[SIGIR'24] The official implementation code of MOELoRA.
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Updated
Jul 22, 2024 - Python
[SIGIR'24] The official implementation code of MOELoRA.
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language Model
memory-efficient fine-tuning; support 24G GPU memory fine-tuning 7B
Official implementation of "DoRA: Weight-Decomposed Low-Rank Adaptation"
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.
Code for NOLA, an implementation of "nola: Compressing LoRA using Linear Combination of Random Basis"
High Quality Image Generation Model - Powered with NVIDIA A100
Official code implemtation of paper AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?
My lab work of “Generative AI with Large Language Models” course offered by DeepLearning.AI and Amazon Web Services on coursera.
Fine Tuning pegasus and flan-t5 pre-trained language model on dialogsum datasets for conversation summarization to to optimize context window in RAG-LLMs
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 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.
LLM projects
Dialogue Summary LLM - FLAN - T5: An implementation of the Flan-t5 LLM to summarize dialogues. Prompt Engineering , Fine tuning with PEFT and fine tuning with RL (PPO) is explored within this project.
Mistral and Mixtral (MoE) from scratch
A fine-tuned LLM great at answering questions about car repairs and maintenance.
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.
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