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turbopuffer is a vector database built on top of object storage, which means 10x-100x cheaper, usage-based pricing, and massive scalability

Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to cases when the model makes mistakes. Here, I would like to narrow down the problem of hallucination to be when the model output is fabricated and not grounded by either the provided context or world knowledge. There are two types of hallucination: In-context hallucination: The model output should be consistent with the source content in context.

Try out Nvidia NIM in the free playground https://nvda.ws/4avifodLearn how to build a futuristic workforce of AI agents, then self-host and scale them for an...

GPTCache is a semantic cache library for LLM models and multi-models, which seamlessly integrates with 🦜️🔗LangChain and 🦙llama_index, making it accessible to 🌎 developers working in any language. -...

The NEW Claude Sonnet 3.5 model is the best model yet. In this video you find out why.👉🏼Join the BEST Ai Community: https://www.skool.com/ai-foundations/ab...

[Hacker News discussion, LinkedIn discussion, Twitter thread]

TL;DR: LLMs have been traditionally regarded as sequential decoders, decoding one token after another. In this blog, we show pretrained LLMs can be easily taught to operate as efficient parallel decoders. We introduce Consistency Large Language Models (CLLMs), a new family of parallel decoders capable of reducing inference latency by efficiently decoding an $n$-token sequence per inference step. Our research shows this process – mimicking human cognitive process of forming complete sentences in mind before articulating word by word – can be effectively learned by simply finetuning pretrained LLMs.

LLM101n: Let's build a Storyteller.

A nanoGPT pipeline packed in a spreadsheet. Contribute to dabochen/spreadsheet-is-all-you-need development by creating an account on GitHub.

The AI Backend * work in progress, please provide feedback so we can improve Just like in 1995 it was obvious that every business needs an internet presence to stay competitive, in 2024 it's obvious that every software needs intelligence to stay competitive. Software products generally have 3 c...

A practical guide to building successful LLM products.

Let's reproduce the GPT-2 (124M) in llm.c (~4,000 lines of C/CUDA) in 90 minutes for $20. The 124M model is the smallest model in the GPT-2 series released by OpenAI in 2019, and is actually quite ...

Large language models (LLMs), the technology that powers generative artificial intelligence (AI) products like ChatGPT or Google Gemini, are often thought of as chatbots that predict the next word. But that isn't the full story of what LLMs are and how they work. This is the first blog post in a three-part series explaining some key elements of how LLMs function. This blog post covers pre-training—the process by which LLMs learn to predict the next word—and why it’s so surprisingly powerful.

Join us for a comprehensive survey of techniques designed to unlock the full potential of Language Model Models (LLMs). Explore strategies such as fine-tunin...

Explore developer resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's platform.

💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows - neuml/txtai

Gain valuable insights into essential topics such as LLM training, prompt engineering, concerns, applications, and more.

Proving you're a human on a web flooded with generative AI content

i gave a talk at the recent AI Eng Singapore meetup (https://www.latent.space/p/community, scroll down) about the past 1 year in agents thinking and the bui...

Experts.js is the easiest way to create and deploy OpenAI's Assistants and link them together as Tools to create advanced Multi AI Agent Systems with expanded memory and attention to detail. - ...

Notes about my hobbies and other things I find interesting.

How does something like Perplexity work, and how do we make our own? And having done that, what turned out to be the most interesting or challenging parts?

Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization. - karpathy/minbpe

llama3 implementation one matrix multiplication at a time - naklecha/llama3-from-scratch

An opensource agent orchestration framework built on top of the latest OpenAI Assistants API. - VRSEN/agency-swarm

Crew AI is not suitable for production use cases. I’ll be going through why I believe this is the case and what you should do instead when building your own ...

Today’s language models are powering ChatGPT and other popular AIs. Learn the differences between LLMs and SLMs in this detailed article.

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