duchenne

I am a long-time user of stackoverflow with 16k points, and even I got all my questions of the last five years downvoted into oblivion.

Nuclear-powered ion thrusters could solve this issue. They provide low acceleration for a long time consuming very little consumable. This would allow the telescope to stay at the right position for observation.

Yes, it would work.

I am working in Mistral robotics team. I confirm this is map-less. The only inputs are the text prompt and the front camera rgb image.

Cloud models can use batch processing which is significantly more efficient. A local model has basically a batch of one which takes as much time to process as a batch of 100 because the gpu is memory bound and spend most of its time loading the model from vram to the gpu cache while the gpu cores are idle. With a batch of 100 the model loading time and compute time are roughly similar. So local Models have a first 100x lower efficiency. Secondly, local models are idle most of the time waiting for the user to write a prompt, so the efficiency gap is probably more around 1000x.

I have done that at meta/FAIR and it is published in the Llama 3 paper. You usually start from a seed. It can be a randomly picked piece of website/code/image/table of contents/user generated data, and you prompt the model to generate data related to that seed. After, you also need to pass the generated data through a series of verifiers to ensure quality.

Looks awesome. Can we get the same thing for pytorch?

There is a manga/anime about this: doctor stone.

For the knowledge preservation, I guess that a copy of deepseek has most of the required information. But, it would be hard to run it in a primitive world.

But we have landfills which are full of great raw materials. I would argue that it is easier to collect steel from a landfill than from a mine during the industrial revolution.

I had one for years. Never had overheating issues, except if I put it on my blanket for long.

Come on... Meta has been refining pytorch for more than a decade. It basically contains all that you need to train LLMs, including the latest technologies. What more do you need? The part of the code that is specific to Meta infrastructure?

The reasoning happens in the chain of thoughts. But OpenAI (aka ClosedAI) doesn't show this part when you use the o1 model, whether through the API or chat. They hide it to prevent distillation. Deepseek, though, has come up with something new.

Training a 1B model on 1T tokens is cheaper than people might think. A H100 GPU can be rented for 2.5$ per hour and can train around 63k tokens per second for a 1B model. So you would need around 4,400 hours of GPU training costing only $11k And costs will keep going down.