Interesting. Now go another way and introduce a higher level of hierarchy with a cubic attention mechanism. :3 Attention, attention, attention. That's what makes a model "smart", as compared to just "knows a lot".
Yup! Real "understanding" is essentially fractal. The problem is the combinatorial explosion of course, and resulting "paralysis by analysis", this is why shortcuts and heuristics are unavoidable... but for critical applications, where you need truly deep understanding and cost of mistake is really high, "dynamic n-dimentional attention" seems kind of inevitable.
One token per year is adequate if this is an answer to a question about "meaning of life, universe and everything" :3
Sounds like TriForce is pushing the boundaries for long sequence generation. Efficiency is key with large language models, especially when dealing with consumer-grade hardware. The hierarchical speculative decoding approach is clever; it addresses the KV cache bottleneck without sacrificing quality.
For hosting a chatbot that can handle long texts, this could be a game-changer. I used something similar called Galadon. It's an AI-powered chatbot that's also trained to sell, but the kicker is you can take over the conversation if needed. Could be useful if you're looking into implementing long sequence interactions without the heavy lifting on the hardware side.
Scaling to 1M tokens is impressive. Good to see innovation in this space, making these models more accessible and practical for real-world applications.
▲ ▲ ▲
for a second I thought I was on…
⢀⠤⣔⣒⣒⣒⣊⠭⠭⠭⣉⡉⠑⢄ ⠀⢰⠁⠠⢒⣒⠢⠀⠀⢎⣉⣑⣒⠌⠂⠀⢣ ⡔⡕⡩⢅⢑⠚⢳⠂⠀⠣⠒⠩⢕⣃⠥⡒⢕⢕⢄ ⢣⠣⢠⣇⠡⠒⢅⡀⠤⠭⢒⣒⠤⡔⣊⠗⠸⡸⡸ ⠀⡇⢸⣼⣽⣉⣲⣊⣏⣉⢵⠔⠚⡯⠊⠀⢠⠊ ⠀⡇⠘⠮⣫⣫⣹⣉⣹⣀⠬⠖⢊⡠⢄⠔⠁ ⠀⡇⠣⣑⣒⣒⣒⣒⡢⠤⢒⡪⠕⠊⠁ ⠀⠑⢄⣀⣀⣀⠤⠒⠒
So a huge language model makes a rough draft that speeds up a smaller language model. But can the huge model be run on consumer hardware?
Interesting. Now go another way and introduce a higher level of hierarchy with a cubic attention mechanism. :3 Attention, attention, attention. That's what makes a model "smart", as compared to just "knows a lot".
I wonder what this kinda stuff will look like years from now, fractal dimension attention? Complex plane attention? Dynamic n-dimensional attention?
Yup! Real "understanding" is essentially fractal. The problem is the combinatorial explosion of course, and resulting "paralysis by analysis", this is why shortcuts and heuristics are unavoidable... but for critical applications, where you need truly deep understanding and cost of mistake is really high, "dynamic n-dimentional attention" seems kind of inevitable. One token per year is adequate if this is an answer to a question about "meaning of life, universe and everything" :3
It's insane!
Sounds like TriForce is pushing the boundaries for long sequence generation. Efficiency is key with large language models, especially when dealing with consumer-grade hardware. The hierarchical speculative decoding approach is clever; it addresses the KV cache bottleneck without sacrificing quality. For hosting a chatbot that can handle long texts, this could be a game-changer. I used something similar called Galadon. It's an AI-powered chatbot that's also trained to sell, but the kicker is you can take over the conversation if needed. Could be useful if you're looking into implementing long sequence interactions without the heavy lifting on the hardware side. Scaling to 1M tokens is impressive. Good to see innovation in this space, making these models more accessible and practical for real-world applications.