#大语言模型#Official release of InternLM series (InternLM, InternLM2, InternLM2.5, InternLM3).
#大语言模型#The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices
#大语言模型#Code and documents of LongLoRA and LongAlpaca (ICLR 2024 Oral)
Practical course about Large Language Models.
#大语言模型#[ACL2024 Findings] Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models
#大语言模型#FineTune LLMs in few lines of code (Text2Text, Text2Speech, Speech2Text)
#大语言模型#AM (Advanced Mathematics) Chat is a large language model that integrates advanced mathematical knowledge, exercises in higher mathematics, and their solutions. AM (Advanced Mathematics) chat 高等数学大模型。一...
#大语言模型#🚀 Easy, open-source LLM finetuning with one-line commands, seamless cloud integration, and popular optimization frameworks. ✨
Fine-tuning Open-Source LLMs for Adaptive Machine Translation
Exploring the potential of fine-tuning Large Language Models (LLMs) like Llama2 and StableLM for medical entity extraction. This project focuses on adapting these models using PEFT, Adapter V2, and Lo...
A data-centric AI package for ML/AI. Get the best high-quality data for the best results. Discord: https://discord.gg/t6ADqBKrdZ
This hands-on walks you through fine-tuning an open source LLM on Azure and serving the fine-tuned model on Azure. It is intended for Data Scientists and ML engineers who have experience with fine-tun...
#计算机科学#Fine-Tuning and Evaluating a Falcon 7B Model for generating HTML code from input prompts.
#大语言模型#DICE: Detecting In-distribution Data Contamination with LLM's Internal State
#自然语言处理#Building a GPT-3 powered Amazon Support Bot for precise customer query responses via fine-tuned model on Amazon QA data
Pre-Training and Fine-Tuning transformer models using PyTorch and the Hugging Face Transformers library. Whether you're delving into pre-training with custom datasets or fine-tuning for specific class...
This repository implements a self-updating RAG (Retrograde Autoregressive Generation) model. It leverages Wikipedia for factual grounding and can fine-tune itself when information is unavailable. This...
EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition
Chatbot built using Flask and the OpenAI GPT-3.5 turbo model. The chatbot allows users to interact with a language model powered by GPT-3.5 turbo and get responses based on their input.
Projects Implemented for the Udacity Generative AI Engineer Nanodegree Program