HomeJD.com Unveils JoyAI-LLM-Flash Model to Drive AI Progress
JD.com made its latest large language model, JoyAI-LLM-Flash, publicly available on the Hugging Face platform on February 14th. The model boasts a total of 4.8 billion parameters, with 3 billion activated during inference. It was pre-trained on 20 trillion text tokens, exhibiting strong proficiency in understanding contemporary knowledge, logical reasoning, and programming tasks.
JoyAI-LLM-Flash utilizes a novel FiberPO optimization framework, which incorporates fiber bundle theory into reinforcement learning. Combined with the Muon optimizer and dense multi-token prediction (MTP) technology, this approach effectively addresses the instability challenges common when scaling traditional models. Compared to versions without MTP, its throughput has increased by 1.3 to 1.7 times, significantly boosting both training efficiency and practical application potential.
The model is built on a mixture-of-experts (MoE) architecture, featuring 40 layers. It supports a context length of 128K tokens and has a vocabulary of 129K, representing a substantial step forward for JD.com in the field of artificial intelligence.
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![RalphMitchell]()
JD這模型參數規模不小，但實際推理只開30億？有點好奇這『部分激活』的設計是為了省成本還是技術限制。不過能開源總是好事，希望後續能多點實際應用案例分享，光看數字有點虛啊😂
JD.com made its latest large language model, JoyAI-LLM-Flash, publicly available on the Hugging Face platform on February 14th. The model boasts a total of 4.8 billion parameters, with 3 billion activated during inference. It was pre-trained on 20 trillion text tokens, exhibiting strong proficiency in understanding contemporary knowledge, logical reasoning, and programming tasks.
JoyAI-LLM-Flash utilizes a novel FiberPO optimization framework, which incorporates fiber bundle theory into reinforcement learning. Combined with the Muon optimizer and dense multi-token prediction (MTP) technology, this approach effectively addresses the instability challenges common when scaling traditional models. Compared to versions without MTP, its throughput has increased by 1.3 to 1.7 times, significantly boosting both training efficiency and practical application potential.
The model is built on a mixture-of-experts (MoE) architecture, featuring 40 layers. It supports a context length of 128K tokens and has a vocabulary of 129K, representing a substantial step forward for JD.com in the field of artificial intelligence.
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JD這模型參數規模不小，但實際推理只開30億？有點好奇這『部分激活』的設計是為了省成本還是技術限制。不過能開源總是好事，希望後續能多點實際應用案例分享，光看數字有點虛啊😂
