Llama2 finetunes are here as predicted on the pod
LLMS
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LLMs Water Consumption Crisis Demands Ban Implementation
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LLMs = Large Liquid Models. Whatever the benefit of various GenAI tools if they use 1/2 a liter of water for each 15 minute interaction their gross excessive use of water is beyond “potential harms” or “risks” and is intended. We need water. Not LLMs. Ban them.
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LLMs Water Consumption Crisis Demands Urgent Ban
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LLMs = Large Liquid Models. Whatever the benefit of various GenAI tools of they use 1/2 a liter of water for each 15 minute interaction their gross excessive use of water is beyond “potential harms” or “risks” and is intended. We need water. Not LLMs. Ban them.
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The AI Apocalypse: A Scorecard
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The AI Apocalypse: A Scorecard https://
bit.ly/3rZiLua #AI #MachineLearning #DeepLearning #LLMs #DataScience -
LangChain Expression Language for Agent Creation
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really great exploration of using langchain expression language to create an agent
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LLMs IP Violations: Attribution, Identity and Indigenous Rights
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As a former actor & author LLMs using my face/words w/out attribution or payment is a major IP violation (it’s why I’m still a SAG actor).That’s unfair & robs me of identity.Then I thought what it must be like for indigenous folks re colonization who lost their language & lives.
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Open Buildings: AI and Deep Learning for Data Science
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Open Buildings https://
bit.ly/3s3xhRJ #AI #MachineLearning #DeepLearning #LLMs #DataScience -
LangSmith RAG Testing Webinar and Evaluation Cookbook
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RAG Evaluation Webinar Last week @WHinthorn added a fantastic cookbook using LangSmith to test RAG systems: https://
github.com/langchain-ai/l
angsmith-cookbook/blob/main/testing-examples/qa-correctness/qa-correctness.ipynb
… Come listen to him talk about this (and more) during our evaluation webinar next week! Register for the webinar here: -
Stanford CS324 LLM Course: Comprehensive Notes on Large Language Models
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[Lecture notes] Stanford CS324 – Large Language Models Excellent notes on various topics related to large language models: fundamentals of language models, capabilities of LLMs, data behind LLMs, modeling, training, scaling laws, selective architectures, task adaptation, and