71–80 of 1001 posts
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Weren't there some frameworks recently released to allow Macs to stream weights from fast SSDs and thus fit way more parameters than what would normally fit in RAM? I have never tried one yet but I am considering trying that for a medium sized model.
Do you have the links for those? Very interested
Note: these were just two that I starred when I saw them posted here. I have not looked seriously at it at the moment,
https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main... Model was released and it's amazing. Frontier level (better than Opus 4.6) at a fraction of the cost.
Claude4.6 was almost 10pp better at at answering questions from long contexts ("corpuses" in CorpusQA and "multiround conversations" in MRCR), while DSv4 was a staggering 14pp better at one math challenge (IMOAnswerBench) and 12pp better at basic Q&A (SimpleQA-Verified).
I like the pelican I got out of deepseek-v4-flash more than the one I got from deepseek-v4-pro. https://simonwillison.net/2026/Apr/24/deepseek-v4/ Both generated using OpenRouter. For comparison, here's what I got from DeepSeek 3.2 back in December: https://simonwillison.net/2025/Dec/1/deepseek-v32/ And DeepSeek 3.1 in August: https://simonwillison.net/2025/Aug/22/deepseek-31/ And DeepSeek v3-0324 in March last year:…
https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main... Model was released and it's amazing. Frontier level (better than Opus 4.6) at a fraction of the cost.
The dragon awakes yet again!
That's literally what the I Ching calls "good fortune."
Competition, when no single dragon monopolizes the sky, brings fortune for all.
I’m deeply interested and invested in the field but I could really use a support group for people burnt out from trying to keep up with everything. I feel like we’ve already long since passed the point where we need AI to help us keep up with advancements in AI.
The players barely ever change. People don't have problems following sports, you shouldn't struggle so much with this once you accept top spot changes.
At this point I would just pick the one who's "ethics" and user experience you prefer. The difference in performance between these releases has had no impact on the meaningful work one can do with them, unless perhaps they are on the fringes in some domain.
Personally I am trying out the open models cloud hosted, since I am not interested in being rug pulled by the big two providers. They have come a long way, and for all the work I actually trust to an LLM they seem to be sufficient.
I like the pelican I got out of deepseek-v4-flash more than the one I got from deepseek-v4-pro. https://simonwillison.net/2026/Apr/24/deepseek-v4/ Both generated using OpenRouter. For comparison, here's what I got from DeepSeek 3.2 back in December: https://simonwillison.net/2025/Dec/1/deepseek-v32/ And DeepSeek 3.1 in August: https://simonwillison.net/2025/Aug/22/deepseek-31/ And DeepSeek v3-0324 in March last year:…
No way. The Pro pelican is fatter, has a customized front fork, and the sun is shining! He’s definitely living the best life.
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It is more than good enough and has effectively caught up with Opus 4.6 and GPT 5.4 according to the benchmarks. It's about 2 months behind GPT 5.5 and Opus 4.7. As long as it is cheap to run for the hosting providers and it is frontier level, it is a very competitive model and impressive against the others. I give it 2 years maximum for consumer hardware to run models that are 500B - 800B quantized on their machines…
What's going to change in 2 years that would allow users to run 500B-800B parameter models on consumer hardware?
The paper is here: [0] Was expecting that the release would be this month [1], since everyone forgot about it and not reading the papers they were releasing and 7 days later here we have it. One of the key points of this model to look at is the optimization that DeepSeek made with the residual design of the neural network architecture of the LLM, which is manifold-constrained hyper-connections (mHC) which is from thi…
> this is why Anthropic wants to ban open weight models Do you have a source?
https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main... Model was released and it's amazing. Frontier level (better than Opus 4.6) at a fraction of the cost.
For the curious, I did some napkin math on their posted benchmarks and it racks up 20.1 percentage point difference across the 20 metrics where both were scored, for an average improvement of about 2% (non-pp). I really can't decide if that's mind blowing or boring? Claude4.6 was almost 10pp better at at answering questions from long contexts ("corpuses" in CorpusQA and "multiround conversations" in MRCR), while DSv4…