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## ⚡ Performance & Efficiency - Only a fraction of the model’s parameters are active at a time for each token - Parallel attention and MoE pathways maximize GPU usage - Sparse activation + quantized caches drastically reduces memory requirements ## 🔮 Future Plans - Faster attention with FlashAttention - Expert parallelism across devices - Advanced quantization methods (GPTQ, AWQ) - Pipeline parallelism and gradient checkpointing for huge-scale models We’re thrilled to see what the community can do with CodeBase-280B and can’t wait to continue development in the coming months. \- Conner | 404Development

# 🚀 Introducing CodeBase-280B: Phase 1 Hey everyone, We’re excited to share the first phase of CodeBase-280B, our next-generation language model built for performance, scalability, and advanced AI capabilities. Here’s what makes it special: 💡 Key Highlights - Mixture of Experts (MoE): 128 experts with 9 active per token, meaning the model only uses the parts it needs for efficiency and speed. - Massive Context Window: Handles up to 384,000 tokens at once, allowing it to understand extremely long documents or conversations. - Compressed Attention: Optimized memory usage with partial KV sharing to keep inference fast. - Efficient Inference: 8-bit quantization with KV cache compression reduces memory requirements without sacrificing quality. - Parallel & Distributed: Designed for multi-GPU setups with support for distributed training and mixed precision (bfloat16) for optimal performance. ## 📊 Specs at a Glance - Hidden Size: 7,168 - Layers: 75 - Attention Heads: 52 - Experts: 128 total, 9 active - Context Window: 384,000 tokens - Parameters: ~280B total (~18B active at a time) - Vocabulary: 51,200 tokens ## 🛠️ Project Structure & Usage CodeBase-280B includes everything you need to train, test, and run inference on the model, including: - Transformer architecture and MoE modules - Compressed attention and RoPE positional encoding - Quantization utilities for memory-efficient inference - Training scripts with multi-GPU support - Open-source configuration for customization Installation is simple: clone the repo and install dependencies via pip. You can train, generate text, benchmark, or run tests with provided scripts.

# Changelog - TimekeeperV2 **Version**: 2.1.04 **Date**: 2025-12-02 ## Overview: TimekeeperV2 has reached a mature and stable state. All planned core features are now implemented. Going forward, the focus will be on maintaining performance, reliability, and improving existing features, rather than adding new functionality. ## What's New Since Last Update: - Final refinements to time tracking and scheduling features - Bug fixes and stability improvements - Optimizations for server performance - Updated user interface for easier navigation ## Maintenance Focus Moving Forward: - Ensuring TimekeeperV2 continues running smoothly across all servers - Improving response times and reliability - Fixing bugs and addressing user-reported issues promptly - Minor quality-of-life improvements to features **Notes:** TimekeeperV2 will not be receiving any major new features for the forseeable future. This allows the team to focus on quality maintenance and prepare for exciting new projects. Ping:|| @Members||

(API Key was given a filler for the time being, we're pushing updates to access your API Key as I write this)