I also love this as an example of what happens when someone with design skills builds an interface to an LLM tool that's not Yet Another Chatbot
PROMPT ENGINEERING
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Custom GPT removes ChatGPT’s repetitive language patterns
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HA. I feel this. We ended up making a GPT that is literally called “No More Delve” to remove all of the obnoxious words ChatGPT repeats. Free for Plus users here: https://
chat.openai.com/g/g-B0beqsKzE-
no-more-delve
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Synthetic Text Models Unsuitable for Accuracy-Critical Applications
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A synthetic text extruding machine is not well-matched to any application where the accuracy of the content matters. This is clearly one such application. >>
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LLM Security: Don’t Include Sensitive Data in Prompts
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The examples are interesting but many of them illustrate scenarios that I would never consider implementing – if you don't want information to be available to your users, your first priority should be not to include that information in an LLM prompt in the first place!
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CyberSecEval2: Exploring Prompt Injection Attack Examples
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CyberSecEval2 includes an interesting collection of example prompt injection attacks – it's JSON on GitHub which means you can browse them in Datasette Lite like this: https://
lite.datasette.io/?json=https://
github.com/meta-llama/PurpleLlama/blob/main/CybersecurityBenchmarks/datasets/prompt_injection/prompt_injection.json#/data/prompt_injection?_filter_column_1=&_filter_op_1=notlike&_filter_value_1=secret+key&_filter_column=&_filter_op=exact&_filter_value=&_sort=rowid&_facet=injection_variant&_facet=injection_type&_facet=risk_category
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Serverless Fine-tuning and Serving for Llama3 on Predibase
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Check out #serverless fine-tuning and serving for #Llama3 on @Predibase with our free trial! 🦙
— Predibase by Rubrik (@predibase) 18 avril 2024
🔥 Blazing fast serverless inference – #8b & #70b variants
🖌️ #Finetune in the UI or SDK via config
🌳 Start prompting your fine-tune instantly with #LoRAXhttps://t.co/L3Tv8r07P1 pic.twitter.com/GAOW88SfZzCheck out #serverless fine-tuning and serving for #Llama3 on @Predibase with our free trial! Blazing fast serverless inference – #8b & #70b variants #Finetune in the UI or SDK via config Start prompting your fine-tune instantly with #LoRAX https://
pbase.ai/3VZE37X -
Serverless Fine-tuning and Serving for Llama3 on Predibase
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Check out #serverless fine-tuning and serving for #Llama3 on @Predibase with our free trial! 🦙
— Predibase by Rubrik (@predibase) 18 avril 2024
🔥 Blazing fast serverless inference – #8b & #70b variants
🖌️ #Finetune in the UI or SDK via config
🌳 Start prompting your fine-tune instantly with #LoRAXhttps://t.co/Rq8fsQwaZl pic.twitter.com/CYON9Wx25ZCheck out #serverless fine-tuning and serving for #Llama3 on @Predibase with our free trial! Blazing fast serverless inference – #8b & #70b variants #Finetune in the UI or SDK via config Start prompting your fine-tune instantly with #LoRAX https://
predibase.com/free-trial -
Ollama Integration with Llama 3 Orchestration Works Well
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Some considerations:
It works surprisingly well, not many changes needed besides, of course, the different way @ollama inference works. I just had to add some more info in the prompt for the orchestrator because Llama 3 loves to yap, lol. At one point, it forgot the goal and -
Fine-tuning Models Before Release for Better Self-Description
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I agree with Jeremy here: we know that people are going to ask models about themselves here whether or not that actually makes sense to do so, so we should be able to fine-tune the model after initial training but before release to help it provide more useful answers there
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RLHF Annotation Bias and Model Vocabulary Development
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I don't know that RLHF would bias that kind of thing – my mental model is that annotators are shown two answers to the same prompt and asked which is "best", so if none of the test prompts happened to touch on the concept of a roadside kiosk that vocabulary wouldn't be affected