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#models#model#glm#training#better#more#data#open#still#fable
Discussion (115 Comments)Read Original on HackerNews
How are you all toying with running this kind of thing in a mega quantized way locally? Two weeks out from released weights, but this is still just GLM 5.2 with post-training magic.
These Chinese models cost less of US SOTA models to run, even if they are less capable. Providers can just run them, offer cheap tokens, and pocket the margin.
I just don't see how you justify a trillion valuation for US AI labs when the underlying models are being commoditized this fast.
It is said that it comes with all hardware and software required to run inference or training with an open weights LLM.
The existence of this product, which competes with cloud-based offerings like those of OpenAI and Anthropic, is presumably the reason why the Palantir CEO criticized very harshly some time ago the business model of OpenAI/Anthropic.
While I doubt that the ethics of Palantir is any better than of OpenAI/Anthropic, in this particular case I have to agree with Alex Karp about "Sovereign AI", i.e. that only losers will make their business completely dependent on an external entity like OpenAI or Anthropic, who are certainly not trustworthy.
We’ve a hybrid shop, including hosting our own ML infra, and we save a ton from cloud spend with local ML. Easily one million USD over past three years. But it’s not “free”, you are shifting a lot of labor into your plate.
Disagree there but I think this is an interesting idea. We would need to find some more cost-efficient hardware to run it on than Nvidia GPUs.
There’s an assumption that you can spin up the infra and acquire customers within that margin
I wish they had tried to IPO because then we’d see the judgement of the market on this. But that’s why they didn’t this year. How long can they keep up the charade that their models are uniquely valuable and on the path to AGI?
Provoking war, this is how the empire "defends" itself, usually.
I just hope that this time it will get stuck in your throat.
Not sure about Sol as I haven't used it, but, at least for security work -- does it matter? It's not like you will be allowed to use Fable (or access Mythos) for anything cybersecurity-related unless your name is "Dario Amodei" or you are one of his rich friends. So regardless of how good Fable/Mythos is here it's a completely moot point for normal people, because they can't use it for that anyway.
I mean what honestly are you thinking Anthropic can do to give you better cyber tools? Their frontier model was literally nuked by the feds for a month for doing it.
Fable is their version with guardrails on everything except "Make me a pelican svg" or "create a to-do" app, that is the version that the government banned
Okay, here's a challenge: I assume you're not a rich and powerful entity, so try to gain access to Mythos. I'll wait.
> I mean what honestly are you thinking Anthropic can do to give you better cyber tools? Their frontier model was literally nuked by the feds for a month for doing it.
Well, first I'd suggest they stop with the constant fear mongering.
Here's my prediction for what will happen: the Chinese models will catch up to Fable/Mythos. They will be fully unrestricted and everyone will have access. The world will not end. Good guys will use them to harden their systems, in equilibrium to what bad guys have access to, so effectively status quo will not change.
Isn't post-training turning out to be the most important part?
4x DGX sparks should let you run this at 4 bit at least and there are some folks who ran GLM 5.2 on this configuration in r/LocalLlama
I only have one and am wondering what the benefits are of getting another. I feel I will be disappointed…
in some cases (mainly reverse engineering) I have observed GLM 5.2 jailbreaking itself with no effort on my part, the thinking trace revealed that it did some mental gymnastics to pretend it was a crackme or capture the flag competition.
Even if there was a small/medium gap, the fact that this is a free model beats both of the above on pure economics.
It does feel like it respects both me and my time.
Thank you, Z.AI. Amazing what difference it makes when the top of your org are actual university professors.
I appreciate they don't just take the opportunity to self-glaze.
Love this opening line. And wow, great results.
> As agent capability improves, much of the difficulty in scaling post-training moves from the model to the environment.
Original images: https://image.non.io/neonRamenDesigns.webp
GLM 5.3 build: https://html.non.io/neonRamenGLM5.3
Opus 5 build for comparison: https://html.non.io/neonRamen
For having no vision, it did a tremendous job. I'm pretty impressed it was able to extract so much detail.
The Opus one is still significantly better, but that's to be expected since it's multimodal. Curious to see where a future version from Z.ai lands on this.
Otherwise we have a world of attackers using open and closed source models against a much smaller group of maintainers that are likely heavily dependent on Anthropic and OpenAI and for whom it may not be a simple matter to just get approval to start using the open model flavor of the month.
It is so valuable in practise to be able to have the models see screenshots - I guess if they aren't in the benchmarks then nobody will focus on it. But it completely nixes these for some of my main use cases.
I remember trying their Coding Plan out before the change and the 5 hour limits felt too restrictive then even for light/medium work, especially cause of the whole peak and off-peak thing: https://blog.kronis.dev/blog/z-ai-s-glm-5-2-is-a-great-model...
Nowadays, I’d probably go with their Max plan if the rate limits are okay? Anyone using them now?
Oh also unrelated but ZCode was surprisingly good, which is surprising for a tool that came out of nowhere - even some of the critiques in my blog post have been patched out. Sadly they don’t support using Claude Code as an agent so can’t use it like Paseo or Kepler or Agent Orchestrator.
Hopping for an AgentWorld variant from Qwen but I guess, I have too high expectations.
yet but it is still contain a lot of trash. you need better models to process those trash and create a curate dataset. this will happen again and again until there is no more juice to squeeze. and I'm sure we are still not done with it.
> post training
yeah this will be crucial. the big models are already too capable, they are just not that aligned with current agent tasks.
