Announcement. @GroqInc is the first to accomplish 100 tokens per second, per user, running @MetaAI Llama-2 at 70B parameter size as an #LLM . No kernels or CUDA libraries necessary! Save thousands of developer hours with our deterministic Compiler methods.
LLMS
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The Rise of the AI Engineer
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The Rise of the AI Engineer https://
bit.ly/45kX1Ho #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Open Source Models API Integration with Fine-tuning Capabilities
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Really excited for this integration – not only do they support the top OSS models behind a stable & reliable API, but they're also making it easy to finetune (and then host) your own
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Evaluating LLM Applications with LangSmith Feedback
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It's really hard to evaluate LLM applications The most direct way is to do so is to gather feedback from the end user Here's an in depth walkthrough of using LangSmith to do that. Feedback is associated with traces, so you can easily debug bad results https://
github.com/langchain-ai/l
angsmith-cookbook/tree/main/feedback-examples/streamlit
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LangSmith Real-World Examples: From LLM Prototype to Production
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More generally we're going to putting together a lot of real world, end-to-end examples of using LangSmith to bring LLM applications from prototype to production Any particular examples you'd like to see?
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Groq Demonstrates Llama-2 70B Inference at 100+ Tokens Per Second
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Join today's GroqSpotlight in just 15 minutes and see Groq running the #LLM, Llama-2 70B, at the inference performance of more than 100 tokens per second per user. Watch on LinkedIn or YouTube at https://
youtube.com/watch?v=manwFu
-oC_c
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Llama2-7B Now Runs on Replit with Boosted Machines
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It's now easier than ever to start and stay on Replit no matter how big your code or filesystem gets – even if it’s a whole LLM.
— Replit ⠕ (@Replit) 8 août 2023
We put it to the test. Check out Llama2-7B running on Replit.
Try it yourself and add a boosted machine to the Repl for the best performance. pic.twitter.com/O6EZ1Au3oMIt's now easier than ever to start and stay on Replit no matter how big your code or filesystem gets – even if it’s a whole LLM. We put it to the test. Check out Llama2-7B running on Replit. Try it yourself and add a boosted machine to the Repl for the best performance.
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Groq Achieves 100 Tokens Per Second with Llama-2 70B LLM
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NEWS: We are the FIRST among AI start-ups and incumbent providers to run #LLM Llama-2 70B at 100 tokens per second (T/s) per user, using Groq LPU™ systems! Read more at http://
groq.link/100tps and if you're interested in a private demo, reach out to us at contact@groq.com. -
Text-to-SQL Deep Dive: Steps and Implementation Guide
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A great deep dive by @manuelsoria_ and @RLanceMartin on text-to-SQL and all the steps involved!
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Understanding How Language Models Learn Beyond Training Data
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While large language models appear to have a rich understanding of the world, how do we know they’re not simply regurgitating from training data? Check out the latest AI Explorable on a phenomenon called grokking to learn more about how models learn. → https://t.co/Okc9GvJjuN pic.twitter.com/StxRLwtSRT
— Google AI (@GoogleAI) 8 août 2023While large language models appear to have a rich understanding of the world, how do we know they’re not simply regurgitating from training data? Check out the latest AI Explorable on a phenomenon called grokking to learn more about how models learn. → https://
goo.gle/45ohnQh