This is the Windows app named PEFT whose latest release can be downloaded as GPTQQuantization,Low-levelAPI.zip. It can be run online in the free hosting provider OnWorks for workstations.
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PEFT
DESCRIPTION
Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. Fine-tuning large-scale PLMs is often prohibitively costly. In this regard, PEFT methods only fine-tune a small number of (extra) model parameters, thereby greatly decreasing the computational and storage costs. Recent State-of-the-Art PEFT techniques achieve performance comparable to that of full fine-tuning.
Features
- Accelerate for large scale models leveraging DeepSpeed and Big Model Inference
- Get comparable performance to full finetuning by adapting LLMs to downstream tasks using consumer hardware
- GPU memory required for adapting LLMs on the few-shot dataset
- Parameter Efficient Tuning of Diffusion Models
- GPU memory required by different settings
- Parameter Efficient Tuning of LLMs for RLHF components such as Ranker and Policy
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/peft.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