> parameter count doesn’t seem to be a direct correlation anymore
I don't think so, remember that chinese labs do not have as much compute power compare to US frontier labs. that's why deepseek v4 flash had that huge jump and deepseek v4 pro is kinda a disappointment, they just do not have the compute power to proper posttrain the pro model like they wanted. glm is also a relative small model so you also can see the huge jump with just post training. so it does not mean the size does not matter, it is just mean that the chinese labs currently only capable of training smaller models effectively.
LLMs are still surprisingly “easy”. You need maybe a couple dozens of right people, a lot of good quality data and a lot of GPU that you know how to operate. There’s relatively little “secret sauce” needed.
10 years ago it was super hard to have usable “frontier” ML. You needed very complex data warehouse, feature engineers, feature stores, multi level ranking, calibrations, tons of different model architectures, etc, etc. Each by itself was extremely hard engineering problem and really only handful of companies could deal with that complexity.
With LLMs, 95% of that is gone, infra to support them is greatly simplified. Of course, to make really reliable, performant, user friendly, etc - you still need to a lot of engineering. But it’s very different challenge.
Despite this being the topic du jour of 2025, it was never true. Most of the "we've hit a wall with data" came from communicators / media and not researchers. It got popular because negativity sells. It's a false premise for a number of reasons:
a) Data curation is as important, if not more important than bulk data. Models becoming better at classification leads to better curation leads to cleaner data. Throwing common crawl and pray is so 2023. We've known this since llama3 days, it worked then, there's no reason to think this will not continue to work as the models imrpove.
b) Models are today good enough that you can augment / multiply your data easily with enough compute. You can now have a model take "authoritative content" and create more data from that + scenarios. Say you take a book on computer architecture. You ask models to break it down. Then you ask models to find examples for each topic. Then you ask models to ask questions and offer answers from several viewpoints. Then you take each of those and ask other models to flag inconsistencies. And so on. But you can whateverX your data from one authoritative source + bulk data into 5x - 10x "scenarios".
c) RL is really really really powerful. It's hard to do right (reward hacking, instabilities, etc) but once it works it "keeps" on working. Again, we knew this to be true a few years ago, ever since models really started to do well on math (highly verifiable). It only follows they're getting better on cybersec and other verifiable tasks. But now, with models improving, you get the same data augmentation pipelines as above, just better because they're also verifiable. For example, the way cursor augments their data: take a repo, ask an agent to identify a feature (it can be a large multi-file feature). Remove all code relating to that feature, but keep the original tests in the repo. While training, that becomes a RL scenario: implement this feature in this repo. Verify it with the original (hidden for training) tests. Reward appropriately. Now you can get 1 repo -> 20-50-100 scenarios. Instead of "feed everything into the pretraining", you're now creating scenarios, verify them w/ existing tools, and get your scoring function for the rewards. And, importantly, as the models become better in general, they also become better at this pipeline building exercise. So the next iteration gets trained on more scenarios, better scenarios, and so on.
> how will models continue to get better?
Probably the same. No one can know for sure, but at the moment, despite all the "walls this, slowdown that, plateauing" and so on, there are no signs of slowing down. And, as you noted, this works across the field of model sizes. There are, of course, theoretical information-based limits on size, but smaller models also improve, once "bigger" models can be used as training data generators, oracles for verification, rubric verifiers for open ended questions, and so on.
And smaller models (i.e. cheaper to serve) get to generate more traces during RL, and more rollouts give you better training, and so on. Next up - hardware optimised inferencing (ASICs basically). Once you have that, we can expect another wave of improvements. And so on.
A positive feedback loop then. RL->better model->better RL pipeline -> better model…
And we’ve only recently started getting into the much better RL pipelines
We just had amazing releases this past two months
kimi k3, glm5.3 qwen3.8 and now glm5.3
These open models are getting really good
JieTang (Founder of Z.ai): It won't take that long
https://x.com/i/trending/2067626647050670400?lang=en
Kimi and QWEN are now moving on to a restricted-usage license, which, although is still better than the proprietary American models, is a step back from the open source Chinese LLM culture.
Let those companies with thousands of GPU making millions pay. They should.
"Open Source: We will release the weights in two weeks after launch, once safety evaluation and hardening are complete."
(In my case "it"=Fable; I assume Opus is similar.)
Man. That would make a lot of sense indeed.
It seems to speak in a shorthand that only it understands, referring back to conversations I never had with it (stuff like "your instinct was right"), and using unusual words for common concepts. That was before the watermarks were announced, but that doesn't necessarily mean they weren't there before the announcement. I don't know what the cause is, but I've begun to have to ask it for explanations a lot more often, and I hate asking it for explanations because it does go on. All models go on, but Claude models are a class of their own in terms of verbosity and purple prose.
It just feels like they're not focused on the models lately, and instead on whatever kind of lobbying and propaganda they're up to. Meanwhile, a handful of much smaller Chinese companies are focused on nothing but the models and are about to lap the US makers while they fart around.
What "safety" evaluation? They already evaluated it and found it to be highly capable at exploiting security vulnerabilities. So we know it is not "safe", and they don't seem to plan to do anything against it. What could be more dangerous than hacking? Biological weapons research? I don't think Chinese labs are doing anything against this either.
/s
Sorry, but can we not casually drop far right extremist conspiracy theories in little side sentences? [0]
[0] https://en.wikipedia.org/wiki/Great_Replacement_conspiracy_t...