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Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

tongyi-agent.github.io

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Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#91

Post by alasr »

tbruckner wrote: post #34

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.

I haven't used any LLM deep research tools in the past; today, after reading this HN post, I gave Tongyi DeepResearch a try to see how it performs on a simple "research" task (in an area I've working experience in: healthcare and EHR) and I'm satisfied with its response (for the given tasks; I, obviously, can't say anything how it'll performs on other "research" tasks I'll ask it in the future). I think I'll keep using this model for tasks for which I was using other local LLM models before.

Besides I might give other large deep research models a try when needed.

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#92

Post by edot »

criemen wrote: post #81
tbruckner wrote: post #34

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.

I tend to use them when I'm looking to buy something of category X, and want to get a market overview. I can then still dig in and decide whether I consider the sources used trustworthy or not, and before committing money, I'll read some reviews myself, too. Still, it's a speedup for me.

Yes, this is one of my primary use cases for deep research right now. It will become garbage in a few short years once OpenAI starts selling influence / ads. I think they’ve started a bit with doing this but the recommendations still seem mostly “correct”. My prior way of doing this was Googling with site:Reddit.com for real reviews and not SEO spam reviewers.

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#93

Post by pama »

ainch wrote: post #62
onlyrealcuzzo wrote: post #47

Earlier quoted context omitted.

Isn't the whole point of the MOE architecture exactly this? That you can individually train and improve smaller segments as necessary

Generally you train each expert simultaneously. The benefit of MoEs is that you get cheap inference because you only use the active expert parameters, which constitute a small fraction of the total parameter count. For example Deepseek R1 (which is especially sparse) only uses 1/18th of the total parameters per-query.

> only uses 1/18th of the total parameters per-query.

only uses 1/18th of the total parameters per token. It may use the large fraction of them in a single query.

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#94

Post by aliljet »

aliljet wrote: post #5

Sunday morning, and I find myself wondering how the engineering tinkerer is supposed to best self-host these models? I'd love to load this up on the old 2080ti with 128gb of vram and play, even slowly. I'm curious what the current recommendation on that path looks like. Constraints are the fun part here. I know this isn't the 8x Blackwell Lamborghini, that's the point. :)

oh my god. 128 gb of RAM! way too late to repair this thread, but most people caught this.

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#95

Post by MaxPock »

mike_hearn wrote: post #87
MaxPock wrote: post #70

Earlier quoted context omitted.

This sounds like copium . If it was just about distillation,we'd be seeing many awesome models from Europe ,Japan and even India.

It's certainly both a lot more than distillation and at least some Chinese labs have been cloning OpenAI via distillation. That's why they instituted much tighter ID verification requirements earlier this year. 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 comb…

There’s no Chinese lab that has been accused by OpenAI or anyone else of distillation. The accusations come from fringe right-wing media that are used to the “China only copies” trope. Training a model, by the way, is not about money, because many Western tech giants have more money than the CCP can allocate to Chinese labs. Apple, Meta, Amazon, SAP, IBM, and others have access to the same data as OpenAI and should thus be able to come up with a SOTA model in under a year, right? On lax copyright enforcement, I’d like to point out that it’s actually Western labs that have been taken to court for stealing content.

On matters protectionism,the Great Firewall was the best thing that China did.It prevented them from digital colonization like the rest of the world.

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#96

Post by VladVladikoff »

Recently I gave a list of 300 links to deep research and asked it to go through each one to analyze a certain question about them. Repeatedly it would take shortcuts and not actually do the full list. Is this caused by a context window limits? Or maybe Open AI limits request size? Is it possible to not run into these types of limits with locally hosted models?

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#98

Post by nextos »

criemen wrote: post #80
zurfer wrote: post #8

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…

The advantage of small purpose-specific models is that they might be much more robust i.e., unlikely to generate wrong sequences for your particular domain. That is at least my experience working on this topic during 2025. And, obviously, smaller models mean you may deploy them on cheaper hardware, latency is reduced, energy consumption is lower, etc. In some domains like robotics, these two advantages might be very compelling, but it's obviously early to draw any long-term conclusions.

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#99

Post by Imustaskforhelp »

zurfer wrote: post #8

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…

> But there is probably already some tradeoff, as GPT 3.5 was awesome at chess and current models don't seem trained extensively on chess anymore.

Wow, I am so curious, can you provide me the source

I am so interested in a chess LLM's benchmark as someone who occasionally plays chess. I have thought about creating things like these but it would be very interesting to find the best model at chess which isn't stockfish/lila but general purpose large language models.

I also agree that there might be an explosion of purpose trained LLM's. I had this idea some year ago when there was llama / before deepseek that what if I want to write sveltekit and there are models like deepseek which know about sveltekit but they are so damn big and bloated when I only want to use sveltekit/svelte models. Yes there are thoughts on why we might need the whole network to get better quality but I genuinely feel like right now, the better quality is debtable thanks to all this benchmarkmaxxing and I would happily take a model trained on sveltekit on like preferably 4b-8b parameter but if an extremely good SOTA-ish model for sveltekit is even around 30-40b I would be happy since I could buy a gpu on my pc to run it or run it on my mac

I think my brother who actually knows what he's talking about in the AI space, (unlike me), also said the same thing a few months back to me as well.

In fact, its funny because I had asked him to please create a website comparing benchmarks of AI playing chess and having an option where we can make two AI LLM's play against each other and we can view it or we can also play against an LLM inside an actual chess board on the web and more..., I had given this idea to him a few months ago after the talk about small llm's really lol and he said that its good but he was busy right now. I think then later he might have forgotten about it and I had forgotten about it too until now.

Re: Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch

#100

Post by Imustaskforhelp »

miki123211 wrote: post #27
deepanwadhwa wrote: post #18

Earlier quoted context omitted.

-> GPT 3.5 was awesome at chess I don't agree with this. I did try to play chess with GPT3.5 and it was horrible. Full of hallucinations.

It was GPT-3 I think. As far as I remember, it's post-training that kills chess ability for some reason (GPT-3 wasn't post-trained).

This is so interesting, I am curious as to why, can you (or anyone) please provide any resources or insightful comments about it, they would really help a ton out here, thanks!
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