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Lexi v2.0 will be releasing soon, likely today or tomorrow. New model architecture and more parameters. Coming down the pipe. PDF/DOC analysis, code execution (beta), a more polished chat UI.

If you had issues with Lexi refusing innocuous or innocent prompts before, it's because we are using an abliterated model (no guardrails), and in order to make sure we minimize the risk of people being able to do bad shit (generate CSAM, get bomb making instructions, et cetera), I had to manually add refusals back in for the egregious stuff. Unfortunately, I used a hammer rather than a scalpel.

This should be fixed mostly in Lexi v2, although considering the political implications and attack surface, I've leaned a little more towards the "safer" side. I've built a good deal of infrastructure to mitigate the worst issues via defense through depth, but as with most other things, it's a matter of when, not if. We just have to make sure the "when" is very far down the road.

Lexi's new reasoning capabilities will allow her to more effectively categorize your requests and answer them with the appropriate context, rather than wigging out and flying off the handle at something unrelated because she hallucinated certain keywords that trip the alarms.

Overall, this will be a substantial improvement over v1.0, not just in behavior, but in raw capability as well.

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@matty @anathema_ai it's hard to say, it kind of depends on why you want guardrails/abliterated models or whatever.

Basically, no matter what guardrails and monitors you put in, the LLM will return stuff that is wrongthink to *someone*, and if that person has political power, well...

It's more "why bother" - both to setting up a public service; as well as making it so it doesn't effect you personally with fallout from being trained on human text....

> code execution (beta)

Is this where the bot has the ability to say something like __RUN_CODE__: <some arbitrary code> and the system will interpret that and send the result back to the bot, so the bot doesn't need to do complex math "in it's head" ?

Yeah, we do that through tool calls. Different models have different tool call architectures. But, to get the model to understand when/how to use the tool call and the format of its input, you have to LoRA train it with examples.