Low-code framework for building custom LLMs, neural networks, and other AI models
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Updated
Jun 10, 2024 - Python
Low-code framework for building custom LLMs, neural networks, and other AI models
SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.
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, ...)
Code examples and resources for DBRX, a large language model developed by Databricks
DLRover: An Automatic Distributed Deep Learning System
Nvidia GPU exporter for prometheus using nvidia-smi binary
LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.
irresponsible innovation. Try now at https://chat.dev/
The official repo of Aquila2 series proposed by BAAI, including pretrained & chat large language models.
Open Source LLM toolkit to build trustworthy LLM applications. TigerArmor (AI safety), TigerRAG (embedding, RAG), TigerTune (fine-tuning)
LLM (Large Language Model) FineTuning
Tune LLM in few lines of code
Sequence Parallel Attention for Long Context LLM Model Training and Inference
Finetune LLMs on K8s by using Runbooks
SiLLM simplifies the process of training and running Large Language Models (LLMs) on Apple Silicon by leveraging the MLX framework.
Collection of best practices, reference architectures, model training examples and utilities to train large models on AWS.
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