过去关于数据改进的基准测试工作包括数据集蒸馏、课程学习和迁移学习。在 DataComp 和 DataPerf 中,参与者针对视觉、视觉-语言和语音任务,使用固定模型和训练配方迭代数据集。BabyLM 挑战 Loose track 专注于在 1.25 亿到 2.2 亿参数的模型上,通过 1000 万到 1 亿个 tokens 进行高效的语言模型开发。使用 200 万亿...
Data is still king: Companies like OpenAI and Google have access to massive proprietary datasets, giving them a significant edge in training superior models. Cloud AI will likely dominate enterprise adoption: Many businesses prefer ready-to-use AI services over the hassle of setting up their own...
AVEVA, HighByte and Hitachi Vantara offer industrial DataOps platforms to meet diverse data management needs, while others, such as Timeseer.ai, provide specific tools
Boost your LLM capabilities with our comprehensive AI training data solutions. From data collection and supervised fine-tuning (SFT) to reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO), we cover the entire lifecy
5、Starcoder Data 数据量：783GB的代码，86种编程语言 数据内容：Starcoder Data是一个以编程为中心的...
LLMs are known for their tendencies to ‘hallucinate’ and produce erroneous outputs that are not grounded in the training data or based on misinterpretations of the input prompt. They are expensive to train and run, hard to audit and explain, and often provide inconsistent answers. ...
megatron/training/ 包含训练过程的控制逻辑,如模型初始化、训练循环、评估等。 三、关键技术点分析 并行技术 数据并行(Data Parallelism):是最基本的并行方式,通过将数据分批次分配到不同的 GPU 上进行处理来实现。Megatron-Core 通过 PyTorch 的分布式数据并行功能来实现。 模型并行(Model Parallelism):针对模型的...
Large language models (LLMs) are advanced AI systems designed to understand human language intricacies and generate intelligent, creative responses to queries. Successful LLM are trained on enormous data sets typically measured in petabytes. This training data is sourced from books, articles, websites...
from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset from llmtuner.extras.constants import IGNORE_INDEX from llmtuner.extras.misc import get_logits_processor from llmtuner.extras.ploting import plot_loss from...
1.【大语言模型基础】GPT(Generative Pre-training )生成式无监督预训练模型原理2023-04-152.【大语言模型基础】图解GPT原理-60行numpy实现GPT2023-12-263.【LLM应用】基于GPT3.5的代码编辑器Cursor试用-智能代码编辑助手2023-04-164.【大语言模型基础】Transformer模型Torch代码详解和训练实战2023-10-245.【LLM】在...