Large Language Models as Evolutionary Optimizers Shengcai Liu, Caishun Chen, Xinghua Qu, Ke Tang, Yew-Soon Ong. [abs], Preprint 2023.11 Large Language Models can Implement Policy Iteration Ethan Brooks, Logan Walls, Richard L. Lewis, Satinder Singh. [abs], NeurIPS 2023 Large Language Models fo...
QLoRA: Another PEFT based on LoRA, which also quantizes the weights of the model in 4 bits and introduce paged optimizers to manage memory spikes. Combine it with Unsloth to run it efficiently on a free Colab notebook. Axolotl: A user-friendly and powerful fine-tuning tool that is used...
Training LLMs efficiently at scale necessitates various system innovations, such as statesharding optimizers [79], meticulous model placement using data, pipeline, and tensor parallelisms [67, 68, 113]. 数据准备。初始阶段涉及收集和预处理训练数据,可分为两部分:(1) 预训练数据,包括从公共或私人...
Overview. Adaptive optimizers such as AdaGrad [22], Adam [33], or AdaFactor [54] scale the update differently for each individual parameter. This is often conceptualized a per-parameter learning rate. For instance, in Adam/AdamW, per-parameter updates are scaled by the inverse root of the ex...
Yang, C. et al. Large language models as optimizers. In12th International Conference on Learning Representations(ICLR, 2023). Zheng, C., Zhou, H., Meng, F., Zhou, J. & Huang, M. On large language models’ selection bias in multi-choice questions. In12th International Conference on Learn...
We also performed some of our experiments by fine-tuning the GPT-J-6B model52,53(which has been trained on the Pile dataset54) on consumer hardware using 8-bit quantization55and 8-bit optimizers56in addition to the low-rank adaptation (LoRA) technique57. ...
https://github.com/zjunlp/Prompt4ReasoningPapers 本文对「基于语言模型提示学习的推理」的最新进展进行了梳理,包括预备知识、提示推理方法的分类、深入的比较和讨论、开放的基准和资源、以及未来的潜在方向。 预备知识 对于标准的提示(Prompt)学习,给定推理问题、提示和参数化的概率模型,推理任务的目标是最大化答案的...
In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies. The resulting models not only preserve the inherent reasoning and decision-making capabilities of...
Trade-off of large language model sizes Issues and questions related to tensor precision What to chose between fp32, fp16, bf16 Mixed-precisions for optimizers, weights, specifics modules How to finetune and integrate a model trained in a precision in another precision Selecting training hyper-...
the training loss. The specific hyperparameter settings utilize default configurations, which can be customized and adjusted atcustom_optimizers. It is important to note that the evaluations were conducted over a duration of 0.1 epochs to provide a preliminary insight into the optimizers' effectiveness...