We wrote a full technical guide on how to train a bilingual (ES/EN) LLM from scratch: TinyQwen.
Covers: - Hybrid architecture based on Qwen3.5 - Pre-training with 15B tokens - Cost benchmark between H200 and B200 - Post-training with SFT + LoRA - Full code and data, open source
With ~$11 of compute on an H200 we ran an initial training run, enough to validate the full architecture and pipeline.
Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.