TinyZero's distributed training scheme
TinyZero has designed a unique parametric parallel architecture, which can automatically adapt hardware configuration according to the model size. For models with parameters below 1.5B, the system provides a complete single-GPU support solution; when dealing with models with parameters above 3B, multi-GPU parallel computation is realized through the ROLLOUT_TP_SIZE parameter, which is especially good for QWen-2.5-3B Instruct, a model that requires complex reasoning ability. The technical implementation uses ray distributed framework combined with vLLM 0.6.3 attention optimization, with flash-attn's memory optimization technology, so that the efficiency of multi-card communication is increased by more than 40%.
- Hardware adaptation: automatic recognition of N_GPUS environment variables
- Key Technology: XFORMERS Attention Backend Guarantees Multi-Card Consistency
- Scalability: supports seamless scaling of parameter sizes
This answer comes from the articleTinyZero: A Low-Cost Replication of DeepSeeK-R1 Zero's Epiphany EffectThe
