Today, we are releasing a new version of K2 (K2-V2), a 360-open LLM built from scratch as a superior base for reasoning adaptation, while still excelling at core LLM capabilities like conversation, knowledge retrieval, and long-context understanding. K2 fills a major gap: highly capable models with no transparency. Instead of releasing only weights, we’re sharing the full training story — dataset recipes, mid-training checkpoints, logs, code, and evaluation tools. That’s 360-open. What’s inside: • 70B dense transformer engineered as a reasoning-enhanced base model • Native 512K context (extendable via RoPE scaling) • Mid-training reasoning phase • Strong tool-use scaffolding What we’re open-sourcing: • 250M+ reasoning traces (math, planning, multi-step logic) • Full pre- & mid-training data compositions • All mid-training checkpoints • Training logs, code, Eval360 Performance: • GPQA-Diamond: 55.1% mid-training → 69.3% after SFT (strongest fully open 70B model) • KK-8 Logic Puzzles: 83% — competitive with DeepSeek-R1 & OpenAI o3-mini-high • ArenaHard V2: 62.1% — close to Qwen3 235B • Outperforms Qwen2.5-72B and approaches Qwen3-235B despite being smaller and fully transparent. 🔗 The Model: bit.ly/3KIYwuo 🔗Technical Report: bit.ly/49V8h2U 🔗Blog: bit.ly/49V7gb6
