🐱 LongCat-2.0 is now fully open-source — MIT licensed, no restrictions.
Since our launch a few days ago, the response from the community has been incredible.
Thank you for all the feedback, discussions, and interest.
Today, we’re releasing the model weights and inference code to everyone.
◆ 1.6T MoE · ~48B active · 1M token context
◆ Agent-native: Integrates directly with Claude Code, OpenClaw, and Hermes Agent
◆ Deployment: Support both GPU and NPU platforms— verified on large-scale domestic clusters
📑 Tech Blog: https://t.co/W9EYWDW3Y9
🤗 HuggingFace: https://t.co/EP2szwIhu2
💻 GitHub: https://t.co/qJzXMQcC2z
🪄 ModelScope: https://t.co/nKt5Wh3m8Y
👇 Inference Code
GPU: https://t.co/Tq8QKvneH6
NPU: https://t.co/l96ebhFxN6
Customizable USB dock/hub with rotary encoder, round touchscreen display.
https://t.co/n2ACJbOBoY
The HALO TOUCH V2 USB 2.0 dock features two USB Type-A ports, one USB Type-C port, two microSD card readers, and a 100Mbps Ethernet port. Its 360 × 360 IPS touchscreen is used as a Pomodoro timer, photo and animation viewer, MP3 player, audio spectrum visualizer, and an AIDA64 system monitoring dashboard.
The rotary encoder provides Microsoft Surface Dial-compatible controls on Windows, while 2.4GHz Wi-Fi enables clock synchronization and OTA firmware updates. It also supports USB PD power input and customization for themes, media, and animations using files on a microSD card.
anoooother one!
Introducing ⭐Grug-30b-a3b!⭐
Grug is an open-source experimental model build on top of Qwen-3.6-30b-a3b. Designed to replicate the efficiency of GPT-5.5 by cutting down thinking into only-necessary chunks.
Grug outputs on average ~69.8% less thinking tokens, while preforming within a ~2% total margin of base Qwen-3.6
This lead to significantly faster generation and less context filling, leading to exponential decreases in generation speed.
Download it here: https://t.co/8FKqE24ptg
Introducing ⭐Grug-12b!⭐
Grug is an open-source experimental model build on top of Gemma-4-12b. Designed to replicate the efficiency of GPT-5.5 by cutting down thinking into only-necessary chunks.
Grug outputs on average ~69.8% less thinking tokens, while preforming within a ~2% total margin of base Gemma-4
This lead to significantly faster generation and less context filling, leading to exponential decreases in generation speed.
Download it here: https://t.co/Se5O0SiYdw
thanks to @LambdaAPI for the credits necessary for this SFT