@Kay2289123: I highly recommend that everyone bookmark this Stanford course from this fall: MS&E 319: Efficient Generative Language …

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This tweet recommends a Stanford course on efficient generative language models, covering techniques from pre-training to inference to balance performance and cost with limited compute.

I highly recommend that everyone bookmark this Stanford course from this fall: MS&E 319: Efficient Generative Language Models The course will go all the way from pre-training to inference and post-training. (My favorite part) Course materials and recordings will be synced and uploaded later I really like its starting point: models keep getting bigger and bigger, but budgets always have limits. Given a specific effect you want to achieve, with limited compute power on hand, how do you set training objectives, choose model architectures, and run inference to make your money go further and avoid using the most expensive model GPT6 for every single task "Buy more GPUs" is of course one approach, and it's also the mainstream in the current market It will cover MoE, attention and KV Cache compression, quantization, speculative decoding, as well as LoRA, RLHF, DPO, and distillation. This course takes these technologies that we usually see scattered around and puts them back into the context of the same problem to understand: exactly which part of computation, memory, or time does it save, and what costs does it incur? For example, when it comes to reducing the cost of using a model in the same way, some methods only call a portion of the parameters each time, some reduce the data that needs to be stored and transported, and some speed up answer generation. First, clarify the specific problems each one solves, and then you'll know which ones are suitable for the tasks at hand, and which methods can be combined together. When I look at AI companies myself, I'm also very focused on this layer: with the same budget, can you train better models, or serve more users. Once you understand the trade-offs behind these technologies, then when you look at model releases and cost changes, your judgments will be much more concrete. This course does have a certain threshold, so if you're interested, be sure to pay attention
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I highly recommend that everyone bookmark this Stanford course from this fall: MS&E 319: Efficient Generative Language Models

The course will go all the way from pre-training to inference and post-training. (My favorite part) Course materials and recordings will be synced and uploaded later

I really like its starting point: models keep getting bigger and bigger, but budgets always have limits. Given a specific effect you want to achieve, with limited compute power on hand, how do you set training objectives, choose model architectures, and run inference to make your money go further and avoid using the most expensive model GPT6 for every single task

“Buy more GPUs” is of course one approach, and it’s also the mainstream in the current market

It will cover MoE, attention and KV Cache compression, quantization, speculative decoding, as well as LoRA, RLHF, DPO, and distillation. This course takes these technologies that we usually see scattered around and puts them back into the context of the same problem to understand: exactly which part of computation, memory, or time does it save, and what costs does it incur?

For example, when it comes to reducing the cost of using a model in the same way, some methods only call a portion of the parameters each time, some reduce the data that needs to be stored and transported, and some speed up answer generation. First, clarify the specific problems each one solves, and then you’ll know which ones are suitable for the tasks at hand, and which methods can be combined together. When I look at AI companies myself, I’m also very focused on this layer: with the same budget, can you train better models, or serve more users. Once you understand the trade-offs behind these technologies, then when you look at model releases and cost changes, your judgments will be much more concrete.

This course does have a certain threshold, so if you’re interested, be sure to pay attention

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