๊ฐ€์ž… ํ›„ ์ดˆ๋Œ€ ๋งํฌ๋ฅผ ๊ณต์œ ํ•˜๋ฉด ๋™์˜์ƒ ์žฌ์ƒ ๋ฐ ์ดˆ๋Œ€ ๋ณด์ƒ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

vLLM
@vllm_project
A high-throughput and memory-efficient inference and serving engine for LLMs. Join to discuss together with the community!
๊ฐ€์ž… March 2024
36 ํŒ”๋กœ์ž‰ ์ค‘    50.2K ํŒฌ
Excited to see @peano_ai run full-parameter RL for @XiaomiMiMo 310B MiMo-V2.6 on TPUs, across 1,000+ TPUs. ๐Ÿš€๐Ÿš€ vLLM drives the rollouts, bitwise-matched with the trainer in validation. All 310B params move across the ICI fabric in under 2s. ๐Ÿ”—
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We enable full-parameter RL on TPUs: MiMo-V2.6 at 310B, plus other stable training runs of 1,000+ steps across 1,000+ TPUs. With JAX, scaling up is a config change, not a rewrite. We built on that with optimized vLLM inference for faster rollouts and full bitwise trainerโ€“sampler agreement in validation. Trainer and sampler share one TPU ICI fabric. All 310B MiMo-V2.6 parameters transfer in <2 seconds.
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