Nicolay Gerold

How much do you think we’re going to get tons of specialization versus no, once the brain gets big enough and we do these fine tunings, that’s going to be it. It will be like AWS, GCP, and Azure.

Ali: I think the answer is closer to the latter. It’s going to have lots of specialization. Having said that, it’s not a dichotomy in the sense that maybe ... See more

• Query the RAG anyway and let the LLM itself chose whether to use the the RAG context or its built in knowledge


• Query the RAG but only provide the result to the LLM if it meets some level of relevancy (ie embedding distance) to the question


• Run the LLM both on it's own and with the RAG response, use a heuristic (or another LLM) to pick the best answer

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If you made a thousand versions of an LLM, that’s good at a thousand different things, and you have to load each of those into the GPUs and serve them, it becomes very expensive. The big holy grail right now that everybody’s looking for is: are there techniques, where you can just do small modifications where you can get really good results? There

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Remote Development Environments for every developer

Develop and debug locally while running environments remotely in the cloud, and see your changes live, precisely as they would look in production... See more

LLMTuner

LLMTuner: Fine-Tune Llama, Whisper, and other LLMs with best practices like LoRA, QLoRA, through a sleek, scikit-learn-inspired interface.

We are excited to release the first version of our multimodal assistant Yasa-1, a language assistant with visual and auditory sensors that can take actions via code execution.

We trained Yasa-1 from scratch, including pretraining base models from ground zero, aligning them, as well as heavily optimizing both our training and serving infrastructure.

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