Llmtools

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LLMTools is an open-source library for running and finetuning Large Language Models (LLMs) on consumer GPUs. It enables finetuning in 2-bit, 3-bit, and 4-bit precision using the ModuLoRA algorithm.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is llmtools?

LLMTools is an open-source Python library designed for efficiently running and finetuning Large Language Models (LLMs) in low-resource environments, specifically on consumer-grade GPUs. It features advanced finetuning capabilities in 2-bit, 3-bit, and 4-bit precision, leveraging the innovative ModuLoRA algorithm. The library provides an easy-to-use Python API for various tasks including quantization, inference, and finetuning. A key differentiator is its modular support for multiple LLMs, quantizers, and optimization algorithms, allowing for flexibility and integration with the HuggingFace Hub for sharing finetuned models. Developed as a research project at Cornell University, LLMTools is based on cutting-edge publications like ModuLoRA and QuIP, making it a valuable tool for researchers and developers working with LLMs.

Best used for

Ideal for developers and data scientists who need to finetune large language models on consumer-grade GPUs, experiment with low-bit quantization, and integrate with the HuggingFace ecosystem. Especially valuable for researchers and practitioners aiming to reduce computational resource requirements for LLM development and deployment.

Common actions

finetune LLMs

quantize LLMs

run LLMs

optimize LLM performance

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Capabilities

Key features

2-bit/3-bit/4-bit finetuning
ModuLoRA algorithm
Python API
Modular LLM support
HuggingFace Hub integration
Low-precision LoRA
Quantization, inference, finetuning

Target Audience

developerdata scientistprofessorstudent

Integrations

hugging-face

Pricing & Plans

Open Source

Free

FAQs

What hardware is required to run LLMTools?

LLMTools requires a UNIX environment with Python (3.9) and PyTorch (tested with 2.1.1 with 12.1 Cuda). It also needs an NVIDIA GPU with Pascal architecture or newer. Other platforms are currently unsupported, emphasizing its focus on specific hardware configurations.

Can LLMTools finetune LLMs with very low precision?

Yes, LLMTools is specifically designed for low-resource settings, enabling LLM finetuning in 2-bit, 3-bit, and 4-bit precision. This is achieved through the ModuLoRA algorithm and integration with state-of-the-art quantizers like QUIP#.

Does LLMTools support distributed training across multiple GPUs?

Yes, LLMTools supports scaling training across multiple GPUs using both Naive Pipeline Parallel (NPP) training and Data Distributed Parallel (DDP) training. This allows users to leverage available hardware for more efficient finetuning of quantized models.

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Coding & Development › Open Source & Models

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