Low-code framework for building custom LLMs, neural networks, and other AI models
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Updated
Jun 17, 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
dstack is an easy-to-use and flexible container orchestrator for running AI workloads in any cloud or data center.
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.
LLM (Large Language Model) FineTuning
Open Source LLM toolkit to build trustworthy LLM applications. TigerArmor (AI safety), TigerRAG (embedding, RAG), TigerTune (fine-tuning)
FineTune LLMs in few lines of code (Text2Text, Text2Speech, Speech2Text)
Sequence Parallel Attention for Long Context LLM Model Training and Inference
SiLLM simplifies the process of training and running Large Language Models (LLMs) on Apple Silicon by leveraging the MLX framework.
Finetune LLMs on K8s by using Runbooks
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