LLMs
The need for better AI or LLM-specific infrastructure, along with the host of problems that come with non-deterministic of LLMs, means that there’s more software work ahead of us, not less. Abstraction layers like LLMs create more possibilities and thus, more work.
Is this a good thing or a bad thing? I’m not sure.
A great example of this is frontend... See more
Is this a good thing or a bad thing? I’m not sure.
A great example of this is frontend... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
Overview
Loki is our open-source solution designed to automate the process of verifying factuality. It provides a comprehensive pipeline for dissecting long texts into individual claims, assessing their worthiness for verification, generating queries for evidence search, crawling for evidence, and ultimately verifying the claims. This tool is espec... See more
Loki is our open-source solution designed to automate the process of verifying factuality. It provides a comprehensive pipeline for dissecting long texts into individual claims, assessing their worthiness for verification, generating queries for evidence search, crawling for evidence, and ultimately verifying the claims. This tool is espec... See more
Libr-AI • GitHub - Libr-AI/OpenFactVerification: Open-source solution designed to automate the process of verifying factuality
In the simplest form, we can use the model’s detection confidence to determine a score. But even here there are quite a few options to choose from:
- Lowest confidence - the score is the lowest confidence of all detected objects
- Average confidence - average of all confidences of detected objects
- Minimizing confidence delta - difference between confidenc
Active Learning with Domain Experts, a Case Study in Machine Learning
What data to label?
Zerox OCR
A dead simple way of OCR-ing a document for AI ingestion. Documents are meant to be a visual representation after all. With weird layouts, tables, charts, etc. The vision models just make sense!
The general logic:
A dead simple way of OCR-ing a document for AI ingestion. Documents are meant to be a visual representation after all. With weird layouts, tables, charts, etc. The vision models just make sense!
The general logic:
- Pass in a PDF (URL or file buffer)
- Turn the PDF into a series of images
- Pass each image to GPT and ask nicely for Markdown
- Aggregat
Tyler Maran • GitHub - getomni-ai/zerox: Zero shot pdf OCR with gpt-4o-mini
Developers can now generate human-quality speech from text via the text-to-speech API. Our new TTS model offers six preset voices to choose from and two model variants, tts-1 and tts-1-hd . tts is optimized for real-time use cases and tts-1-hd is optimized for quality. Pricing starts at $0.015 per input 1,000 characters. Check out our TTS guide to ... See more
New models and developer products announced at DevDay
The new seed parameter enables reproducible outputs by making the model return consistent completions most of the time. This beta feature is useful for use cases such as replaying requests for debugging, writing more comprehensive unit tests, and generally having a higher degree of control over the model behavior. We at OpenAI have been using this ... See more
New models and developer products announced at DevDay
- Traditional AI - The most secure, understandable, and performant. However, Good implementations of traditional AI require that we define the rules behind the system, which makes it unfeasible for many of the use cases that the other 2 techniques thrive on.
- Supervised Machine Learning- Middle of the road b/w AI and Deep Learning. Good when we have
Devansh • How to Pick between Traditional AI, Supervised Machine Learning, and Deep Learning [Thoughts]
Where would I add generative AI? Generative AI has the ease of accessibility of traditional AI, where people think it is understandable, but it does not have that feature in itself. It also has the opaque and costly nature of DL. Many companies are at the moment rushing into developing things with generative AI without having any prior foundation in AI and any processes set up to manage it: data ops, devops, …
Traditional AI forces you to think about how something works, understand the system, and then define the rules for it. ML lets you use features and feature importance to shortcut some. Deep Learning allows you to brute force it. Generative AI allows you to brute force without any background in DL.
⚡ LitGPT
Pretrain, finetune, evaluate, and deploy 20+ LLMs on your own data
Uses the latest state-of-the-art techniques:
✅ flash attention ✅ fp4/8/16/32 ✅ LoRA, QLoRA, Adapter (v1, v2) ✅ FSDP ✅ 1-1000+ GPUs/TPUs
Lightning AI • Models • Quick start • Inference • Finetune • Pretrain • Deploy • Features • Training recipes (YAML)
Finetune, pretrain and d... See more
Pretrain, finetune, evaluate, and deploy 20+ LLMs on your own data
Uses the latest state-of-the-art techniques:
✅ flash attention ✅ fp4/8/16/32 ✅ LoRA, QLoRA, Adapter (v1, v2) ✅ FSDP ✅ 1-1000+ GPUs/TPUs
Lightning AI • Models • Quick start • Inference • Finetune • Pretrain • Deploy • Features • Training recipes (YAML)
Finetune, pretrain and d... See more