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DeepSeek Introduces Vision

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Re: DeepSeek Introduces Vision

#191

Post by cocoflunchy »

ForceBru wrote: post #89
cocoflunchy wrote: post #43

Earlier quoted context omitted.

I believe that most reasoning models actually think in their own "language" which is not really understandable by humans. The thinking traces that are shown in the UI are actually summaries generated by a smaller model in plain english (or user language). Sometimes this leaks through and you see some chinese/japanese characters in e.g. Claude's reasoning.

Wait, this isn't real, is it? Is there actually an intermediate model that translates DeepSeek's thinking from its "alien language" into human languages? That's not actually the case, right? I thought "thinking" is literally the model generating additional text in a human language that shows its "thought process". It's added to the model's context, which helps it reason better because it now has this self-generated c…

I may have misremembered but I thought I had read somewhere that recent models by OpenAI and Anthropic tend to produce reasoning that is not always understandable for humans. But you're right that it's not the case for Deepseek so maybe I'm hallucinating ;)

Or maybe it was an article or a tweet about researchers trying really hard to steer the model to think in English otherwise interpretability / safety becomes a lot harder?

Re: DeepSeek Introduces Vision

#193

Post by bonoboTP »

bigmadshoe wrote: post #172
bonoboTP wrote: post #167

Earlier quoted context omitted.

CNNs are fine when trained with a good recipe. There are very few good studies comparing them with proper hyperparam search and all the training tricks applied consistently. Transformers are good but ViT vs CNN is not some settled issue. Transformers are more hyped and more popular with the tech enthusiasts who just read forums and news, but if you need stuff done, CNNs are still great.

I agree, but since we're talking about imagine understanding with text output, clearly a CNN is unsuitable. My previous comment was overly reductive and CNNs can still be SoTA depending on your performance metrics. I spent the earlier part of my career training CNNs, and they are very pleasant to work with.

You can run a CNN and use the downsampled feature map the same way as patch tokens.

Re: DeepSeek Introduces Vision

#194

Post by bonoboTP »

famouswaffles wrote: post #184
bonoboTP wrote: post #167

Earlier quoted context omitted.

CNNs are fine when trained with a good recipe. There are very few good studies comparing them with proper hyperparam search and all the training tricks applied consistently. Transformers are good but ViT vs CNN is not some settled issue. Transformers are more hyped and more popular with the tech enthusiasts who just read forums and news, but if you need stuff done, CNNs are still great.

>Transformers are more hyped and more popular with the tech enthusiasts who just read forums and news, but if you need stuff done, CNNs are still great. Vits are straight up more popular for ML research now, it's not just 'tech enthusiasts'.

There's a dearth of research properly comparing them.

Re: DeepSeek Introduces Vision

#195

Post by paulcole »

echelon wrote: post #131
exabrial wrote: post #128

The product I want most is the ability to return to the late January 2026 version of Anthropic models.

This is why we need open weights for everything. Nobody will cry when their AI girlfriend model gets revoked. You'll always have the weights. Presumably for the low cost of spinning up an H200 or two you can use the weights forever. No more claiming your LLM gets nerfed. No more claiming your video model can't do Spider-Man anymore.

> Nobody will cry when their AI girlfriend model gets revoked

These are the people who cry the loudest and there’s not a close second. They have infinite time to whine online (see /r/chatgpt after 4.5 went away).

Re: DeepSeek Introduces Vision

#196

Post by brianjking »

johnvanommen wrote: post #182
freedomben wrote: post #152

Earlier quoted context omitted.

Indeed, Gemini really is incredible at image analysis. Yesterday I pointed it at some sloppy handwritten notes and asked it to add up the numbers in the right column, and it did it no problem. I've also used it to find out what TV show or actor is on screen, and various other things. It's quite impressive.

> Indeed, Gemini really is incredible at image analysis. Yesterday I pointed it at some sloppy handwritten notes and asked it to add up the numbers in the right column, and it did it no problem. I've also used it to find out what TV show or actor is on screen, and various other things. It's quite impressive. I do not know if it works as well as Gemini, but Salesforce (of all places) has a model that does something si…

There is a newer BLIP-2, but it's also fairly old. You're better off with many other local models such as Moondream 3 https://huggingface.co/moondream/moondream3-preview.

Moondream is great as it can point, count, perform bounding boxes, descriptions, and visual grounded reasoning.

Re: DeepSeek Introduces Vision

#197

Post by famouswaffles »

bonoboTP wrote: post #194
famouswaffles wrote: post #184

Earlier quoted context omitted.

>Transformers are more hyped and more popular with the tech enthusiasts who just read forums and news, but if you need stuff done, CNNs are still great. Vits are straight up more popular for ML research now, it's not just 'tech enthusiasts'.

There's a dearth of research properly comparing them.

I'm talking about research pushing state of the art in computer vision. Vits have 100% become more popular than CNNs in most CV research.

Re: DeepSeek Introduces Vision

#198

Post by vitorgrs »

eckr wrote: post #139
vitorgrs wrote: post #79

I already had it for months? What's the news here?

In the past, they just ran Deepseek OCR on your image and extracted the text, then gave it to a language only model. I believe now there is a model that actually takes images as input directly.

Talking about the vision... I already had the vision tab there hahahaha I guess everything in tech these days are A/B...
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