SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.
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Updated
Jun 4, 2024 - Python
SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.
Collection of best practices, reference architectures, model training examples and utilities to train large models on AWS.
The Arcee client for executing domain-adpated language model routines
Nvidia GPU exporter for prometheus using nvidia-smi binary
DLRover: An Automatic Distributed Deep Learning System
H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://h2oai.github.io/h2o-llmstudio/
An efficient, flexible and full-featured toolkit for fine-tuning LLM (InternLM2, Llama3, Phi3, Qwen, Mistral, ...)
Fine-tuning of Flan-5T LLM for text classification
Linux LiveCD for offline AI training and inference.
Backend for the AI-copilot
LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.
A benchmark for evaluating learning agents based on just language feedback
Collection of bet practices, reference architectures, examples, and utilities for foundation model development and deployment on AWS.
SiLLM simplifies the process of training and running Large Language Models (LLMs) on Apple Silicon by leveraging the MLX framework.
Low-code framework for building custom LLMs, neural networks, and other AI models
A collection of hand on notebook for LLMs practitioner
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