Les fonctionnalités principales de bindsnet/bindsnet sont : Training and Orchestration, Machine Learning Libraries.
Les alternatives open-source à bindsnet/bindsnet incluent : automl/hpbandster — a distributed Hyperband implementation on Steroids. axolotl-ai-cloud/axolotl — Axolotl is a configuration-driven framework designed for the fine-tuning, evaluation, and quantization of large… codefuse-ai/mftcoder — High Accuracy and efficiency multi-task fine-tuning framework for Code LLMs. This work has been accepted by KDD 2024. combust/mleap — MLeap: Deploy ML Pipelines to Production. continualai/avalanche — Avalanche: an End-to-End Library for Continual Learning based on PyTorch. apple/corenet — Corenet is a deep learning training framework and computer vision model library designed for developing neural…
Axolotl is a configuration-driven framework designed for the fine-tuning, evaluation, and quantization of large language models. It functions as a comprehensive orchestrator for distributed training, enabling users to manage complex workflows across multi-node and multi-GPU environments. By utilizing structured configuration files, the platform streamlines the setup of training parameters, dataset paths, and hardware distribution strategies. The project distinguishes itself through its support for diverse training methodologies, including full-parameter tuning, parameter-efficient adaptation,
High Accuracy and efficiency multi-task fine-tuning framework for Code LLMs. This work has been accepted by KDD 2024.
Corenet is a deep learning training framework and computer vision model library designed for developing neural networks across vision, text, and audio modalities. It functions as a distributed training orchestrator for scaling workloads across multiple compute nodes and provides a multimodal data pipeline for processing image, text, and video data. The project includes a model conversion toolkit for transforming weights and architectures between different machine learning frameworks. It also provides tools for optimizing model performance on Apple Silicon and reducing response latency in gene
