https://x.com/Chinazhidx/status/2071877413685109071
TODAY: #Huawei open-sources OpenPangu-2.0-Flash
#OpenPangu 2.0 includes two 512K-context models:
• Flash: 92B total,6B active—Weights+inference code+training ops released
• Pro: 505B total,18B active—flagship model, coming in July More open-source components later this year
New model dropped. 1.6T params with 48B active.
Training and inference running entirely on ASIC superpods. Showing the Chinese keep exploring alternative compute options.
They also introduce LongCat Sparse Attention, their improvement on Deepseek Sparse Attention.
This was also Owl Alpha, which was available on Openrouter for free for the past month, which shows they can handle production compute loads using their alternative inference solution.
Hey everyone,
We're the team behind Krea, and today we're launching Krea 2, our new text-to-image model. Krea 2 is the most aesthetic open-source image model available. On quality, Krea 2 is the #1 text-to-image model from an independent lab on Artificial Analysis.
We are releasing Krea 2 as two variants:
Krea 2 Raw. CFG-guided, built for control and fidelity and training.
Krea 2 Turbo. Distilled and few-step, so it's fast, and it renders up to 2K.
A few things worth knowing:
It's tuned for natural language. Prompt it the way you'd describe an image to a person. Long, specific prompts give the best results, but short ones work fine too.
To render text in an image, wrap the words in quotes, like a sign that reads "open late".
There's a growing set of style LoRAs, and you can load any Krea 2 LoRA by its Hugging Face path.
Try it today:
Code and weights: krea.ai/krea-2-open-source
Technical report: https://www.krea.ai/blog/krea-2-technical-report
Code: github.com/krea-ai/krea-2
Try it on Krea: krea.ai
Try it on Hugging Face: https://huggingface.co/spaces/krea/Krea-2
AMA: We're doing an AMA right here today at 10 AM PT. Ask us anything: how we trained it, the LoRAs, prompting, limitations, what's next. The krea team will be in the comments.
Livestream: we are also doing a livestream with the ComfyUI team at 3PM PT: https://www.youtube.com/watch?v=31jiUhCEjJ4
Thanks for taking a look. We'd genuinely love your feedback, rough edges included.
- The Krea Team
There is a clear downward slope apparent on this graph starting from Deepseek V3 to the recent GLM-5.2.
Do we expect the next OSS release to be even faster and better?
One caveat is that this graph appears to show a comparison between an open model and a closed model of *similar capability*, not the absolute frontier. So it looks like it compares DSv4 pro to Sonnet 4.6, for example.
Of course, you could also argue that a direct comparison to absolute frontier models wouldn't be fair either since 5.5, Opus, and Fable are almost certainly sized on the order of multiple trillion parameters.
EDIT: Looks like this was a mistake and the scores have been corrected since. Opus now scores 74.3 and is ranked #3, above GLM-5.2
Link to the index: https://artificialanalysis.ai/?intelligence=coding-index
It has come to my attention as of late that there is a lack of a centralized discussion space specifically related to the discussion of open-weight and open-source models.
Most existing subreddits are focused either on specific modalities (e.g., r/LLMs, r/stablediffusion), a specific usage of open models (e.g., r/LocalLLaMa, r/LocalLLM), or on a more generalized technological scope (e.g., r/singularity, r/ArtificialInteligence).
As a result, discussion of open-source models in general is either unwelcome, fragmented into different communities, or hidden amidst conversation about other broader topics.
So, if there's enough interest, I sincerely hope we can form a community of individuals interested in discussion and news surrounding open-source and open-weight AI technologies (as well as any related technologies).
What is an open source or open weight model?
From ChatGPT:
>An open-weight model is a model where the trained parameters—the “weights”—are publicly available, so people can download, run, inspect behavior, and often fine-tune or adapt the model locally. In AI terms, the weights are the learned numerical parameters that work with the architecture and inference code to produce outputs.
>An open-source model, in the stricter sense, is more than “you can download the weights.” It should come with a license and materials that let people use, study, modify, and share the system. OSI’s open-source licensing criteria emphasize free use, modification, redistribution, and non-discrimination against users or fields of use.
Discussion around technologies relating to both topics are welcome. Additionally, any kind of open-source license that permits some level of free use is welcome.
What kind of discussions are welcome?
Any kind of discussion related to open-source/open-weight AI models and surrounding technologies is welcome. Some examples include news, model releases, use cases, open-source AI-related tools such as harnesses, agents, and interfaces, comparisons of models or tools, advice, tips, set-ups, etc.
Why are open models important?
As many of us realized, from the way the US government dealt with Anthropic's most recent model release, it is becoming more and more evident that our continued ability to access these key technologies is by no means guaranteed.
In just a few short years, AI has become both a powerful democratizing force in society, as well as one of, if not the most important technology that humanity has ever developed.
It is clear that unequal access to these technologies can be a powerful destabilizing force in society. In a worst-case scenario, those who have access to this power can weaponize this imbalance to further disadvantage those who don't, creating further ways of stratifying an already deeply imbalanced society.
Note: This post was NOT generated by AI (aside from the two definitions provided above).
Anyone tried it yet?
https://x.com/designarena/status/2066940737011560652
From Zai: https://x.com/zai_org/status/2066938937344495629
Edit:
Pricing:
input - 1.4$
output - 4.4$
Today, we are thrilled to open-source Ling–2.6–1T from the Ling family.
Tailored for real–world, complex scenarios, this trillion–parameter model introduces targeted optimizations across inference efficiency, token overhead, and agentic capabilities, making it highly effective for coding and daily workflows.
Key upgrades in Ling–2.6–1T include:
