Search Results for "gpu hardware"

GPT4All is an open-source project that allows users to run large language models (LLMs) locally on their desktops or laptops, eliminating the need for API calls or GPUs. The software provides a simple, user-friendly application that can be downloaded and run on various platforms, including Windows, macOS, and Ubuntu, without requiring specialized hardware. It integrates with the llama.cpp implementation and supports multiple LLMs, allowing users to interact with AI models privately. This...

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...

RamaLama is an open-source developer tool that simplifies working with and serving AI models locally or in production by leveraging container technologies like Docker, Podman, and OCI registries, allowing AI inference workflows to be treated like standard container deployments. It abstracts away much of the complexity of configuring AI runtimes, dependencies, and hardware optimizations by detecting available GPUs (or falling back to CPU) and automatically pulling a container image...

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. Since TFP inherits the benefits of TensorFlow, you can build, fit, and deploy a model using a single language throughout the lifecycle of model exploration and production. ...

...Running these deep learning models on large document or video datasets is costly and time-consuming. For example, the state-of-the-art object detection model takes multiple GPU years to process just a week’s videos from a single traffic monitoring camera. Besides the money spent on hardware, these models also increase the time that you spend waiting for the model inference to finish.