Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop.

Toolkit for fine-tuning, ablating and unit-testing open-source LLMs.

The official fork of THoR Chain-of-Thought framework, enhanced and adapted for Emotion Cause Analysis (ECAC-2024)

perform deduplication on FLAN v2 dataset & Finetune LLaMa3 using this dataset

Prompt-engineered RAGs for Open Domain Complex QA

Instruction fine tuning BART for Dialogue Summarization | IT4772E | NLP Project 20232

Fine-tuning of Flan-5T LLM for text classification

For this project, I fine-tuned two separate models for three tasks: document summarization, dialogue summarization and text classification

Empower your LLM to do more than you ever thought possible with these state-of-the-art prompt templates.

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 repository contains one of my cool project which I have created during my college's MINeD hack-a-thon.

The codebase for finetuning a flant5 model to include tag in a lyric.

This project uses LLMs to generate music from text by understanding prompts, creating lyrics, determining genre, and composing melodies. It harnesses LLM capabilities to create songs based on text inputs through a multi-step approach.

LLM projects

Using Open-Source LLMs like FLAN-T5, built a Dialog Summarization model and did fine-tuning with DialogSum HF Dataset

Demonstration of LLM techniques such as prompt engineering, full finetuning, PEFT (LoRA) etc.

MTP-FlanT5-SBERT-Model-for-NewsQA-and-Teacher-Student-Model

Performing Prompt engineering on a dialogue summarization task using Flan-T5 and the dialogsum dataset. Exploring how different prompts affect the output of the model, and compare zero-shot and few-shot inferences.

[Preprint] Learning to Filter Context for Retrieval-Augmented Generaton

This repository contains notebook files that discuss Large Language Models (LLMs), covering topics like fine-tuning, prompt engineering, and techniques such as PEFT (Parameter Efficient Fine-Tuning) and PPO (Proximal Policy Optimization) etc.