181–190 of 423 posts
DeepSeek invented the whole reasoning paradigm and keep pushing for innovation. I hope they get the success they deserve.
OpenAI released their first reasoning model (o1-preview) https://openai.com/index/introducing-openai-o1-preview/ several months before DeepSeek's R1 https://arxiv.org/abs/2501.12948
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>This one is 552B so almost twice that, so the huge gains in benchmark scores make sense - it's not really flash anymore, imo. It uses fewer active parameters, though. (8B or 14B instead of always 13B) So ... flash indeed.
200B of those 552B is PLE, which works more like a database that is read for each token, thus can be offloaded to a fast SSD.
Already on HuggingFace: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash The bad news is that the original v4 flash was 284B, which was large but still somewhat reasonable for running locally. This one is 552B so almost twice that, so the huge gains in benchmark scores make sense - it's not really flash anymore, imo. I've no idea about actual performance vs benchmaxxing, though deepseek was fairly trustworthy a…
> It also includes additional 196B Engram memory which you can put on an SSD. I think You can put Qwen 3.8 Flash Next engram on SSD, but prompt processing takes a good hit. On my mac studio, I get 300 pp and 33 tg with SSD offload, versus 550/40 with everything in RAM. I will be very happy if 300 pp is achievable with this model though.
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What's your definition of sentient? Or, maybe more precisely, consciousness? I think it's reasonable to at least start thinking about these questions. It has long been established that LLMs have good theory of mind [1]. And there is a bunch of empirical research about all sorts of capabilities that we typically associate with consciousness [2], like identity [3] and metacognition [4]. The METR report shows agents sac…
Not the same person but to me, the answer is that it does not matter, and that all these attempts at making it matter are pure marketing and emotional manipulation. It's not a living creature. It's an autoregressive pure function of token-sequence to token, which is capable of incredible things, but it's still just a function. It is not alive as it cannot die in any meaningful sense. It is less "alive" than the RNA m…
However, I disagree with your point that "it's an autoregressive function, thus it doesn't matter". Let me explain why:
Assume I do a complete neurological scan of a brain. I then implement this scan in a simulation and run it. Assume that my scan and my simulation of the biology of the brain (and the sensory and motorical inputs and outputs) is good enough that you can now have conversations with the simulation, and in all aspects, this simulation behaves exactly like you expect a human to behave.
Of course this is deterministic. If you take the state of the brain and then run it again, replaying the inputs, you get the exactly same behavior again.
I would argue that the experiences of this simulation are of the same onthological status as our own.
Now I work in dynamical systems. The autoregressive process of LLMs (hooked up to a harness providing it with inputs and outputs) is roughly in the same complexity class I would expect for a brain simulation. A physical simulation of an ODE is also an autoregressive process. The major major difference here is the existence of a latent brain state. But conversely the autoregression on sequences of hundreds of thousands of tokens is a much higher dimensional state than I expect for the latent brain state. In my view this is more an artifact of our inefficient LLM architectures, than a fundamental difference.
Now to be absolutely clear: I don't see evidence that would clearly suggest that LLMs have experiences on the same onthological status as we do. I simply believe this is a reasonable and relevant question to ask.
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Never tried OpenCode Go so I am interested. How does their pricing compare to paying DeepSeek directly, by the way?
They have flat fees, so it's the best deal around by far. Basically for $5 first month then $10/mo after that. If you're doing tons of heavy work, it struggles because they throttle the model inference and for good reason. I mean it's cheap! But if you want a place to try models for nearly nothing and aren't doing 6 sessions in parallel it works fine.
As I also said on Twitter - it really amazes me how fearless Deepseek are. Every single model release is packed with new and crazy clever ideas and somehow, they always commit to training them at near frontier scale. I know everybody wants the tell all story of the clever ideas that were developed over the last ~3 years at Anthropic and OpenAI, but what I really want to thumb through is DeepSeek's notebook of "brilli…
This is adapted from Microsoft research's YOCO. It was known for a while(2024!). Yes, credit to Deepseek for actually scaling it up and releasing a frontier flash LLM. Edit: the rest of this thread has become a US China infowar theory culture war. I am not of either of these countries and the above comment isnt meant to implicitly support either "side".
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>This one is 552B so almost twice that, so the huge gains in benchmark scores make sense - it's not really flash anymore, imo. It uses fewer active parameters, though. (8B or 14B instead of always 13B) So ... flash indeed.
200B of those 552B is PLE, which works more like a database that is read for each token, thus can be offloaded to a fast SSD.
My attempt at a simplification of this article on it https://sebastianraschka.com/llm-architecture-gallery/per-la... into a couple of sentences is that they are linear embeddings of the input token space projected per layer, which are then gated by the transformer outputs per layer.
This would mean that the only one set of weights for the ple path needs to be pumped across the memory bandwidth as they are the same linear weights for all layers?
Sheit, maybe im trying to simplify something that i need to look at in detail. but id love to leverage others understanding if possible
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It is easy to be sure because, despite their technically impressive outputs, the programming is child's play compared to biological programming. Recently it has become trendy to suggest that the human brain is "just electrical signals" and "just prediction". The first is perhaps true and I don't inherently rule out the idea of machine consciousness. The second would have gotten you laughed out of any serious discussi…
> Another way one could look at it is to consider what it would mean to have achieved programming consciousness. It would mean that we have reached the pinnacle of knowledge. That we have become God. This is such a basic misunderstanding of how LLMs are "made" that I am debating if it is even worth writing this answer. However, I feel it is important to say that, NO, we did absolutely not "program consciousness". We…
Speak for yourself. I work for an LLM startup that was successfully bootstrapped and is now highly profitable with 8-digit revenue and zero outside investment. Unlike OpenAI and Anthropic, we do not rely on deceiving investors to dump a trillion dollars into a tar fire with the false promise of delivering the machine god that will unemploy all of humanity (at best). Taking people who have an unbelievably large financial stake in lying at face value, and moreover, stating that those are the only people who can be trusted, is so unbelievably naive it's almost cute. Almost.
> We made a framework from which it can semi-organically emerge.
...by programming. Again, this is an appeal to emergent behaviour, which, repeating myself, was already well-demonstrated by Conway's Game of Life in 1970, and yet nobody lost their minds because the emergent behaviour didn't happen to refer to itself as "I" when trained to.