High Quality Image Generation Model - Comes Under NGC Models @prithivmlmods
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Updated
Jun 1, 2024 - Python
High Quality Image Generation Model - Comes Under NGC Models @prithivmlmods
PEFT is a wonderful tool that enables training a very large model in a low resource environment. Quantization and PEFT will enable widespread adoption of LLM.
Fine Tuning pegasus and flan-t5 pre-trained language model on dialogsum datasets for conversation summarization to to optimize context window in RAG-LLMs
Fine-tuning Llama3 8b to generate JSON formats for arithmetic questions and process the output to perform calculations.
[SIGIR'24] The official implementation code of MOELoRA.
Mistral and Mixtral (MoE) from scratch
memory-efficient fine-tuning; support 24G GPU memory fine-tuning 7B
This repo contains everything about transformers and NLP.
LLM projects
Code for NOLA, an implementation of "nola: Compressing LoRA using Linear Combination of Random Basis"
Official implementation of "DoRA: Weight-Decomposed Low-Rank Adaptation"
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language Model
Using Open-Source LLMs like FLAN-T5, built a Dialog Summarization model and did fine-tuning with DialogSum HF Dataset
A QLoRA+ LLM Ensemble with Schema-Linking for Text-to-SQL Generation
Fine-tune StarCoder2-3b for SQL tasks on limited resources with LORA. LORA reduces model size for faster training on smaller datasets. StarCoder2 is a family of code generation models (3B, 7B, and 15B), trained on 600+ programming languages from The Stack v2 and some natural language text such as Wikipedia, Arxiv, and GitHub issues.
This repository was commited under the action of executing important tasks on which modern Generative AI concepts are laid on. In particular, we focussed on three coding actions of Large Language Models. Extra and necessary details are given in the README.md file.
This repo contains implementations of fine-tuning LLaMA LLM model using LoRA weights (PEFT) as well as focuses on the Retrieval Augmented Generation (RAG) framework.
This project is an implementation of the paper: Parameter-Efficient Transfer Learning for NLP, Houlsby [Google], ICML 2019.
Official code implemtation of paper AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?
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