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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

#131

Post by idiotsecant »

viraptor wrote: post #66
idiotsecant wrote: post #59

Earlier quoted context omitted.

I think it's the exact opposite - you don't specifically train each 'expert' to be a SME at something. Each of the experts is a generalist but becomes better at portions of tasks in a distributed way. There is no 'best baker', but things evolve toward 'best applier of flour', 'best kneader', etc. I think explicitly domain-trained experts are pretty uncommon in modern schemes.

That's not entirely correct. Most of moe right now are fully balanced, but there is an idea of a domain expert moe where the training benefits fewer switches. https://arxiv.org/abs/2410.07490

Yes, explicitly trained experts were a thing for a little while, but not anymore. Yet another application of the Hard Lesson.

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

#132

Post by embedding-shape »

enraged_camel wrote: post #123
barrell wrote: post #108

Earlier quoted context omitted.

> If/when frontier model development speed slows down You do not believe that this has already started? It seems to me that we’re well into a massive slowdown

Not the OP but I use AI all day every day and have noticed substantial improvements in the models over the past ~6 months. GPT-5 was a huge leap (contrary to reporting) and so was Sonnet 4.5.

> GPT-5 was a huge leap (contrary to reporting) and

Depends on what you compare it to. For us who were using o3/o1 Pro Mode before GPT-5, the new model isn't that huge of a leap, compared to whatever was before Pro Mode existed.

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

#133

Post by infecto »

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 use ChatGPTs quite often. I can send it a loaded question and it helps tease out sources and usually at the very least scrapes away some of the nuance. I have used it a lot for finding a list of a type of products too. Taking the top n search results is already pretty useful for me but I find it typically is a little more in depth than that, going down a few rabbit holes of search depending on the topic. It does not eliminate doing your own research but it helps consolidate some of the initial information.

Then I can further interrogate the information returned with a vanilla LLM.

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

#134

Post by infecto »

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.

Same case for me. I find it pretty good at it too. Far from perfect but it usually a pretty darn good start.

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

#136

Post by incomingpain »

I love this one: https://github.com/LearningCircuit/local-deep-research

I tied it together with qwen3 30b thinking. Very easy to get it up and running, but lots of the numbers are shockingly low. You need to boost iterations and context. Especially easy if you already run searxng locally.

I havent finished tuning the actual settings, but for the detailed report it'll take ~20 minutes and so far has given pretty good results. Similar to openai's deep research. Mine often has ~100 sources.

But something I have noticed. It didnt seem to me the model was important. The magic was moreso in the project. Getting deep with higher iterations and more results.

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

#137

Post by greggh »

Imustaskforhelp wrote: post #101
sumo43 wrote: post #36

I made a 4B Qwen3 distill of this model (and a synthetic dataset created with it) a while back. Both can be found here: https://huggingface.co/flashresearch

Can you please create a huggingface space or something similar, I am not sure about the state of huggingface but I would love to be able to try it out in a browser or something similar if possible as I am really curious and I just love qwen3 4b as they were one of the models which work even on my intel integrated gpu graphics card at a really impressive rate and they were really nice the last time I tried but this lo…

I use emotions-analyzer-bert for this classifying content in a similar way. It's very small and very fast, under a gig of vram in use.

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

#138

Post by greggh »

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. :)

If you really need a lot of VRAM cheap rocm still supports the amd MI50 and you can get 32gb versions of the MI50 on alibaba/aliexpress for around $150-$250 each. A few people on r/localllama have shown setups with multiple MI50s running with 128gb of VRAM and doing a decent job with large models. Obviously it won't running as fast as any brand new GPUs because of memory bandwidth and a few other things, but more than fast enough to be usable.

This can end up getting you 128gb of VRAM for under $1000.

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

#139

Post by greggh »

davidsainez wrote: post #49
DataDaemon wrote: post #35

Unfortunately soon China will take lead in AI.

I have been very impressed with the Qwen3 series. I'm still evaluating them, and I generally take LLM benchmarks with a huge grain of salt, but their MoE models in particular seem to offer a lot of bang for the compute. But what makes you so sure they will take the lead?

Deepseek, Qwen, GLM (quite good). All being open and available for local use definitely puts them ahead in that space, which means a lot of the tinkerers and younger people learning to do things like train and fine-tune are getting good with Chinese models and I do think getting in early like that is a great way to gain mindshare in a space. Look at Apple or Microsoft doing everything they could early on to get their machines and software into schools as early as possible.

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

#140

Post by roryirvine »

remus wrote: post #121
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 run a small website and am based in the UK and have used it a couple of times to summarise what I need to do to comply with different bits of legislation e.g. Online Safety Act. What's really useful for me is that I can feed in a load of context about what the site does and get a response that's very tailored to what's relevant for me, and generate template paperwork that I can then fill out to improve my position…

The hidden cost there is that the risk of complying with the legislation remained entirely with you. Even the best specialist research LLM still might easily have hallucinated or made some other sort of error which resulted in it giving you confusing or incorrect advice - and you would have been the one held liable for following it.

Whereas with real legal advice, your lawyer will carry Professional Indemnity Insurance which will cover any costs incurred if they make a mistake when advising you.

As you say, it's a reasonable trade-off for you to have made when the alternative was sifting through the legislation in your own spare time. But it's not actually worth very much, and you might just as well have used a general model to carry out the same task and the outcome would likely have been much the same.

So it's not particularly clear that the benefits of these niche-specific models or specialised fine-tunes are worth the additional costs.

(Caveat: things might change in the future, especially if advancements in the general models really are beginning to plateau.)

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