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An awesome & curated list of best LLMOps tools for developers
Self-hosted game stream host for Moonlight.
Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI
Intrinsic Motivation from Artificial Intelligence Feedback
🔍 LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your d…
A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book
Covid's Impact on Trade Volumes in Canada: A Regression Discontinuity Approach with Time Series Predictions
Represent trained machine learning models as Pyomo optimization formulations
A python library for generating cohesive Stable Diffusion Prompts
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Stable Diffusion, SDXL, LoRA Training, DreamBooth Training, Automatic1111 Web UI, DeepFake, Deep Fakes, TTS, Animation, Text To Video, Tutorials, Guides, Lectures, Courses, ComfyUI, Google Colab, R…
The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.
📋 A list of open LLMs available for commercial use.
Community interface for generative AI
Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
Stable Diffusion web UI
Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.
Browser extension to replace Youtube comments with Reddit comments or view the Reddit comments of any webpage.
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
Approaching (Almost) Any Machine Learning Problem
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.