Quasar is entering its next chapter on Bittensor SN24. We are moving toward a 10T-token decentralized training run. The idea is simple Quasar Models needs more useful training, not just bigger parameter counts. Real model quality comes from tokens, data quality, training direction, and the ability to keep improving checkpoint by checkpoint. This run starts with a 5T-token phase to produce a stronger checkpoint, then continues into another 5T-token phase, reaching 10T total trained tokens. SN24 will set the direction: the starting checkpoint, the data, the training recipe, and the evaluation system. Miners become the extra compute layer. They help Quasar train faster, improve continuously, and move forward together. this would be the largest token-scale training runs ever attempted in decentralized AI. This is how we scale Quasar. Help us train it 👇

🌘 Kimi-K2.7-Code, our latest coding model, is now released and open-sourced! 🔷 Improved coding & agent performance over K2.6: 21.8% on Kimi Code Bench v2, 11.0% on Program Bench, and 31.5% on MLS Bench Lite. 🔷 Reasoning efficiency: Less overthinking, with 30% lower reasoning-token usage compared to K2.6. 🔷 Long-horizon coding: Improved instruction following, higher end-to-end coding task success rates. ⚡️ 6x High-Speed Mode coming soon! 🔌 Available today via Kimi API and Kimi Code. 🔗 Kimi Code: kimi.com/code 🔗 API: platform.moonshot.ai

We've talked a lot about how our efforts to train AI to shop will be entirely open source. Through Bittensor, we're committed to that ethos. We're excited to share our pre-print on arXiv, our code, our data and our entire post-training pipeline. Huge shoutout and thanks to @JarrodBarnes in helping us leverage this very valuable data. This is how AI is going to learn to shop.

Quasar is entering its next chapter on Bittensor SN24. We are moving toward a 10T-token decentralized training run. The idea is simple Quasar Models needs more useful training, not just bigger parameter counts. Real model quality comes from tokens, data quality, training direction, and the ability to keep improving checkpoint by checkpoint. This run starts with a 5T-token phase to produce a stronger checkpoint, then continues into another 5T-token phase, reaching 10T total trained tokens. SN24 will set the direction: the starting checkpoint, the data, the training recipe, and the evaluation system. Miners become the extra compute layer. They help Quasar train faster, improve continuously, and move forward together. this would be the largest token-scale training runs ever attempted in decentralized AI. This is how we scale Quasar. Help us train it 👇

This is our new design for the future of Quasar models using every bit of power Bittensor can offer to make Quasar a SOTA model. We do not need to win against closed ones, but we do need a fair fight. Let’s do it!! Keep an eye on this. We’ll be sharing the design and a lot of tests together soon.

Quasar is entering its next chapter on Bittensor SN24. We are moving toward a 10T-token decentralized training run. The idea is simple Quasar Models needs more useful training, not just bigger parameter counts. Real model quality comes from tokens, data quality, training direction, and the ability to keep improving checkpoint by checkpoint. This run starts with a 5T-token phase to produce a stronger checkpoint, then continues into another 5T-token phase, reaching 10T total trained tokens. SN24 will set the direction: the starting checkpoint, the data, the training recipe, and the evaluation system. Miners become the extra compute layer. They help Quasar train faster, improve continuously, and move forward together. this would be the largest token-scale training runs ever attempted in decentralized AI. This is how we scale Quasar. Help us train it 👇

Quasar is entering its next chapter on Bittensor SN24. We are moving toward a 10T-token decentralized training run. The idea is simple Quasar Models needs more useful training, not just bigger parameter counts. Real model quality comes from tokens, data quality, training direction, and the ability to keep improving checkpoint by checkpoint. This run starts with a 5T-token phase to produce a stronger checkpoint, then continues into another 5T-token phase, reaching 10T total trained tokens. SN24 will set the direction: the starting checkpoint, the data, the training recipe, and the evaluation system. Miners become the extra compute layer. They help Quasar train faster, improve continuously, and move forward together. this would be the largest token-scale training runs ever attempted in decentralized AI. This is how we scale Quasar. Help us train it 👇

Quasar is entering its next chapter on Bittensor SN24. We are moving toward a 10T-token decentralized training run. The idea is simple Quasar Models needs more useful training, not just bigger parameter counts. Real model quality comes from tokens, data quality, training direction, and the ability to keep improving checkpoint by checkpoint. This run starts with a 5T-token phase to produce a stronger checkpoint, then continues into another 5T-token phase, reaching 10T total trained tokens. SN24 will set the direction: the starting checkpoint, the data, the training recipe, and the evaluation system. Miners become the extra compute layer. They help Quasar train faster, improve continuously, and move forward together. this would be the largest token-scale training runs ever attempted in decentralized AI. This is how we scale Quasar. Help us train it 👇

Today we’re releasing Quasar-Preview! Our first public proof that the Quasar architecture works at real scale. [ 18B MoE - 2B active / 5M context ] Built with Loop Transformer Quasar attention Trained on Bittensor through decentralized infrastructure 👇

Are you up for a challenge? openai.com/parameter-golf

mythos will be bad ON PURPOSE on ai "frontier llm research" tasks, this is very very sad for the research community also the fact that this is un purpose not visible to the user is crazy

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use. Its capabilities exceed those of any model we’ve ever made generally available.