It’s pretty stupid actually, I asked it to decode an ascii sequence and it gave me jiberish. I tried phi-4 Q4 and it got it. 9gb vs 2gb(reasoning). I imagine you just cannot fit enough information in 2gb to be of much use other than solving general math problems or knowing what training data was in it. It gave it a good go 2min on a m2 vs phi4 which was instant.
DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL
71–80 of 137 posts
Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL
#72Does 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 man…
Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL
#73Does 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…
I have two. One is a simple one that only deepseek R1 has passed (in my opinion): I have a 12 liter jug and a 6 liter jug. How do I get exactly 6 liters of water? Answer (Deepseek): Fill the 6-liter jug completely to obtain exactly 6 liters of water. Every other LLM I've tried include o3-mini-high: Fill the 12-liter jug completely. Pour it into the 6 liter jug. Although o3 did get it right in the reasoning: It seems…
It told me to fill the 12 liter jug then pour that into the 6 liter jug.
I then scolded it and told it I was going to use Deepseek R1 because it did a better job, and we were shutting down OpenAI forever, and THEN it thought for 4 minutes and got a bit snippy with me haha, it got the answer but then glossed over it also said “but that’s too simple isn’t it?”.
Output: Here’s a more “classic puzzle” style approach, spelled out step-by-step, to end up with exactly 6 liters in the 12-liter jug. This method uses multiple fills and transfers (instead of the trivial “fill the 12, pour off 6” approach) so you can see a more puzzle-like sequence: 1. Fill the 6-liter jug from the water supply. • State: (12-liter jug = 0 L, 6-liter jug = 6 L) 2. Pour from the 6-liter jug into the 12-liter jug until the 6-liter jug is empty (the 12-liter jug still has plenty of room). • State: (12-liter jug = 6 L, 6-liter jug = 0 L) 3. Fill the 6-liter jug again from the water supply. • State: (12-liter jug = 6 L, 6-liter jug = 6 L) 4. Pour from the 6-liter jug into the 12-liter jug until the 12-liter jug is completely full. • The 12-liter jug currently has 6 liters and can take 6 more liters before it’s full, so you’ll pour the entire 6 liters from the 6-liter jug. • State: (12-liter jug = 12 L, 6-liter jug = 0 L) 5. Empty the 12-liter jug (discard the water or set it aside). • State: (12-liter jug = 0 L, 6-liter jug = 0 L) 6. Transfer the remaining water in the 6-liter jug to the 12-liter jug—but right now the 6-liter jug is empty, so that doesn’t help. The trick is: we want to exploit the difference in capacities to measure out new amounts. So let’s continue. Since both are empty, let’s do something else: a. Fill the 12-liter jug from the supply. • State: (12-liter jug = 12 L, 6-liter jug = 0 L) b. Pour from the 12-liter jug into the 6-liter jug until the 6-liter jug is full, which removes 6 liters from the 12-liter jug. • Now the 12-liter jug has liters left. • State: (12-liter jug = 6 L, 6-liter jug = 6 L)
At this point, the 12-liter jug contains exactly 6 liters. That’s what we wanted.
If You Want the 6 Liters in the Smaller Jug Instead
If, for some reason, you need exactly 6 liters in the 6-liter jug (though that jug obviously holds 6 liters when full, so it’s usually trivial), here’s a more puzzle-like sequence: 1. Fill the 12-liter jug (12 L in the big jug, 0 L in the small jug). 2. Pour from the 12-liter jug into the 6-liter jug until the small jug is full. • State: (12-liter jug = 6 L, 6-liter jug = 6 L) 3. Empty the 6-liter jug. • State: (12-liter jug = 6 L, 6-liter jug = 0 L) 4. Transfer the 6 liters from the 12-liter jug into the 6-liter jug. • State: (12-liter jug = 0 L, 6-liter jug = 6 L)
Again, you end up with exactly 6 liters in the 6-liter jug.
Both sequences go beyond the simple “fill the 12, pour 6 off” short-cut. Hopefully, that’s less “terrible” and shows a more classic multi-step approach often seen in water-jug riddles.
Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL
#74Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL
#75im very naive here but does anyone trust these benchmarks? do they mean anything to you? they seem far too easy to game and it doesn't feel like its an accurate way to really tell how these models compare to one another. seems like benchmark performance declines quite a bit if you introduce a problem that's similar to those in benchmarks but one that the model hasn't seen before
> Problems near identical to the test set can be found online.
Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL
#76Does 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…
Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL
#77Does 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…
Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL
#78Smaller reinforced models are what will win. Look at our civilization, your company, heck even your team: lots of specialized folks, not some singular bloated mastermind. The issue though is the overemphasis on the current benchmarks. Ideally the thing benchmarked is against user kpis.
Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL
#79Earlier quoted context omitted.
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.
for now, they're not doing that but just releasing new models that also have updated information
there is one group thats scraping the latest information and including it in their latest models, another group thats only doing synthetic data sets from the prior group's models, and every enthusiast everywhere making fine tuned LLMs with synthetic data sets and whatever niche they want
Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL
#80Earlier quoted context omitted.
Try bfloat16! We have a bug where the model was saved as fp32.
I just tried it with this 3.6GB F16 model: ollama run hf.co/bartowski/agentica-org_DeepScaleR-1.5B-Preview-GGUF:F16 And this time it didn't get confused with the tokenization of strawberry! https://gist.github.com/simonw/9e79f96d69f10bc7ba540c87ea0e8...