81–90 of 156 posts
Has anyone found these deep research tools useful? In my experience, they generate really bland reports don't go much further than summarization of what a search engine would return.
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llama.cpp + quantized: https://huggingface.co/bartowski/Alibaba-NLP_Tongyi-DeepRese... get the biggest one that will fit in your vram.
How do people deal with all the different quantisations? Generally if I see an Unsloth I'm happy to try it locally; random other peoples...how do I know what I'm getting? (If nothing else Tongyi are currently winning AI with cutest logo)
Has anyone found these deep research tools useful? In my experience, they generate really bland reports don't go much further than summarization of what a search engine would return.
My experience is the same as yours. It feels to me (similar to most LLM writing) like they write for someone who’s not going to read it or use it but is going to glance at it and judge the quality that way and assume it’s good. Not to different from a lot of consulting reports, in fact, and pretty much of no value if if you’re actually trying to learn something. Edit to add: even the name “deep research” to me feels…
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I actually can’t read it for some reason? My brain just can’t connect the words
so it appears the entire text has been Translated with non-breaking space unicode x00a0 instead of normal spaces x0020, so the web layout is considering all paragraph text as a super-long single word ('the\00a0quick\00a0\brown\00a0fox' instead of 'the quick brown fox') - the non-breaking space character appears identically to breaking-space when rendered but underlying coding breaks the concept of "break at end of wo…
The script is great!
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I've recently put together a setup that seemed reasonable for my limited budget. Mind you, most of the components were second-hand, open box deals, or deep discount of the moment. This comfortably fits FP8 quantized 30B models that seem to be "top of the line for hobbyists" grade across the board. - Ryzen 9 9950X - MSI MPG X670E Carbon - 96GB RAM - 2x RTX 3090 (24GB VRAM each) - 1600W PSU
Does it offer more performance than a Macbook Pro that could be had for a comparable sum? Your build can be had for under $3k; a used MBP M3 with 64 GB RAM can be had for approximately $3.5k.
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I think the lesson of the Chinese catchup in AI is that there is a massive disadvantage in being first, in this domain. You can do all the hard work and your competitors can distill that work out of your model for pennies on the dollar. Why should anyone want to do the work?
This sounds like copium . If it was just about distillation,we'd be seeing many awesome models from Europe ,Japan and even India.
No, the reason you don't see many open source models coming from the rest-of-world (other than Mistral in France) is that you still need a ton of capital to do it. China can compete because the CCP used a combination of the Great Firewall and lax copyright/patent enforcement to implement protectionism for internet services, which is a unique policy (one that obviously came with massive costs too). This allowed China to develop home grown tech companies which then have the datacenters, capital and talent density to train models. Rest of world didn't do this and wasn't able to build up domestic tech industries competitive with the USA.
Unfortunately soon China will take lead in AI.
Isn't this an indication they are already in the lead? They currently have the best model that beats everyone on all quantitative metrics? Are you implying that the US has a better model somewhere?
It makes me wonder if we'll see an explosion of purpose trained LLMs because we hit diminishing returns on invest with pre training or if it takes a couple of months to fold these advantages back into the frontier models. Given the size of frontier models I would assume that they can incorporate many specializations and the most lasting thing here is the training environment. But there is probably already some tradeo…
So yeah I think there are different levels of thinking, maybe future models with have some sort of internal models once they recognize patterns of some level of thinking, I'm not that knowledgeable of the internal workings of LLMs so maybe this is all nonsense.
It makes me wonder if we'll see an explosion of purpose trained LLMs because we hit diminishing returns on invest with pre training or if it takes a couple of months to fold these advantages back into the frontier models. Given the size of frontier models I would assume that they can incorporate many specializations and the most lasting thing here is the training environment. But there is probably already some tradeo…
> or if it takes a couple of months to fold these advantages back into the frontier models. Right now, I believe we're seeing that the big general-purpose models outperform approximately everything else. Special-purpose models (essentially: fine tunes) of smaller models make sense when you want to solve a specific task at lower cost/lower latency, and you transfer some/most of the abilities in that domain from a bigg…