EDIT: The HN title was changed, which previously made the claim. But as HN user swyx pointed out, Tencent is also claiming this is open source, e.g.: "The currently unveiled Hunyuan-Large (Hunyuan-MoE-A52B) model is the largest open-source Transformer-based MoE model in the industry".
Model weights could be treated the same way phone books, encyclopedias, and other collections of data are treated. The copyright is over the collection itself, even if the individual items are not copyrightable.
[0] https://arxiv.org/pdf/2411.02265 [1] https://llm.hunyuan.tencent.com/
Edit: Also, if you don't want to follow or deal with EU law, you don't do business in the EU. People here regularly say if you do business in a country, you have to follow its laws. The opposite also applies.
> "By open-sourcing the Hunyuan-Large model"
They're actually a best-case for CPU inference vs dense models. I usually run deepseek 2.5 quanted to q8, but if this model works well I'll probably switch to it once support hits llama.cpp.
If your GPU has enough VRAM to support it, you might benefit from https://github.com/kvcache-ai/ktransformers
Does the core count matter or can you get away with the smallest 2x EPYC 9015 configuration? What are "good speeds"?
Apparently 20% of Nvidia's quarterly revenue is booked in Singapore where shell companies divert product to China: https://news.ycombinator.com/item?id=42048065
Anyone have some background on this?
There's many places where the model might be used which could count as high-risk scenarios and require lots of controls. Also, we have:
GPAI models present systemic risks when the cumulative amount of compute used for its training is greater than 10^25 floating point operations (FLOPs). Providers must notify the Commission if their model meets this criterion within 2 weeks. The provider may present arguments that, despite meeting the criteria, their model does not present systemic risks. The Commission may decide on its own, or via a qualified alert from the scientific panel of independent experts, that a model has high impact capabilities, rendering it systemic.
In addition to the four obligations above, providers of GPAI models with systemic risk must also:
- Perform model evaluations, including conducting and documenting adversarial testing to identify and mitigate systemic risk.
- Assess and mitigate possible systemic risks, including their sources.
- Track, document and report serious incidents and possible corrective measures to the AI Office and relevant national competent authorities without undue delay.
- Ensure an adequate level of cybersecurity protection."
We've come so astonishingly far in like two years. I have no idea what AI will do in another year, and it's thrilling.
They use
- 16 experts, of which one is activated per token
- 1 shared expert that is always active
in summary that makes around 52B active parameters per token instead of the 405B of LLama3.1.