u/Timotheeee1
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22points
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38points
Who needs artillery (i.redd.it)
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16points
Hard Mode Marcus & Faleris beaten in 10 seconds (Credit: The Pal Professor) (v.redd.it) [Video]
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111points
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26points
Base building be like (i.redd.it) [Meme]
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247points
GPT-4.5 cost (i.redd.it) [New Model]
Timotheeee1
Timotheeee1
from a large stockpile built up over ages
Timotheeee1
it's just the kerafactor cap but cheaper
Timotheeee1
this is an LED cap, which is useless
Timotheeee1
Timotheeee1
usually yes, because the web versions tend to include enormous system prompts with hundreds of instructions, while the API has none
Timotheeee1
Anthropic shows the first 1k tokens or so, then a summary
Timotheeee1
go to meetup events
Timotheeee1
what happens if you instead use a specialized calibration dataset that contains only code or only english writing? you could probably prune the 235B down quite a lot more and make several specialist models.
Timotheeee1
what will the speed and cost be with a reasonable batch size once available on openrouter?
Timotheeee1
only the boss drops the schematic
Timotheeee1
actually I did build beds after building all of the farms
Timotheeee1
closed-source frontier models can be used to generate high quality data for fine-tuning local models that are specialized in specific tasks. (especially this one as it shows the reasoning traces)
they also provide a preview of the capabilities that open models will likely have in the future.
Timotheeee1
try asking in the unsloth discord
Timotheeee1
no, gradient checkpointing only offloads activations, but my idea would be to additionally offload model weights too
Timotheeee1
It would be cool if when doing QLoRA you could offload some layers of the model to the CPU and have them streamed to the GPU over PCIe as they are needed. in theory this shouldn't make the speed that much worse since PCIe has 63GB/s, enough bandwidth to stream the weights of a 32B 4 times per second, and it usually takes much longer than that do process one batch. this could allow for fine-tuning of larger models on colab and local hardware
Timotheeee1
It was solved a few months ago: https://arxiv.org/pdf/2409.12517v1
Timotheeee1
did you ever manage to fix this?
Timotheeee1
I think your project could still be useful for applying changes in a big file with 20k tokens, as you can't really expect the model to just re-output everything

in about half of these cases the person actually did the test and told me, but fair enough I might be wrong about some of the rest