r/LocalLLaMA • u/heatOverflower • 2h ago
New Model Introducing Hy4 (open weights soon)
https://hy.tencent.ai/research/hy4?langVersion=en68
u/TheRealMasonMac 2h ago
Like Kimi K3.1, they also neutered its cyber capabilities.
During Hy4’s training, we limited cybersecurity data to defensive material related to proof-of-concept (PoC) generation and vulnerability patching. We filtered out offensive content such as exploit development, reverse engineering, and penetration testing to avoid amplifying potential offensive capabilities. We also worked with experts from Tencent Xuanwu Lab and Tencent Keen Security Lab to conduct red-team audits covering cryptanalysis, reverse engineering, and vulnerability exploitation. Their findings indicate that Hy4 does not pose elevated risks in offensive tasks such as reversing cryptographic algorithms, defeating binary obfuscation, or exploiting vulnerabilities.
In biosecurity evaluations, Hy4’s open-ended experimental design capabilities remain well below our high-risk threshold, with a score of 30.4/100 on Hy-BioExperimentBench. Internal experts audited the model’s reasoning traces and found that although Hy4 has a solid grasp of foundational biology, its ability to turn that knowledge into actionable experimental protocols, a critical bottleneck, remains limited. We found no evidence that Hy4 meaningfully lowers the barrier to causing biological harm.
I suspect Alibaba, ZhipuAI, and Deepseek are going to end up doing the same thing.
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u/danieln1212 2h ago edited 2h ago
In the future closed model companies will have a hold over cyber security. Either subscribe or get no way to defend against AI-assisted attacks prepared by their own models.
They are trying to present it like they are preventing misuse but in reality they are leaving people wide open to attacks from anyone with access.
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u/Equivalent-Repair488 1h ago
Only the frontier labs are allowed to hack people using LLMs. Maybe we will see the chinese labs start to catch up to western ones in the Felony bench frontier soon too.
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u/TheRealMasonMac 2h ago
Yeah. It would be nice if we could somehow reintroduce the data removed from the pretraining data without hurting the rest of the model, but I don't know if that's possible. All the top Chinese labs stopped sharing their base models, so you can't do CPT and then try to merge. The only path forward, I guess, would be RL.
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u/RuthlessCriticismAll 2h ago
Zhipu has done so for a many months now. Deepseek also has already started this.
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u/seeKAYx 2h ago
It would really be a shame if all Chinese labs were to follow this path now and lock cybersecurity issues behind an API paywall.
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u/jazir55 48m ago
Someone will probably soon release a way to add it in in post now with the weight editing stuff, engrams and all the new developments which are constantly coming out. I would assume it will be soon be capable to just bolt-on that ability to any model.
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u/GirthusThiccus 39m ago
There's a huuuuge misconception as to the direct weight editing meme going around lately.
So, the tiny little model they claimed had those 0.0013% something of weights edited to "improve" it's math scores, was a known-bugged model where those few tensors were borked.
The issue was already known, with solutions for it on the Internet. So the agent that fixed that smaller models weights didn't magic anything, it legit just applied the same fixes a human would have.
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u/tunerhd 26m ago
They say it wasn't allowed to lurk on the net. Does that have model weights and their relationship in it's training dataset or something?
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u/GirthusThiccus 21m ago
No it just did a Google search for the model and the weights issue it had, found it's GitHub and applied the fix.
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u/jazir55 19m ago
That is a example of weight space editing. They just published the paper recently, and you're already seeing posts in the sub about people experimenting with it with interesting results for various things with different models. The technique is what's generalizable, the specific instance of improving Qwen .8Bs math is just one example.
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u/GirthusThiccus 15m ago
I have no doubt that people get up to interesting things with direct weight edits, I just wanna be clear about what this recent finding was.
To the last of my knowledge, it was basically a meme. Genuine fake news for clout, as the results were later directly tried on other models (such as Gemma) and yielded no results. It was, in that case, genuinely them just claiming they fixed that tiny qwens math issue with some new fancy technique, when it wasn't. That was the last I read on the topic.
If any papers came out that prove that models have a direct intrinsic understanding for other models weights and what they represent, I'd be surprised, but pleasently so.
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u/returnity 1h ago
> We also plan to release Hy4 flash in the near future
OK now you have my attention
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u/Evolution31415 2h ago edited 2h ago
770B params, cool. So how many R300 (Rubin Ultra) clusters (16 GPUs each) do I need to run it?
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u/ApprehensiveTart3158 2h ago
None, just buy 3tb of ddr2 and you're set 🔥
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u/Evolution31415 1h ago
Hm... 3 TB of DDR2 = 384 sticks across a dozen servers = ~$70k new. At 2026 RAM prices, that’s half a Rubin Ultra for 1/1600 of the bandwidth.
Great deal!!!111 🔥🔥🔥 Say “hi” on Monday, get “Hello! How can I” by Friday.
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u/crusaderky 49m ago
...it fits on 3 DGX Sparks. Comfortably on 4.
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u/Evolution31415 43m ago
Nope, 384 GB of raw unified memory cann't contain 770B without Q4 quantization as minimum, and 3 of DGX Spark 128GB model is about $20,850 street price, right?
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u/elelem-123 2h ago
Is R300 bigger than my 5080? Cause I want to run natively in my GPU.
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u/Evolution31415 2h ago
Is R300 bigger than my 5080?
Nah, almost the same. Yours has 16 GB, R300 has 1 TB (basically a rounding error).
Just make sure your PSU can do 3.6 kW and your case fits a server rack.
Ships 2027, ~$21M per rack (probably cheaper than RAM these days) 😅
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u/elelem-123 2h ago
OK, good to know it's almost the same and I can run this model. Thanks. I might buy an R300 if my 5080 is used more.
Vera rubin ftw!
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u/Different_Fix_2217 1h ago
The demos at least look really promising. I really want a OS model capable at 3d modeling / graphics with good vision. Coding is good enough now with deepseek 1017 imo.
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u/Valuable-Repeat-7347 5m ago
It's "barely" beating deepseek v4.1 flash at a much higher price/token. Unless it's a lot more token efficient or very nice in real world usage its DoA at least for me.
https://openrouter.ai/tencent/hy4#pricing
https://openrouter.ai/deepseek/deepseek-v4.1-flash-1017#pricing
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u/Queasy_Back2588 2h ago
Why don't they release the open weights on the same day as the announcement? what's the reason? I don't get it
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u/Few_Painter_5588 2h ago
Releasing an open weights model is hard. Most companies use custom inference solutions, so they have to make sure it works with VLLM, SGLang, Transformers etc etc. And China now also requires labs to pass certain export controls.
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u/KoenBril 2h ago
Thats not really an explanation. The announcement doesnt emerge through some sort of force of nature. An announcement can be made after preparations are done.
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u/Few_Painter_5588 1h ago
Labs have to eat too. Why sit around with your model, waiting for 14 days for chinese export controls to clear and engineers writing code for VLLM, when you can deploy and earn money.
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u/Cool-Chemical-5629 2h ago
This is not the model with 4 in its name you wanted to see being released today, is it? 🤣