🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

Explainable Hate Speech Detection using NLP Model Reasoning

Mindful Monk AI Represents a Revolutionary Approach to AI that emphasizes mindfulness, presence and compassion, assisting you with a diverse range of tasks, utilizing a step-by-step-by-step approach. MindfulMonk AI is designed to align seamlessly with your objectives

Study Uses LLMs to craft accurate, engaging headlines from Reddit Posts

4-Bit Finetuning of Large Language Models on One Consumer GPU

Tutorials on how to use language models

llm training rust

Simple UI for LLM Model Finetuning

An unnecessarily tiny implementation of GPT-2 in NumPy.

H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://h2oai.github.io/h2o-llmstudio/

This repo contains the code of concepts and projects i learnt from the course "LangChain & Vector Databases in Production", “Training and Fine-tuning LLMs for Production” and "Retrieval Augmented Generation for Production with LangChain & LlamaIndex"

This repo is dedicated to providing open-source tutorials for Large Language Model experimentation.

This chat bot is fine tuned on GPT-2 model to generate responses like Shakespear.

LLM for assisting with form inputs

LLMS can mutually compete with and enlighten each other to form a stronger group,Multi agents can be SFTed by other agents' positive samples and guided by self experiences with rl. This shows how human evolved

This project utilizes a machine learning model where consumer brand data is employed. Initially, a preliminary model is developed, followed by a refined model using a process called 'fine-tuning' to improve results. Additionally, a comprehensive testing suite has been created to validate accuracy and reliability of the model's predictions.

Pretrain, finetune, deploy 20+ LLMs on your own data. Uses state-of-the-art techniques: flash attention, FSDP, 4-bit, LoRA, and more.

This project leverages LLaMA-2, a powerful and versatile language model, as the foundation to create a fine-tuned Large Language Model (LLM) for a medical AI chatbot using a dataset of 5,000 medical questions and answers involves several steps, each crucial for ensuring the model's accuracy, relevance, and safety.