common standard for sharing them, we cannot systematically test and improve our systems. After comparing different community approaches, I think that working with modular .yaml or .json files is the best approach.

The prompt-templates library :
- proposes a standard for sharing prompts (entirely locally or on the HF hub)
- provides some utilities that are interoperable with the broader ecosystem

Try it:
# !pip install prompt-templates
from prompt_templates import PromptTemplateLoader 
prompt_template = PromptTemplateLoader.from_hub(repo_id="MoritzLaurer/closed_system_prompts", filename="claude-3-5-artifacts-leak-210624.yaml")


The library is in early stages, feedback is welcome!

More details in the docs: https://github.com/MoritzLaurer/prompt_templates/
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m-ricย 
posted an update 14 days ago
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2409
๐Ÿ’ฅ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐—ฟ๐—ฒ๐—น๐—ฒ๐—ฎ๐˜€๐—ฒ๐˜€ ๐—š๐—ฒ๐—บ๐—ถ๐—ป๐—ถ ๐Ÿฎ.๐Ÿฌ, ๐˜€๐˜๐—ฎ๐—ฟ๐˜๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐˜๐—ต ๐—ฎ ๐—™๐—น๐—ฎ๐˜€๐—ต ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น ๐˜๐—ต๐—ฎ๐˜ ๐˜€๐˜๐—ฒ๐—ฎ๐—บ๐—ฟ๐—ผ๐—น๐—น๐˜€ ๐—š๐—ฃ๐—ง-๐Ÿฐ๐—ผ ๐—ฎ๐—ป๐—ฑ ๐—–๐—น๐—ฎ๐˜‚๐—ฑ๐—ฒ-๐Ÿฏ.๐Ÿฒ ๐—ฆ๐—ผ๐—ป๐—ป๐—ฒ๐˜! And they start a huge effort on agentic capabilities.

๐Ÿš€ The performance improvements are crazy for such a fast model:
โ€ฃ Gemini 2.0 Flash outperforms the previous 1.5 Pro model at twice the speed
โ€ฃ Now supports both input AND output of images, video, audio and text
โ€ฃ Can natively use tools like Google Search and execute code

โžก๏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 ๐Ÿคฏ

๐Ÿค– What about the agentic capabilities?

โ€ฃ Project Astra: A universal AI assistant that can use Google Search, Lens and Maps
โ€ฃ Project Mariner: A Chrome extension that can complete complex web tasks (83.5% success rate on WebVoyager benchmark, this is really impressive!)
โ€ฃ Jules: An AI coding agent that integrates with GitHub workflows

I'll be eagerly awaiting further news from Google!

Read their blogpost here ๐Ÿ‘‰ https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/
m-ricย 
posted an update 14 days ago
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1790
๐’๐œ๐š๐ฅ๐ข๐ง๐  ๐ฅ๐š๐ฐ๐ฌ ๐š๐ซ๐ž ๐ง๐จ๐ญ ๐๐ž๐š๐ ๐ฒ๐ž๐ญ! 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. ๐Ÿง

โ“Since 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.

๐Ÿ—ž๏ธ This new article by Semianalysis (generally a good source, specifically on hardware) provides a counter-rumor that I find more convincing:

โžก๏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/
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m-ricย 
posted an update 16 days ago
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Last week was crazy in OS AI, with important models and datasets releases every day.

Here are the most important ones I've pinned:

๐ŸŒŽ Cohere relased GLobal-MMLU, a multilingual version of MMLU, to evaluate AI models' world knowledge in many languages!

๐Ÿฆ™ 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.

๐Ÿ”‰ FishAudio released fish-speech-1.5, multilingual text to speech model

๐ŸŽจ Microsoft Research released TRELLIS, an extremely impressive image-to-3D model, which you can try here: JeffreyXiang/TRELLIS

๐Ÿ“š 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!
reach-vbย 
posted an update 18 days ago
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VLMs are going through quite an open revolution AND on-device friendly sizes:

1. Google DeepMind w/ PaliGemma2 - 3B, 10B & 28B: google/paligemma-2-release-67500e1e1dbfdd4dee27ba48

2. OpenGVLabs w/ InternVL 2.5 - 1B, 2B, 4B, 8B, 26B, 38B & 78B: https://huggingface.co./collections/OpenGVLab/internvl-25-673e1019b66e2218f68d7c1c

