microsoft/VibeVoice-1.5B
Text-to-Speech
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Feynman Innovations
ajibawa-2023
AI & ML interests
LLM, RL, DL, ML, AGI. Developing LLMs (preferably fully fine tuned ) for various use cases.
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fdaudens's
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about 2 months ago
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You might not have heard of Moonshot AI — but within 24 hours, their new model Kimi K2 shot to the top of Hugging Face’s trending leaderboard.
So… who are they, and why does it matter?
Had a lot of fun co-writing this blog post with @xianbao , with key insights translated from Chinese, to unpack how this startup built a model that outperforms GPT-4.1, Claude Opus, and DeepSeek V3 on several major benchmarks.
🧵 A few standout facts:
1. From zero to $3.3B in 18 months:
Founded in March 2023, Moonshot is now backed by Alibaba, Tencent, Meituan, and HongShan.
2. A CEO who thinks from the end:
Yang Zhilin (31) previously worked at Meta AI, Google Brain, and Carnegie Mellon. His vision? Nothing less than AGI — still a rare ambition among Chinese AI labs.
3. A trillion-parameter model that’s surprisingly efficient:
Kimi K2 uses a mixture-of-experts architecture (32B active params per inference) and dominates on coding/math benchmarks.
4. The secret weapon: Muon optimizer:
A new training method that doubles efficiency, cuts memory in half, and ran 15.5T tokens with zero failures. Big implications.
Most importantly, their move from closed to open source signals a broader shift in China’s AI scene — following Baidu’s pivot. But as Yang puts it: “Users are the only real leaderboard.”
👇 Check out the full post to explore what Kimi K2 can do, how to try it, and why it matters for the future of open-source LLMs:
https://huggingface.co/blog/fdaudens/moonshot-ai-kimi-k2-explained
So… who are they, and why does it matter?
Had a lot of fun co-writing this blog post with @xianbao , with key insights translated from Chinese, to unpack how this startup built a model that outperforms GPT-4.1, Claude Opus, and DeepSeek V3 on several major benchmarks.
🧵 A few standout facts:
1. From zero to $3.3B in 18 months:
Founded in March 2023, Moonshot is now backed by Alibaba, Tencent, Meituan, and HongShan.
2. A CEO who thinks from the end:
Yang Zhilin (31) previously worked at Meta AI, Google Brain, and Carnegie Mellon. His vision? Nothing less than AGI — still a rare ambition among Chinese AI labs.
3. A trillion-parameter model that’s surprisingly efficient:
Kimi K2 uses a mixture-of-experts architecture (32B active params per inference) and dominates on coding/math benchmarks.
4. The secret weapon: Muon optimizer:
A new training method that doubles efficiency, cuts memory in half, and ran 15.5T tokens with zero failures. Big implications.
Most importantly, their move from closed to open source signals a broader shift in China’s AI scene — following Baidu’s pivot. But as Yang puts it: “Users are the only real leaderboard.”
👇 Check out the full post to explore what Kimi K2 can do, how to try it, and why it matters for the future of open-source LLMs:
https://huggingface.co/blog/fdaudens/moonshot-ai-kimi-k2-explained
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GeorgiaArm's
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about 2 months ago
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Join us in Austin tomorrow for AI Camp’s monthly meetup.
Arm’s Zach Lasiuk and Geremy Cohen will dive into “From Model to Product: Right-Sizing Infrastructure for Real-World Use Cases.”
RSVP here 👉 https://www.aicamp.ai/event/eventdetails/W2025071616
Arm’s Zach Lasiuk and Geremy Cohen will dive into “From Model to Product: Right-Sizing Infrastructure for Real-World Use Cases.”
RSVP here 👉 https://www.aicamp.ai/event/eventdetails/W2025071616
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Migrating the Hub from Git LFS to Xet
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We've moved over 20PB from Git LFS to Xet on the Hub without downtime or data loss. Having things "just work" on a migration of this scale is about as good as it gets.
Now, we're migrating the rest of the Hub https://huggingface.co/blog/migrating-the-hub-to-xet
But how did we get here?
In the early days of joining Hugging Face, we made a few key design decisions:
* There would be no "hard cut-over" from Git LFS to Xet
* A Xet-enabled repository should be able to contain both Xet and LFS files
* Repository migrations from LFS to Xet can run in the background without disrupting downloads or uploads
These were largely driven by our desire to ensure the community could keep working without interruption.
We cover the infrastructure making this all go in this post, specifically:
* An integral piece of infrastructure known internally as the Git LFS Bridge
* Background content migrations that run around the clock
To skip the wait and join Xet now, sign up here https://huggingface.co/join/xet
Now, we're migrating the rest of the Hub https://huggingface.co/blog/migrating-the-hub-to-xet
But how did we get here?
In the early days of joining Hugging Face, we made a few key design decisions:
* There would be no "hard cut-over" from Git LFS to Xet
* A Xet-enabled repository should be able to contain both Xet and LFS files
* Repository migrations from LFS to Xet can run in the background without disrupting downloads or uploads
These were largely driven by our desire to ensure the community could keep working without interruption.
We cover the infrastructure making this all go in this post, specifically:
* An integral piece of infrastructure known internally as the Git LFS Bridge
* Background content migrations that run around the clock
To skip the wait and join Xet now, sign up here https://huggingface.co/join/xet
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