Official release of InternLM series (InternLM, InternLM2, InternLM2.5, InternLM3).

The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices

Code and documents of LongLoRA and LongAlpaca (ICLR 2024 Oral)

Practical course about Large Language Models.

[ACL2024 Findings] Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

FineTune LLMs in few lines of code (Text2Text, Text2Speech, Speech2Text)

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。一个集成数学知识和高等数学习题及其解答的大语言模型。

🚀 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 LoRA techniques to efficiently and accurately extract drug names and adverse side-effects from pharmaceutical texts

A data-centric AI package for ML/AI. Get the best high-quality data for the best results. Discord: https://discord.gg/t6ADqBKrdZ

Source code and educational content for the Polite Guard NLP model

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-tuning but are unfamiliar with Azure ML.

Fine-Tuning and Evaluating a Falcon 7B Model for generating HTML code from input prompts.

DICE: Detecting In-distribution Data Contamination with LLM's Internal State

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 classification tasks, these notebooks offer explanations and code for implementation.

Over 60 figures and diagrams of LLMs, quantization, low-rank adapters (LoRA), and chat templates FREE TO USE in your blog posts, slides, presentations, or papers.

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.