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Wulab

@wulab

Paris, France Katılım Ekim 2009
376 Takip Edilen99 Takipçiler
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Saining Xie
Saining Xie@sainingxie·
Introducing Cambrian-S it’s a position, a dataset, a benchmark, and a model but above all, it represents our first steps toward exploring spatial supersensing in video. 🧶
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Microsoft Research
Microsoft Research@MSFTResearch·
Microsoft researchers are introducing AutoGen, a framework for simplifying the orchestration, optimization, and automation of workflows for large language model (LLM) applications—potentially transforming and extending what LLMs can do. Learn more: bit.ly/466e8NY
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Yann LeCun
Yann LeCun@ylecun·
This is huge: Llama-v2 is open source, with a license that authorizes commercial use! This is going to change the landscape of the LLM market. Llama-v2 is available on Microsoft Azure and will be available on AWS, Hugging Face and other providers Pretrained and fine-tuned models are available with 7B, 13B and 70B parameters. Llama-2 website: ai.meta.com/llama/ Llama-2 paper: ai.meta.com/research/publi… A number of personalities from industry and academia have endorsed our open source approach: about.fb.com/news/2023/07/l…
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Andrej Karpathy
Andrej Karpathy@karpathy·
Code Interpreter Beta (rolling out to ChatGPT Plus) is quite powerful. It's your personal data analyst: can read uploaded files, execute code, generate diagrams, statistical analysis, much more. I expect it will take the community some time to fully chart its potential. To turn on: In ChatGPT on bottom left click on name > Settings > Beta features > turn on Code Interpreter.
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Ethan Mollick@emollick

I wrote a bit of a guide to ChatGPT’s Code Interpreter, which I have found to be the most useful and powerful mode of AI. It is, like every product made by OpenAI so far, terribly named. It is less a tool for coders and more a coder who works for you. oneusefulthing.org/p/what-ai-can-…

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Lu Jiang
Lu Jiang@roadjiang·
Fascinating research by Google reveals the power of Language Models (LLMs) like PaLM or GPT in tackling visual tasks using in-context learning. This novel method enables LLMs to perform image generation tasks without requiring any parameter updates. #palm #GPT4 #LLMs
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Shishir Patil
Shishir Patil@shishirpatil_·
📢 Excited to release Gorilla🦍 Gorilla picks from 1000s of APIs to complete user tasks, surpassing even GPT-4! LLMs need to interact with the world through APIs, and Gorilla teaches LLMs APIs. Presenting Gorilla-Spotlight demo🤩 Webpage: gorilla.cs.berkeley.edu
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Alex Volkov
Alex Volkov@altryne·
Watching @karpathy presentation from today and taking twitter notes, come along for the ride: If you're like only the practical tips, skip to #32 @karpathy starts with stages: 1 - Pre-training - months x thousands of GPUs 2, 3, 4 - Finetuning stages that take hours or days 1/
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Hugging Face
Hugging Face@huggingface·
We just released Transformers' boldest feature: Transformers Agents. This removes the barrier of entry to machine learning Control 100,000+ HF models by talking to Transformers and Diffusers Fully multimodal agent: text, images, video, audio, docs...🌎 huggingface.co/docs/transform…
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Haotian Liu
Haotian Liu@imhaotian·
🚀Introducing LLaVA Lightning: Train a lite, multimodal GPT-4 with just $40 in 3 hours! With our newly introduced datasets and the efficient design of LLaVA, you can now turbocharge your language model with image reasoning capabilities, in an incredibly affordable way.🧵
Chunyuan Li@ChunyuanLi

🔥Visual Instruction Tuning with GPT-4 ! We release LLaVA, a Language-and-Vision Assistant that exhibits some near multimodal GPT-4 level capabilities: - 🤖Visual Chat: 85% relative score of GPT-4 - 🧪Science QA on reasoning: New SoTA 92.53%, beats multimodal chain-of-thoughts

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Databricks AI Research
Databricks AI Research@DbrxMosaicAI·
📢 Introducing MPT: a new family of open-source commercially usable LLMs from @MosaicML. Trained on 1T tokens of text+code, MPT models match and - in many ways - surpass LLaMa-7B. This release includes 4 models: MPT-Base, Instruct, Chat, & StoryWriter (🧵) mosaicml.com/blog/mpt-7b
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Modular
Modular@Modular·
Mojo🔥 combines the usability of Python with the performance of C, unlocking unparalleled programmability of AI hardware and extensibility of AI models. Also, it's up to 35000x faster than Python 🤯 and … deploys 🏎
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Aakash Kumar Nain
Aakash Kumar Nain@A_K_Nain·
The code example for Denoising Diffusion Probabilistic Models in Keras is live on the site! 🥳🥳 What's in the code example, and why should you go through it? A thread 👇 keras.io/examples/gener…
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Frank Hutter
Frank Hutter@FrankRHutter·
This may revolutionize data science: we introduce TabPFN, a new tabular data classification method that takes 1 second & yields SOTA performance (better than hyperparameter-optimized gradient boosting in 1h). Current limits: up to 1k data points, 100 features, 10 classes. 🧵1/6
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MIT CSAIL
MIT CSAIL@MIT_CSAIL·
"A Path Towards Autonomous Machine Intelligence" - Yann LeCun's new position/vision/proposal paper: bit.ly/3u9LiLF
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