neuralwork

We specialize in AI safety and research, designing AI systems that are not only dependable but also clear in their workings and easily guided.

[neuralwork @neuralwork]

✨ Sharper details, better quality! Head over to our Hugging Face Space to play with our Puncta Lite image upscaler for free:
👉 https://t.co/pSXht9fwoS

[neuralwork @neuralwork]

We just launched Puncta AI! 🎉 An affordable, no-fuss image upscaler that turns low-res photos into stunning high-quality visuals in seconds. Try it out and let us know what you think! https://t.co/ssrClANmkW 📸

[neuralwork @neuralwork]

https://t.co/rxPdNtS430

[neuralwork @neuralwork]

AI is revolutionizing creativity—image generation and editing models are breaking boundaries every day. Curious about the latest advancements and trends? 🎨✨

Check out our latest survey on image generation and editing:

[neuralwork @neuralwork]

If you are a small technical team looking for simpler and cheaper workflow automation, look no further :) @DevMHat has you covered

[Muhammet Hatipoğlu @DevMHat]

🚀 Want to integrate your Linear workspace with Discord to stay updated? You could use tools like Zapier or Jira.

If you do not want to pay for it and look for (almost) free and easy option, check out my detailed walkthrough:
👉https://t.co/UDNotO4qbl

[neuralwork @neuralwork]

We are open sourcing Arxiver: 138,830 arXiv papers in multi markdown format! 📚

You can use Arxiver with @huggingface datasets library for semantic search, language modeling, QA, and summarization and more.

Dataset: https://t.co/DfdFDBEkJb

[neuralwork @neuralwork]

Need to train your own object detection model? Check out for new blog post for a step-by-step tutorial on how to efficiently fine tune Faster R-CNN on a small custom dataset

Blog post: https://t.co/4s9jYTPgr6

[neuralwork @neuralwork]

🧬💊 Accelerating drug discovery with ML-based protein-ligand binding affinity prediction! Our latest blog post explores how ML based approaches are changing the pharmaceutical industry. Check it out and let us know your thoughts! 🔬🦠

Blog: https://t.co/dGQCDrakCR

[Alara Dirik @alaradirik]

I have been working on a ControlNet pipeline for interior design with @alicanacar04 on the side. It's a work in progress but the results look pretty cool 😎

Demo: https://t.co/xS3WDUTUYB
Code: https://t.co/zlpOQ5Glq0

[neuralwork @neuralwork]

🚀 New Blog Alert! Deploying LLMs on #AWS EC2 just got easier! 📘✨

Dive into our complete guide to mastering the containerization of an LLM Inference API and deployment to AWS EC2 using #cog and #docker!

Don't miss out ➡️ (https://t.co/0UYwaKfYzG)

[Sebastian Raschka @rasbt]

I am a few days late to the party, but I just got a chance to read the TinyLlama paper (https://t.co/x9mhOcLnCf) -- the latest addition to the "small" LLM category.

On that note, what makes small LLMs (also referred to as SLMs, short for Small Language Models) so attractive? Small LLMs are:

1. Accessible and affordable, meaning they can be run (in inference mode) on limited resource regimes (such as laptops and/or small GPUs).

2. Cheaper to develop and pretrain — these models only require a relatively small number of GPUs.

3. Easier to customize for target tasks — small models can typically be fine-tuned on just a single GPU.

4. More energy-efficient — this is an important consideration given concerns about the environmental impact of training and running large-scale AI models.

5. Valuable for educational purposes — they are more manageable and thus easier to understand and tweak.

Of course, TinyLlama can't rival much larger models in benchmarks, but since all the code is open source, it presents interesting opportunities for further studies and finetuning.

For instance, an intriguing educational takeaway from the authors' training runs is that training the model for 3 epochs (instead of 1 epoch) on 1 trillion tokens is actually beneficial, despite contradicting the Chinchilla scaling laws (Hoffmann et al., 2022). These laws would have suggested a much smaller dataset for this model size.

Studying the behavior on "too large" datasets or running the training for more than one epoch would not be trivial with larger models. In any case, I am excited to see what future finetuning experiments on TinyLlama will yield.

[neuralwork @neuralwork]

An engaging chat that's a treasure trove for those eager to roll up their sleeves and dive into implementation! 🛠️💻

https://t.co/1un3oHO90y

#DeepDiveIntoTech #HandsOnLearning

[neuralwork @neuralwork]

🎙️Meet StyleTTS 2: The game-changer in text-to-speech! Open source speech stylist, mixing style diffusion & adversarial training with large speech language models. The result? Human-level that’ll make you question reality! 🤯

[neuralwork @neuralwork]

Paper: https://t.co/RaSWgTHzHM

[neuralwork @neuralwork]

3D computer vision is moving fast 🚀 Check out ControlRoom3D for detailed 3D room generations from a primitive 3D proxy.

Project page: https://t.co/cYl1MqEZwf
Paper: https://t.co/qFgw6fxuJP

[neuralwork @neuralwork]

Github: https://t.co/QIXefNzREK

[neuralwork @neuralwork]

🚀🌟 Text-to-3D & Image-to-3D models are the future, and they're here! Discover the wonders of Stable-Zero123 by @StabilityAI. Effortless implementation awaits with #threestudio team's support! Dive into the 3D revolution:
https://t.co/gONMIDHOLs

[neuralwork @neuralwork]

Model page: https://t.co/OamxLxHEKU