3. Qwen w/ Qwen 2 VL - 2B, 7B & 72B: Qwen/qwen2-vl-66cee7455501d7126940800d

4. Microsoft w/ FlorenceVL - 3B & 8B: https://huggingface.co./jiuhai

5. Moondream2 w/ 0.5B: https://huggingface.co./vikhyatk/

What a time to be alive! ๐Ÿ”ฅ
ariG23498ย 
posted an update 20 days ago
pagezyhfย 
posted an update 20 days ago
m-ricย 
posted an update 21 days ago
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๐—ฆ๐—ต๐—ผ๐˜„๐—จ๐—œ: ๐—ฎ ๐˜€๐—บ๐—ฎ๐—น๐—น ๐—ฒ๐—ป๐—ฑ-๐˜๐—ผ-๐—ฒ๐—ป๐—ฑ ๐—ฎ๐—ด๐—ฒ๐—ป๐˜ ๐˜๐—ต๐—ฎ๐˜ ๐—ฐ๐—ฎ๐—ป ๐—ป๐—ฎ๐˜ƒ๐—ถ๐—ด๐—ฎ๐˜๐—ฒ ๐—ฎ๐—ป๐˜† ๐—จ๐—œ ๐—ฎ๐—ป๐—ฑ ๐—ผ๐˜‚๐˜๐—ฝ๐—ฒ๐—ฟ๐—ณ๐—ผ๐—ฟ๐—บ๐˜€ ๐—บ๐˜‚๐—ฐ๐—ต ๐—ฏ๐—ถ๐—ด๐—ด๐—ฒ๐—ฟ ๐˜€๐˜†๐˜€๐˜๐—ฒ๐—บ๐˜€! ๐Ÿ“ฒ

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 ! ๐Ÿ‘๐Ÿ‘

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.

โš™๏ธ They put several good ideas to work:

๐Ÿ’ก 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

๐Ÿ’ก 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.

โžก๏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! ๐Ÿ†

And performance could certainly jump with a slightly bigger vision model. Let's hope the community builds this soon! ๐Ÿš€

Paper added to my "Agents" collection ๐Ÿ‘‰ m-ric/agents-65ba776fbd9e29f771c07d4e
m-ricย 
posted an update 22 days ago
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1206
Need a measurement for traction of a GitHub repo, a more reliable one than Github star history? (which is a bit too hype-driven) ๐Ÿ“ˆ

โžก๏ธ I've made a Space to visualize PyPI downloads.

Try it here ๐Ÿ‘‰ m-ric/package-download-history
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pagezyhfย 
posted an update 22 days ago
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Itโ€™s 2nd of December , hereโ€™s your Cyber Monday present ๐ŸŽ !

Weโ€™re cutting our price down on Hugging Face Inference Endpoints and Spaces!

Our folks at Google Cloud are treating us with a 40% price cut on GCP Nvidia A100 GPUs for the next 3๏ธโƒฃ months. We have other reductions on all instances ranging from 20 to 50%.

Sounds like the time to give Inference Endpoints a try? Get started today and find in our documentation the full pricing details.
https://ui.endpoints.huggingface.co/
https://huggingface.co./pricing
m-ricย 
posted an update 23 days ago
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๐Ÿค– ๐—”๐—ฑ๐—ผ๐—ฏ๐—ฒ'๐˜€ ๐—ฐ๐—ผ๐—ฑ๐—ฒ-๐—ด๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐—ป๐—ด ๐—ฎ๐—ด๐—ฒ๐—ป๐˜ ๐—ฟ๐—ฒ๐—ฎ๐—ฐ๐—ต๐—ฒ๐˜€ ๐˜๐—ต๐—ฒ ๐˜๐—ผ๐—ฝ ๐—ผ๐—ณ ๐—š๐—”๐—œ๐—” ๐—น๐—ฒ๐—ฎ๐—ฑ๐—ฒ๐—ฟ๐—ฏ๐—ผ๐—ฎ๐—ฟ๐—ฑ - and their paper cites my work!

๐Ÿ’ก Reminder:ย In short, Agentic systems are a vehicle in which you put your LLM to allow it access to the outside world.

โžก๏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 rows


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! ๐Ÿฅ‡

Go take a look, it's really clear and informative!

Paper added to my agents collection ๐Ÿ‘‰ m-ric/agents-65ba776fbd9e29f771c07d4e
m-ricย 
posted an update 26 days ago
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Single most important thing to do today: ๐—ด๐—ผ ๐˜๐—ฟ๐˜† ๐—ค๐˜„๐—ค ๐—ผ๐—ป ๐—›๐˜‚๐—ด๐—ด๐—ถ๐—ป๐—ด ๐—–๐—ต๐—ฎ๐˜!

๐Ÿ‘‰ https://huggingface.co./chat/models/Qwen/QwQ-32B-Preview
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pagezyhfย 
posted an update 28 days ago
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297
Hello Hugging Face Community,

if you use Google Kubernetes Engine to host you ML workloads, I think this series of videos is a great way to kickstart your journey of deploying LLMs, in less than 10 minutes! Thank you @wietse-venema-demo !

