Am I missing something here. Not finding a major bug/vulnerability just means that maybe the code is really good, not that the model is not what is claimed?
Akamai CTO Robert Blumofe offers four tips for business leaders striving to foster AI fluency by empowering employees with the right tools and best use cases.
As I understand, the LLM uses the techniques of searchformer - https://arxiv.org/abs/2402.14083. To do "slow thinking" doing a A* search using a transofrmer.
TLDR: Don’t buy H100s. The market has flipped from shortage ($8/hr) to oversupplied ($2/hr), because of reserved compute resales, open model finetuning, and decline in new foundation model co’s. Rent instead.
I know you mean this in jest, but we are much closer to this than we would imagine, the use of LLMs to process communication / translation is becoming ubiquitous. We are 1 bad translation away from a disaster.
I also use some email providers ability to have +xyz at the end of the username. So for a registration I would for user.name+sitea@domain.com. has helped me track spam and leaks in the past.
I just checked huggingface and the model files download is about 25GB but in a comment below someone mentioned it is 8fp quantized model. Trying to understand how the quantization affects the model (and RAM) size. Can someone please enlighten.
I have experience using the Motofone - https://en.wikipedia.org/wiki/Motorola_Fone . It was not the best of experience especially due to the lag which many have mentioned here. E-Ink displays have come a long way, hoping this time it is a better experience.
This is about managing security risks of using OSS libraries. Many organizations end up using OSS code/libs/products without having the capability or the motivation to validate them. Google addresses some of those issues with this.
As the responses are non-deterministic I wonder how useful these tests are? Even if I did the tests with my own prompts, wouldn't I get different answers for the same questions at different points in time?