LLaMA2
⭐️ Getting Started with Llama 2
Llama 2 is an open-source large language model (LLM) developed by Meta. See Llama 2's capabilities, comparisons, and how to run LLAMA 2 locally using Python.
OpenAI
Production Guides
⭐ Building Reliable LLM Apps: 5 Things To Know
In this blog post, we explore a roadmap for building reliable large language model applications. Let’s get started!
Production Guides
⭐ Reducing LLM Costs & Latency with Semantic Cache
Implementing semantic cache from scratch for production use cases.
paper summaries
FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance - Summary
The paper discusses the cost associated with querying large language models (LLMs) and proposes FrugalGPT, a framework that uses LLM APIs to process natural language queries within a budget constraint. The framework uses prompt adaptation, LLM approximation, and LLM cascade to reduce the inference
Production Guides
⭐️ Decoding OpenAI Evals
Learn how to use the eval framework to evaluate models & prompts to optimise LLM systems for the best outputs.
paper summaries
We're Afraid Language Models Aren't Modeling Ambiguity - Summary
The paper discusses the importance of managing ambiguity in natural language understanding and evaluates the ability of language models (LMs) to recognize and disentangle possible meanings. The authors present AMBIENT, a linguist-annotated benchmark of 1,645 examples with diverse kinds of ambiguity
paper summaries
Sparks of Artificial General Intelligence: Early experiments with GPT-4 - Summary
The paper reports on the investigation of an early version of GPT-4, which is part of a new cohort of LLMs that exhibit more general intelligence than previous AI models. The paper demonstrates that GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psyc
paper summaries
Eight Things to Know about Large Language Models - Summary
The paper discusses eight potentially surprising claims about large language models (LLMs), including their predictable increase in capability with increasing investment, the unpredictability of specific behaviors, and the lack of reliable techniques for steering their behavior.
paper summaries
Instruction Tuning with GPT-4 - Summary
The paper presents the first attempt to use GPT-4 to generate instruction-following data for Large Language Models (LLMs) finetuning. The 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks compared to the instruction-following