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
even for highly complex queries. Thus, the LLM is ideal for high-performing computing, enterprise-level AI systems, and cutting-edge research. This makes it a model that organizations can rely on for AI innovation at scale.
Data splitting divides datasets appropriately for training and testing purposes so machine learning models can generalize. The most common approach is to divide the dataset into training and test sets, typically using a ratio of 70-30 or 80-20. The training set is used to teach the model, whi...
These techniques filter token selection. Top-k selects the top-k most likely tokens, ensuring high-quality output. Top-p, on the other hand, sets a cumulative probability threshold, retaining tokens with a total probability above it. Top-k is useful for avoiding nonsensical responses, while ...
LLMs can tease out words in the data, understand the context of words and assign sets of words to themes. Using that information, analysts can then adjust the LLMmodel trainingfor subsequent predictive analytics operations. Examples of data analysis with LLMs ...
This will include more robust mechanisms for generating question-answer test sets as well as additional metrics, such as accuracy and context relevance. Next steps By combining LLM-generated knowledge graphs and graph machine learning, GraphRAG enables us to answer important classes of questions ...
Mix and match different sets of data to experiment and create better models. Combine datasets with CombinedStreamingDataset. As an example, this mixture of Slimpajama & StarCoder was used in the TinyLLAMA project to pretrain a 1.1B Llama model on 3 trillion tokens. from litdata import Streami...
Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps. redisdatabasenosqlkey-valuecachemessage-broker ...
The NVIDIA-powered AI workstation enables our data scientists to run end-to-end data processing pipelines on large data sets faster than ever. Leveraging RAPIDS to push more of the data processing pipeline to the GPU reduces model development time which leads to faster deployment and business ins...
支持GroupAgg/HashAgg/PlainAgg,及Agg所有特性,包含AggFilter、GroupingSets、RollUp/Cude等。 支持HashJoin/NestLoopJoin,完整支持Left/Right/Full/Inner/Anti/Semi/Not-exist-in七种Join规则。 支持Sort所有场景,包含FullSort/TopNSort。 Agg/Join/Sort均支持落盘功能。