fine-tuning-llm

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。

Enhancing LLMs with LoRA

Your SDK solves all of this. One interface. Unified logic. Local + hosted models. Fine-tuning. Agent tools. Enterprise-ready. Governance, RAG.Star It 🌟

🚀 Easy, open-source LLM finetuning with one-line commands, seamless cloud integration, and popular optimization frameworks. ✨

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

Fine-tuning Open-Source LLMs for Adaptive Machine Translation

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 for Intel's Polite Guard NLP project

Official Pytorch Implementation of [GRL] SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model

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.

md2LLM enables fine-tuning of open-source language models using personal Markdown files. It includes an observability and management layer for the fine-tuning process, allowing users to generate training data, manage models, and track and store each state of the fine-tuned 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