
WITH GOD ALL THINGS ARE POSSIBLE 🙇🕌 Lover of sports and cruise😜⚽||LORD OF MEMES💀😈|| Turn on my notification 📣🔔 Backup account : @Bigguy_x
X
Joined October 2022
- Tweets117K
- Following5.5K
- Followers10.6K
- Likes37K
Øffîcîal Dēstîñy🌍🥷 retweeted
North West has decided to use Kanye West as her manager and remove Kim after some money went missing from her account recently and couldn’t be accounted for. North was very disappointed that her mom could steal from her despite all the money she has.
Øffîcîal Dēstîñy🌍🥷 retweeted
No matter how badly I have been treated, how I'm feeling, or what I have been through, I still have a kind heart and so much love to give. That's something no one can take away from me
Øffîcîal Dēstîñy🌍🥷 retweeted
Prado SUV for them, One cartoon of indomie for you and your familytill another 4yrs.
Congratulations!
Øffîcîal Dēstîñy🌍🥷 retweeted
You need more dick to pull of this position than you need to do doggystyle with a fat ass girl btw, doggy doesn’t really need big dicks to work . But this position ..? It won’t work if you aren’t long down there .
Øffîcîal Dēstîñy🌍🥷 retweeted
🔥 “One careless decision can turn a moment of pleasure into a lifetime of regret. ⚠️ Never compromise your safety for temporary satisfaction!”
Øffîcîal Dēstîñy🌍🥷 retweeted
One thing I look for in intent understanding is whether a model can get the full request right—not just predict a plausible category, but extract the correct details, recognize multiple intents, and avoid inventing categories when there’s nothing to extract.
@XingChenAGI How does Xing4.0-29B-Intent perform across different real-world workloads?
🔗 Click the link and give it a try!
GitHub:
github.com/XingChen-AGI/Xing…
HuggingFace:
huggingface.co/XingChen-AGI/…
huggingface.co/datasets/Xing…
Øffîcîal Dēstîñy🌍🥷 retweeted
A commonly overlooked part of building AI agents: identifying what a user wants is only half the job. A model may correctly recognize “control the lights,” but it still needs the room, brightness, or color temperature. Unlike decision models such as Jev, Xing4.0-29B-Intent combines intent classification with parameter extraction—handling both the multiple-choice and fill-in-the-blank parts. @XingChenAGI It returns the details needed to act, not just a label.
Give it a try.
GitHub:
github.com/XingChen-AGI/Xing…
HuggingFace:
huggingface.co/XingChen-AGI/…
huggingface.co/datasets/Xing…
Øffîcîal Dēstîñy🌍🥷 retweeted
I don't think every AI task needs a general-purpose LLM. When a system handles millions of requests, using a model for simple classification and information extraction can add unnecessary latency and inference costs. Free-form responses can also make downstream parsing harder.
@XingChenAGI The interesting idea behind Xing4.0-29B-Intent is specialization: focus on well-defined decisions and return structured outputs that applications can use directly.
Check it out and try it yourself:
GitHub:
github.com/XingChen-AGI/Xing…
HuggingFace:
huggingface.co/XingChen-AGI/…
huggingface.co/datasets/Xing…

































