Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.
Low-code framework for building custom LLMs, neural networks, and other AI models
LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters
H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/
A C#/.NET library to run LLM (π¦LLaMA/LLaVA) on your local device efficiently.
Distributed LLM inference. Connect home devices into a powerful cluster to accelerate LLM inference. More devices means faster inference.
Enhances Tesseract OCR output using LLMs (local or API) for error correction, smart chunking, and markdown formatting of scanned PDFs
A simple, performant, and scalable Jax LLM!
Inference Llama 2 in one file of pure π₯
Uses ChatGPT API, Bard API, and Llama2, Python-Nmap, DNS Recon, PCAP and JWT recon modules and uses the GPT3 model to create vulnerability reports based on Nmap scan data, and DNS scan information. It can also perform subdomain enumeration to a great extent
Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs.
A 14M-parameter LLM keeps a virtual fish tank alive on an $8 ESP32-S3 β distilled from a 26B teacher into 7.56 MB, no network.