The tech community is abuzz with the introduction of new tools and benchmarks designed to evaluate the performance of Large Language Models (LLMs). A novel `EvaluatorBenchmarkerPack` has been released to validate the judgment of LLMs used in production applications. A new dataset bundle has also been introduced to specifically benchmark LLMs as evaluators. Anyscale Compute has launched the LLMPerf leaderboard, a public and open source platform for benchmarking the performance of various LLM inference providers, focusing on key metrics such as time to first token, inter-token latency, and derived end-to-end latency. The initiative aims to provide users and developers with a clear understanding of performance across different LLM providers, including AWS Bedrock, Fireworks, Replicate, and Together. Anyscale Compute has also open-sourced a reproducible benchmarking suite for comparing leading LLM providers. Additionally, there is a push for a common and standard language to compare performance metrics like latency, throughput, and correctness. Resources such as a developer's guide to prompt evaluation and the Open Source Leaderboard for LLM APIs have been released to further aid in the comparison and understanding of LLM performance. The community is encouraged to participate in this collaborative effort to enhance transparency and innovation in LLM research and applications. Moreover, new developments such as special purpose chips (LPUs) for accelerating machine learning and tools for LLM monitoring and online evaluation, along with an SDK for prompt and model experimentation, are contributing to the advancement of LLM technologies.
really useful post covering most of the recent tricks in LLM Inference and some tricks from LLM training -- and in a very approachable, easy-access way. great read! https://t.co/z2uI4DvbFB
We’re excited to announce the launch of LLM monitoring and online evaluation! This builds on our SDK for prompt and model experimentation, and our playground for team-wide LLM evaluation, to provide a way for teams to track and measure LLMs after deployment. https://t.co/srxMZExexA
We’ve developed an LLM chat service that runs at breathtaking speed compared to some other LLM chat services you may have used. Please give it a try via https://t.co/ZKfofTuRsM. The LLM chat service is built using our special purpose chips (LPUs) for accelerating machine learning… https://t.co/LlEz7SG0Fr
🌐 LLM360 by @llm360 unlocks the true significance of open-source LLM research. Transparency, collaboration, innovation, and sharing learning in the community take center stage. Want to learn more? Check out our blog post here: https://t.co/BjweITv4qf
Comparing LLM performance: Introducing the Open Source Leaderboard for LLM APIs https://t.co/pdQvvuyyFY via @anyscalecompute @replicate @awscloud @togethercompute
Just released! A developer's guide to prompt evaluation. Understanding prompt engineering is crucial for anyone looking to access the full potential of LLMs in practical applications. Get started: https://t.co/TKwTMbohEV
Curious how LLM providers compare on performance (e.g., AWS Bedrock, Fireworks, Replicate, Together, Anyscale)? Two key metrics: 🚅 Time to first token 🚢 Inter-token latency And of course, end-to-end latency can be derived from these two numbers. Importantly, the code and… https://t.co/TOfGD2sjaA
With so many Open LLM API providers it is crucial to have a common and standard language when comparing performance metrics (latency, throughput and correctness). Today we are releasing LLMPerf Leaderboard. A public and open source leaderboard for benchmarking performance.… https://t.co/BQWJ8tDFNA
LLM models are big and slow. Choosing the right provider often requires writing your own benchmarking scripts. Today, at @anyscalecompute we are open-sourcing a reproducible benchmarking suite and comparing leading LLM providers. https://t.co/J1NfLV6RER
📈We’re excited to introduce the LLMPerf leaderboard: the first public and open source leaderboard for benchmarking performance of various LLM inference providers in the market. Our goal with this leaderboard is to equip users and developers with a clear understanding of the… https://t.co/XGF4fhkaWG
To evaluate LLMs, you can use other LLMs. But how do you evaluate the LLM evaluators? If you’re trying to get evals to work in your production LLM app, you should validate that you can trust their judgment 🤔 You have to check out our brand-new `EvaluatorBenchmarkerPack` ☄️ -… https://t.co/t8hsWhvmf1 https://t.co/WTsMNYxon7
Evaluating LLM Evaluators 🧑🔬🧑🔬 A popular way to eval LLM outputs is to use other LLMs. But for this to work, these “LLM judges” have to be reliable. We’re excited to present a new kind of eval + dataset bundle 📦, specifically designed to benchmark LLMs as evaluators compared… https://t.co/gORenZROqj