To watch in this order:
1. Learn what are Hugging Face Deep Learning Containers
https://youtu.be/aWMp_hUUa0c?si=t-LPRkRNfD3DDNfr

2. Learn how to deploy a LLM with our Deep Learning Container using Text Generation Inference
https://youtu.be/Q3oyTOU1TMc?si=V6Dv-U1jt1SR97fj

3. Learn how to scale your inference endpoint based on traffic
https://youtu.be/QjLZ5eteDds?si=nDIAirh1r6h2dQMD

If you want more of these small tutorials and have any theme in mind, let me know!
m-ricย 
posted an update 30 days ago
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1079
๐Ÿ—ž๏ธ ๐—ฆ๐˜๐—ฎ๐˜๐—ฒ ๐—ผ๐—ณ ๐—˜๐—ป๐˜๐—ฒ๐—ฟ๐—ฝ๐—ฟ๐—ถ๐˜€๐—ฒ ๐—”๐—œ ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฐ: ๐—”๐—ป๐˜๐—ต๐—ฟ๐—ผ๐—ฝ๐—ถ๐—ฐ ๐—ฒ๐—ฎ๐˜๐—ถ๐—ป๐—ด ๐˜‚๐—ฝ ๐—ข๐—ฝ๐—ฒ๐—ป๐—”๐—œ, ๐—”๐—ด๐—ฒ๐—ป๐˜๐˜€ ๐—ฟ๐—ฎ๐—บ๐—ฝ ๐˜‚๐—ฝ ๐˜๐—ผ ๐Ÿญ๐Ÿฎ% ๐—ผ๐—ณ ๐˜‚๐˜€๐—ฒ-๐—ฐ๐—ฎ๐˜€๐—ฒ๐˜€, ๐—ผ๐—ฝ๐—ฒ๐—ป ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€ ๐—บ๐—ฎ๐—ธ๐—ฒ ๐Ÿญ๐Ÿต% ๐—ผ๐—ณ ๐˜‚๐˜€๐—ฎ๐—ด๐—ฒ

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.

๐Ÿ‘ท Top enterprise use cases by adoption:
โ€ฃ Code copilots (51%)
- GitHub Copilot hit $300M revenue run rate
โ€ฃ Support chatbots (31%)
โ€ฃ RAG (28%)
โ€ฃ Data extraction/transformation (27%)
โ€ฃ Meeting summarization (25%)

๐Ÿ“ˆ Market dynamics:
โ€ฃ OpenAI's enterprise share dropped from 50% to 34% ๐Ÿ‘Ž
โ€ฃ Anthropic doubled presence from 12% to 24% ๐Ÿš€
โ€ฃ Open-source makes up 19% of usage ๐Ÿค—

๐Ÿ˜ฌ Implementation challenges:
โ€ฃ 26% failed due to unexpected implementation costs
โ€ฃ 21% failed due to data privacy issues
โ€ฃ 18% failed due to disappointing ROI
โ€ฃ 15% failed due to hallucinations

Read the full report here ๐Ÿ‘‰ https://menlovc.com/2024-the-state-of-generative-ai-in-the-enterprise/
reach-vbย 
posted an update about 1 month ago
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3152
Massive week for Open AI/ ML:

Mistral Pixtral & Instruct Large - ~123B, 128K context, multilingual, json + function calling & open weights
mistralai/Pixtral-Large-Instruct-2411
mistralai/Mistral-Large-Instruct-2411

Allen AI Tรผlu 70B & 8B - competive with claude 3.5 haiku, beats all major open models like llama 3.1 70B, qwen 2.5 and nemotron
allenai/tulu-3-models-673b8e0dc3512e30e7dc54f5
allenai/tulu-3-datasets-673b8df14442393f7213f372

Llava o1 - vlm capable of spontaneous, systematic reasoning, similar to GPT-o1, 11B model outperforms gemini-1.5-pro, gpt-4o-mini, and llama-3.2-90B-vision
Xkev/Llama-3.2V-11B-cot

Black Forest Labs Flux.1 tools - four new state of the art model checkpoints & 2 adapters for fill, depth, canny & redux, open weights
reach-vb/black-forest-labs-flux1-6743847bde9997dd26609817

Jina AI Jina CLIP v2 - general purpose multilingual and multimodal (text & image) embedding model, 900M params, 512 x 512 resolution, matroyoshka representations (1024 to 64)
jinaai/jina-clip-v2

Apple AIM v2 & CoreML MobileCLIP - large scale vision encoders outperform CLIP and SigLIP. CoreML optimised MobileCLIP models
apple/aimv2-6720fe1558d94c7805f7688c
apple/coreml-mobileclip

A lot more got released like, OpenScholar ( OpenScholar/openscholar-v1-67376a89f6a80f448da411a6), smoltalk ( HuggingFaceTB/smoltalk), Hymba ( nvidia/hymba-673c35516c12c4b98b5e845f), Open ASR Leaderboard ( hf-audio/open_asr_leaderboard) and much more..

Can't wait for the next week! ๐Ÿค—