LESS

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LESS is a Data & Analytics tool that selects influential data for targeted instruction tuning. It helps AI researchers and machine learning engineers optimize model training by identifying the most impactful data points.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is LESS?

LESS is a data selection method designed for targeted instruction tuning, as detailed in its ICML 2024 paper. This tool allows users to select influential data to induce a specific target capability in large language models. The process involves warmup training, building a gradient datastore, selecting data for a particular task based on influence scores, and then training the model with the curated dataset. It supports various instruction tuning datasets like Flan v2, COT, Dolly, and Open Assistant, and provides evaluation capabilities for datasets such as MMLU, Tydiqa, and BBH. This method is crucial for optimizing training efficiency and model performance by focusing on the most impactful data.

Best used for

Ideal for data scientists and developers who need to optimize instruction tuning, improve model performance on specific tasks, and efficiently utilize training data. Especially valuable for researchers working with large language models to achieve targeted capabilities with reduced data requirements.

Common actions

select data

optimize training

tune models

improve performance

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Capabilities

Key features

Influential data selection
Targeted instruction tuning
Gradient datastore building
Warmup training
LoRA method support
Influence score calculation

Target Audience

data scientistdeveloper

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of data can LESS select?

LESS can select influential data from various instruction tuning datasets, including Flan v2, COT, Dolly, and Open Assistant. It is designed to work with data that can be used to induce a target capability in large language models.

What is the primary benefit of using LESS for instruction tuning?

The primary benefit of LESS is its ability to identify and select the most influential data points for a specific target capability. This allows for more efficient and effective instruction tuning, potentially reducing training time and improving model performance on desired tasks.

Does LESS support full-parameter fine-tuning?

Yes, while LESS primarily details training with the LoRA method, the documentation notes that full-parameter fine-tuning can also be performed by adjusting or removing the LoRA training parameters in the provided scripts.

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