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DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

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Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#12
post #4

Newb question. If the big boys are crawling, scraping, stealing the whole of the entire internet to train their models — how are these OS alternatives aiming to outperform them? Is it as simple as... ALSO crawl, scrape, steal the whole of the entire internet?

"Stealing the whole of the entire internet" isn't a particularly accurate mental model these days.

That's more or less what people were doing back in 2023 - crawling everything and dumping as much data in as possible.

It's not a great strategy to build a best-in-class model though, as a lot of the internet is junk. The SolidGoldMagikarp/davidjl bug is the kind of thing that happens if you crawl all of https://www.reddit.com/r/counting/ for example: https://simonwillison.net/2023/Jun/8/gpt-tokenizers/#glitch-...

These days model training labs are more selective about what they train on. Most of the game of training a great model comes down to selectively training your data. They still use a lot of unlicensed data but it's a bit more sophisticated than just dumping in everything they can find.

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#13
post #10

Does anyone have any good simple prompts for testing new "reasoning" models like this one? "Count the letter Rs in the word strawberry" is a bit dull! I'm trying this one locally using Ollama and the smallest quantized GGUF version (769MB) I could find - https://huggingface.co/bartowski/agentica-org_DeepScaleR-1.5... - I ran it like this: ollama run hf.co/bartowski/agentica-org_DeepScaleR-1.5B-Preview-GGUF:IQ3_XXS He…

The negative quality impact of quantization is more pronounced for smaller models [0], so I'm surprised this tiny quant works at all.

[0] or rather models closer to saturation, which is a function of model params and amount of training

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#14
post #10

Does anyone have any good simple prompts for testing new "reasoning" models like this one? "Count the letter Rs in the word strawberry" is a bit dull! I'm trying this one locally using Ollama and the smallest quantized GGUF version (769MB) I could find - https://huggingface.co/bartowski/agentica-org_DeepScaleR-1.5... - I ran it like this: ollama run hf.co/bartowski/agentica-org_DeepScaleR-1.5B-Preview-GGUF:IQ3_XXS He…

... well that's weird, I got exactly the same weird bug on the 7.1 GB F32 GGUF version: https://gist.github.com/simonw/58ff74a55b402dc55764a567b10ec...

  % ollama run hf.co/bartowski/agentica-org_DeepScaleR-1.5B-Preview-GGUF:F32
  pulling manifest 
  pulling 84159ad584aa... 100%  7.1 GB                         
  pulling 8aa4c0321ccd... 100%   493 B                         
  pulling c737442a210a... 100%   170 B                         
  pulling ed76df87b934... 100%   193 B                         
  verifying sha256 digest 
  writing manifest 
  success 
  >>> Count the letter Rs in the word strawberry
  
  Okay, so I need to count how many times the letter 'r'
  appears in the word "strawberry." Hmm, let's break this down
  step by step. First, I should probably spell out each letter
  of the word to make sure I don't miss any 'r's.

  Starting with "strawberry." Let me write it out: S, T, R, A,
  W, B, E, R, F, U, R, Y? Wait, maybe I'm not spelling it
  correctly. Actually, let's count each letter properly.

  The word is spelled as follows:
  S T R A W B E R F U R Y. Wait, no, that might be longer than
  needed. Let me write it out more accurately:

  The full word is "strawberry." So breaking it down:
  1. S
  2. T
  3. R
  4. A
  5. W
  6. B
  7. E
  8. R
  9. F
  10. U
  11. R
  12. Y
Update: running the F16 model fixed that problem:

  ollama run hf.co/bartowski/agentica-org_DeepScaleR-1.5B-Preview-GGUF:F16
See https://news.ycombinator.com/item?id=43017599#43018767 and https://gist.github.com/simonw/9e79f96d69f10bc7ba540c87ea0e8...

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#15
post #9
post #4

Newb question. If the big boys are crawling, scraping, stealing the whole of the entire internet to train their models — how are these OS alternatives aiming to outperform them? Is it as simple as... ALSO crawl, scrape, steal the whole of the entire internet?

No, fine tuning doesn't require that and models are using synthetic data sets now, for like 2 years as in, the huge models did the crawling and then the next models had conversations with the huge models oversimplified but basically the scraping ship has sailed, artists/writers will never be satisfied, and other companies bragging about using whatever aspirational goal post of licensed material are just doing it for…

Does this mean the model of LLM = search engine is also coming to an end? Ignoring things like functions and google search capabilities, current chatgpt models have quite a lot of niche knowledge, I assume from crawling the entire internet.

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#16
post #13
post #10

Does anyone have any good simple prompts for testing new "reasoning" models like this one? "Count the letter Rs in the word strawberry" is a bit dull! I'm trying this one locally using Ollama and the smallest quantized GGUF version (769MB) I could find - https://huggingface.co/bartowski/agentica-org_DeepScaleR-1.5... - I ran it like this: ollama run hf.co/bartowski/agentica-org_DeepScaleR-1.5B-Preview-GGUF:IQ3_XXS He…

The negative quality impact of quantization is more pronounced for smaller models [0], so I'm surprised this tiny quant works at all. [0] or rather models closer to saturation, which is a function of model params and amount of training

Yeah I'm very impressed that the tiniest quant version appears partially usable... especially now I've replicated that same S, T, R, A, W, B, E, R, F, U, R, Y bug on a much larger one: https://news.ycombinator.com/item?id=43018494

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#17
post #4

Newb question. If the big boys are crawling, scraping, stealing the whole of the entire internet to train their models — how are these OS alternatives aiming to outperform them? Is it as simple as... ALSO crawl, scrape, steal the whole of the entire internet?

[dead]

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#19
post #10

Does anyone have any good simple prompts for testing new "reasoning" models like this one? "Count the letter Rs in the word strawberry" is a bit dull! I'm trying this one locally using Ollama and the smallest quantized GGUF version (769MB) I could find - https://huggingface.co/bartowski/agentica-org_DeepScaleR-1.5... - I ran it like this: ollama run hf.co/bartowski/agentica-org_DeepScaleR-1.5B-Preview-GGUF:IQ3_XXS He…

Is it a quantisation or tokenisation problem?
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