31–40 of 141 posts
The point that the major labs don’t seem to get is that the vast majority of use cases simply don’t need models that have 50 PhDs and can speak 12 languages. Most use cases are defined within constraints where costs matter a lot. As open weight models and cheap fine tuning services become the norm the whole economic framework of these mega models the labs are in an arms race building just completely crumbles. As does…
Tool calling is an example - in some tasks RAG works really well, including coding agents where code, git log, Epics/Tasks, dependencies sources, etc. are all available in very structured manner. You can save many extra tool calls if you can run separate prompts and retrieve the source data needed for the actual work - rather its prompt.
And I really want to focus on search - this is the key technology if we want to use RAG instead of fine-tuning. If we can present really contextual sources in the prompts using a hybrid search approach - you can see how easily we get better results - either decisions or summaries from even small models.
What I really started to notice is that SOTA models are really good at putting themselves out of the job. We can see this already with GPT how luna can do 90% of what sol is used for. The only reason why china still bothers 'distilling' models is accurate training data generation, something that oai and anthropic had to spend years collecting while trying to dodge legal challenges. The more intelligent models get, th…
The upper end is all about coding. If I have terra on extra high write code, Sol will find a plethora of bugs and rip the code apart. Anything else? Sure use a cheaper model.
The only cases where weaker models fail entirely and the frontier models really come through is when you have a non-obvious bug in a larger codebase - one that requires tracing lot of calls through the ast (especially in multithreaded code) to understand what might be going wrong.
The point that the major labs don’t seem to get is that the vast majority of use cases simply don’t need models that have 50 PhDs and can speak 12 languages. Most use cases are defined within constraints where costs matter a lot. As open weight models and cheap fine tuning services become the norm the whole economic framework of these mega models the labs are in an arms race building just completely crumbles. As does…
what are those cheap fine-tuning services these days?
What I really started to notice is that SOTA models are really good at putting themselves out of the job. We can see this already with GPT how luna can do 90% of what sol is used for. The only reason why china still bothers 'distilling' models is accurate training data generation, something that oai and anthropic had to spend years collecting while trying to dodge legal challenges. The more intelligent models get, th…
The point that the major labs don’t seem to get is that the vast majority of use cases simply don’t need models that have 50 PhDs and can speak 12 languages. Most use cases are defined within constraints where costs matter a lot. As open weight models and cheap fine tuning services become the norm the whole economic framework of these mega models the labs are in an arms race building just completely crumbles. As does…
I think they understand it but they think they can get away with it because they own the narrative. As long as they can make people believe they need such a model, it doesn't matter if it's true or not.
They are playing the cloud playbook, it didn't matter that most companies didn't need 99,999% uptime and instantaneous horizontal scaling, as long as people believed they did they are happily paying 10-100x the cost to AWS instead.
The problem i've had with finetuning models is that most of the time better prompting beats finetuning