suryaputral's Stars
mustafaaljadery/gemma-2B-10M
Gemma 2B with 10M context length using Infini-attention.
xai-org/grok-1
Grok open release
mlabonne/llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
redis-developer/ArXivChatGuru
Use ArXiv ChatGuru to talk to research papers. This app uses LangChain, OpenAI, Streamlit, and Redis as a vector database/semantic cache.
microsoft/autogen
A programming framework for agentic AI 🤖
aymenfurter/azure-transcript-search-openai-demo
Sample ChatGPT-style Q&A app via RAG-pattern on Video transcripts.
deep-floyd/IF
google/dreambooth
andrewmitrofanov/siamese-artists
asahi417/wikiart-image-dataset
We release WikiART Crawler, a python-library to download/process images from WikiART via WikiART API, and two image datasets: `WikiART Face` and `WikiART General`.
LiLittleCat/awesome-free-chatgpt
🆓免费的 ChatGPT 镜像网站列表,持续更新。List of free ChatGPT mirror sites, continuously updated.
yousefebrahimi0/1000-AI-collection-tools
More than 1000 Artificial Intelligence AI-powered tools - categorized & updated
nalgeon/pokitoki
Humble GPT Telegram Bot
zeke/solresol
code, design experiments, and collected documents concering Solresol, the 'langue musicale universelle'
Agerrr/Automated_Music_Transcription
A program that automatically transcribes a music file with polyphonic piano music in .wav format to sheet notes.
Music-and-Culture-Technology-Lab/omnizart
Omniscient Mozart, being able to transcribe everything in the music, including vocal, drum, chord, beat, instruments, and more.
chrisdonahue/sheetsage
Transcribe music into lead sheets!
nucular/mmpi-2
An implementation of the MMPI-2 (Minnesota Multiphasic Personality Inventory rev. 2)
kosciolek/MMPI-2
Kalkulator MMPI-2 https://kosciolek.github.io/MMPI-2/
TGDivy/MBTI-Personality-Classifier
A model which uses your social media posting predict your MBTI personality type.
Neoanarika/MBTI
MBTI text classifer trained on the Kaggle MBTI dataset
drprasannakulkarni/doshaprediction
Ayurveda- the oldest medical system known to mankind, treats the person using herbs. This traditional science strongly believes that the diseases are due to an imbalance in Body humors called- Doshas. If these Doshas increases, they will cause disease. To treat the disease, Ayurveda uses various herbs (and some minerals too). These herbs act on the Doshas through- Rasa (taste): The taste of the drug will have an influence on Doshas. Ex. Sweet taste (madhura Rasa) will decrease the Vata and Pitta Guna (Properties): Certain properties like oiliness, heaviness will increase Kapha Veerya (Potency): It can be of Usha (hot) or Sheeta (Cold) rarely Anushna (either hot or cold) that will have an impact on Doshas Vipaka (Final transformation): It can be of Madhura (sweet), Amla (sour), and Katu (pungent- hot taste) Dosha: The overall outcome of any drug can be understood as VS (Vata shamaka- decreases Vata), PS (Pitta shamaka- decreases Pitta), KS(Kapha shamaka- decreases Kapha), TS (Tridosha shamaka- decreases Vata, Pitta and Kapha), VPS (Vata Pitta shamaka- decreases Vata and Pitta), VKS (Vata Kapha shamaka- decreases Vata and Kapha), PKS (Pitta Kapha shamaka- decreases Pitta and Kapha) So, in the given data set, Dosha is the target variable i.e we try to understand that which of the above components- Rasa, Guna, Veerya, Vipaka, will have an impact on Doshas