I do fine tuning with pytorch, and RL-forms of distillation with Tinker! I hope that helps. I work with a full stack of models that enable agentic AI: embedders, rerankers, LLMs, decision models.
I'm genuinely curious about this topic and love learning. My fundamental belief is that it makes sense to build highly tuned models that specialized in specific areas and using orchestration on top to call out to these specialized models with a "shared memory". This would allow for the ability to improve parts of the system without the fear of regression in other areas and it would be cheaper to create a smart "platform". I know there's models with MoE etc but feel like the "microservices" approach (micromodels) would work well here.
For me, training data comes from agent runs.
I do fine tuning with pytorch, and RL-forms of distillation with Tinker! I hope that helps. I work with a full stack of models that enable agentic AI: embedders, rerankers, LLMs, decision models.
Curious, why are you asking?
I'm genuinely curious about this topic and love learning. My fundamental belief is that it makes sense to build highly tuned models that specialized in specific areas and using orchestration on top to call out to these specialized models with a "shared memory". This would allow for the ability to improve parts of the system without the fear of regression in other areas and it would be cheaper to create a smart "platform". I know there's models with MoE etc but feel like the "microservices" approach (micromodels) would work well here.
I could be totally wrong.