How AI models are trained — datasets, fine-tuning, and the methods behind building and improving machine learning systems.
![Open-source single-GPU reproductions of Cartridges and STILL for neural KV-cache compaction [P] image](/_next/image?url=https%3A%2F%2Fbestaifor.s3.eu-west-1.amazonaws.com%2Fopen_source_single_gpu_reproductions_of_cartridges_featured_954b9a265f.jpg&w=3840&q=75)
Neural KV-cache compaction — using learned compression rather than heuristic eviction — is one of the more credible paths to running long-context LLMs without bleeding GPU memory. Cartridges and STILL are two recent...
![DeepSeek V4 paper full version is out, FP4 QAT details and stability tricks [D] image](/_next/image?url=https%3A%2F%2Fbestaifor.s3.eu-west-1.amazonaws.com%2Fdeepseek_v4_paper_full_version_is_out_fp4_qat_deta_featured_e67ef34cd6.jpg&w=3840&q=75)
DeepSeek's V4 technical paper documents a working FP4 quantization-aware training pipeline for a frontier-scale mixture-of-experts model — a meaningful step beyond the FP8 approach the V3 paper introduced. The...
Three Chinese LLMs — DeepSeek-V3, Qwen2.5-Coder, and Kimi k1.5 — have each made distinct training bets, and the benchmark divergence is now large enough to matter when selecting a model for production. Coding...