4.以推理计算换取对抗鲁棒性(Trading Inference-Time Compute for Adversarial Robustness) 📄 2025 年 1 月 31 日,《Trading Inference-Time Compute for Adversarial Robustness》,详见 https://arxiv.org/abs/2501.18841 增加推理阶段的计算量(
One common optimization for the decode phase is KV caching. The decode phase generates a single token at each time step, but each token depends on the key and value tensors of all previous tokens (including the input tokens’ KV tensors computed at prefill, and any new KV tensors computed...
4. 用推理时计算量换取对抗鲁棒性 (Trading Inference-Time Compute for Adversarial Robustness) ** 1月 31 日,_用推理时计算量换取对抗鲁棒性 (Trading Inference-Time Compute for Adversarial Robustness)_**,https://arxiv.org/abs/2501.18841 在许多情况下,增加推理时计算量可以提高推理 LLM 的对抗鲁棒性,从...
Early Exit Inference LITE 在神经网络的中间层添加预测能力,当置信度较高时,token 会提前退出,最高可节省 38% FLOPS。 注意力机制优化(Attention Optimization) FlashAttention 1, 2, 3 通过内存分块实现快速、精确的注意力计算,速度与内存效率优于标准实现方式。 ROFormer 引入旋转位置编码(Rotary Position Embedding...
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这些基础模型的训练成本高昂,而且在推理过程中可能会占用大量内存和计算资源(这是一种重复性成本)。目前最热门的大型语言模型 (LLM)可以达到数百亿到数千亿的参数规模,并且根据用例,可能需要处理长输入(或上下文),这也会增加费用。 本文讨论了大型语言模型(LLM)推理中最紧迫的挑战及其实用解决方案。建议读者对...
今年以来我们观察到 LLMscaling up 的边际收益开始递减,用 RL self-play + MCTS 提升 LLM 推理能力成为下一个技术范式。在新范式下,LLM 领域的 scaling law 会发生变化:计算量变大仍会带来模型智能的提升,但会从模型参数量变大,转移到 inference-time compute 增加,也就是模型进行更多 RL 探索。
Generally speaking, LLM inference is a memory bandwidth bounded task for weight loading. Weight-only quantization (WOQ) is an effective performance optimization algorithm to reduce the total amount of memory access without losing accuracy. int4 GEMM with a weight-only quantization (WOQ) recipe speci...
So, how exactly should we think about inference speed? Our team uses four key metrics for LLM serving: Time To First Token (TTFT): How quickly users start seeing the model's output after entering their query. Low waiting times for a response are essential in real-time interactions, but ...
device:"cpu"#itrex int4 llm runtime optimizationoptimization:use_neural_speed:trueoptimization_type:"weight_only"compute_dtype:"fp32"weight_dtype:"int4" add keyuse_neural_speedand keyuse_gptqto useNeural Speedand loadGPT-Qmodel (example). ...