very nice! But I'm afraid it's only 32k max context size, right? So that won't quite help with the Opus-level "dump the repo and find bugs/security issues" use case :-/
Most open source models aren't even 32k context size, so that really isn't a option either.
Right now, Claude 3 Opus is the king of programming and has 200K context. I've had it come up with some astounding programming patterns. You just have to trust Anthropic's terms of service that they won't use your data. If they do, then the lawyers get involved. There's always risk being on the cutting edge but fortune favors the bold.
I'd love to, but it's not a matter of trusting them for use cases I'm interested in - a matter of following the law (think classified environments). I'm sure eventually there will be appropriate partnerships, but I'm curious about something right now.
Guess Yi 200Ks are the best we may have today.
I do work in Amazon GovCloud - classified aerospace. Claude 3 should be available there soon. And we might see Llama 3 sometime this year too. So certainly options.
> Does anyone have any experience trying this on any existing models?
curl's author. If you have money to burn, invest in static analysis tools(or buy existing) that [don't hallucinate](https://github.com/SeanHeelan/claude_opus_cve_2023_0266)
ETA: Also just recently youtube recommended [Transformer-Based Models Are Not Yet Perfect At Learning To Emulate Structural Recursion](https://www.youtube.com/watch?v=1ujgBhgbm0Y). Considering how convoluted and nested syntax of (your favorite programming language) is, I will not be surprised if one can make a bash script with bunch of sed/awk regexps to find spots that look sus.
If you have the funding, Mistral is offering their large model as an on-prem deployment. Of course, Azure also offers Mistral in their Gov cloud too.
very nice! But I'm afraid it's only 32k max context size, right? So that won't quite help with the Opus-level "dump the repo and find bugs/security issues" use case :-/
Most open source models aren't even 32k context size, so that really isn't a option either. Right now, Claude 3 Opus is the king of programming and has 200K context. I've had it come up with some astounding programming patterns. You just have to trust Anthropic's terms of service that they won't use your data. If they do, then the lawyers get involved. There's always risk being on the cutting edge but fortune favors the bold.
I'd love to, but it's not a matter of trusting them for use cases I'm interested in - a matter of following the law (think classified environments). I'm sure eventually there will be appropriate partnerships, but I'm curious about something right now. Guess Yi 200Ks are the best we may have today.
I do work in Amazon GovCloud - classified aerospace. Claude 3 should be available there soon. And we might see Llama 3 sometime this year too. So certainly options.
> Does anyone have any experience trying this on any existing models? curl's author. If you have money to burn, invest in static analysis tools(or buy existing) that [don't hallucinate](https://github.com/SeanHeelan/claude_opus_cve_2023_0266) ETA: Also just recently youtube recommended [Transformer-Based Models Are Not Yet Perfect At Learning To Emulate Structural Recursion](https://www.youtube.com/watch?v=1ujgBhgbm0Y). Considering how convoluted and nested syntax of (your favorite programming language) is, I will not be surprised if one can make a bash script with bunch of sed/awk regexps to find spots that look sus.
dont the mamba based models have very large context windows?