Vishesh vishesh9131
-
NVIDIA
- India
-
14:43
(UTC -12:00) - https://vishesh9131.github.io/git-page-intro/
- https://orcid.org/0009-0008-9059-0759
- https://corerec.tech
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Building a 2.3M-parameter LLM from scratch with LLaMA 1 architecture.
Llama from scratch, or How to implement a paper without crying
Qwen2.5 is the large language model series developed by Qwen team, Alibaba Cloud.
YuE: Open Full-song Music Generation Foundation Model, something similar to Suno.ai but open
This repository contains LLM (Large language model) interview question asked in top companies like Google, Nvidia , Meta , Microsoft & fortune 500 companies.
MiniCPM-o 2.6: A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone
利用AI大模型,一键生成高清短视频 Generate short videos with one click using AI LLM.
A simple probabilistic programming language.
Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous …
Dead simple FLUX LoRA training UI with LOW VRAM support
A unified, comprehensive and efficient recommendation library
stackblitz-labs / bolt.diy
Forked from stackblitz/bolt.newPrompt, run, edit, and deploy full-stack web applications using any LLM you want!
System design patterns for machine learning
[SIGIR'2023] "GFormer: Graph Transformer for Recommendation"
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
Python tool for converting files and office documents to Markdown.
This is a repository of public data sources for Recommender Systems (RS).
Classic papers and resources on recommendation
Fullstack app framework for web, desktop, mobile, and more.
Transformer related optimization, including BERT, GPT
QGIS is a free, open source, cross platform (lin/win/mac) geographical information system (GIS)
Reference implementation of the Transformer architecture optimized for Apple Neural Engine (ANE)
Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Ranking (CTR/CVR prediction), Post Ranking, Large Model (Generative Recommen…
A collection of libraries for recommender systems