Announcement: New Improved #Finetuning Stack—10x Faster Training! Here are the highlights: New #training stack up to 10x faster + better model quality #Llama3 available for inference + fine-tuning New Python #SDK: More consistent and robust
GENERATIVE AI
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GPT-4.5 disappoints: gpt2-chatbot outperforms expectations
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gpt2-chatbot is good. really good. but if this is gpt-4.5, I’m disappointed.
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GPT Prompt Engineering Approach for Optimal Results
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Could apply a gpt-prompt-engineer type approach if there's a lot of time!
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AWS Cohere Command R deployment webinar for enterprise automation
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AWS and Cohere will share more about Command R and R+ and how to seamlessly deploy our models at scale to automate critical business tasks. Join us on May 1st at 1:00 pm ET: https://
pages.awscloud.com/awsmp-gim-xlwy
-webinar-aim-generative-ai-cohere-spotlight-series.html?trk=475ad537-df52-4a80-9317-6ed4db81757a&sc_channel=el
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Command R Models Now Available on Amazon Bedrock
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Our latest Command R model family is now available on Amazon Bedrock by @awscloud
! These state-of-the-art LLMs are designed to tackle enterprise-grade workloads, and excel at business-critical capabilities like multilingual coverage, RAG, and tool use. -
AWS AI Models Now Available for Testing
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You can put our models to the test for your specific use cases on @awscloud
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Understanding System Prompts in AI Language Models
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That assumes that it knows what a system prompt is, as opposed to knowing that when people ask about system prompts the response should look something like what it output there
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Extracting AI System Prompts: Challenges and Hallucination Risks
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I'm not sure that's the real system prompt – looks to me like it could be a hallucination based on training data. My own attempts to extract the system prompt have so far failed:
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AI Model Hallucination-Free Responses Challenge
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I don't see any hallucinated details in here at all, which never happens with these kinds of ego-prompts for me
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Training Data Contamination and Model Self-Knowledge Limitations
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Loads of models do that, because their training data included examples of text that included ChatGPT output Directly asking a model questions about itself remains a surprisingly ineffective way of learning anything useful about the model