Sketch: A Toolkit for Streamlining LLM Operations
Xin Jiang, Xiang Li, Wenjia Ma, Xuezhi Fang, Yiqun Yao, Naitong Yu,, Xuying Meng, Peng Han, Jing Li, Aixin Sun, Yequan Wang

TL;DR
Sketch is a comprehensive toolkit that simplifies the deployment and management of large language models by providing structured task schemas, user-friendly interfaces, and open-source resources for output control.
Contribution
It introduces a modular toolkit with schemas, interactive processes, datasets, and an open-source model to enhance LLM usability across diverse NLP tasks.
Findings
Facilitates structured output control for LLMs
Provides an open-source dataset and tools for dataset construction
Includes an open-source model based on LLaMA3-8B-Instruct
Abstract
Large language models (LLMs) represented by GPT family have achieved remarkable success. The characteristics of LLMs lie in their ability to accommodate a wide range of tasks through a generative approach. However, the flexibility of their output format poses challenges in controlling and harnessing the model's outputs, thereby constraining the application of LLMs in various domains. In this work, we present Sketch, an innovative toolkit designed to streamline LLM operations across diverse fields. Sketch comprises the following components: (1) a suite of task description schemas and prompt templates encompassing various NLP tasks; (2) a user-friendly, interactive process for building structured output LLM services tailored to various NLP tasks; (3) an open-source dataset for output format control, along with tools for dataset construction; and (4) an open-source model based on…
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Taxonomy
TopicsSimulation Techniques and Applications · Distributed and Parallel Computing Systems · Scheduling and Optimization Algorithms
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Attention Is All You Need · Cosine Annealing · Linear Layer · Residual Connection · Linear Warmup With Cosine Annealing · Attention Dropout · Discriminative Fine-Tuning · Multi-Head Attention · Byte Pair Encoding
