Popular GitHub Repositories for Bitsandbytes

Official repository of my book "A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face"

? X—LLM: Cutting Edge & Easy LLM Finetuning

Faster Pytorch bitsandbytes 4bit fp4 nn.Linear ops

??? Instruction Fine-Tuning of Meta Llama 3.2-3B Instruct on Kannada Conversations ??? Tailoring the model to follow specific instructions in Kannada, enhancing its ability to generate relevant, context-aware responses based on conversational inputs. ?? Using the Kannada Instruct dataset for fine-tuning! Happy Finetuning??

A simple custom QLoRA implementation for fine-tuning a language model (LLM) with basic tools such as PyTorch and Bitsandbytes, completely decoupled from Hugging Face.

Open source RAG with Llama Index for Japanese LLM in low resource settting

Orca 2 on Colab

LLM-Lora-PEFT_accumulate explores optimizations for Large Language Models (LLMs) using PEFT, LORA, and QLORA. Contribute experiments and implementations to enhance LLM efficiency. Join discussions and push the boundaries of LLM optimization. Let's make LLMs more efficient together!

In this project, I have provided code and a Colaboratory notebook that facilitates the fine-tuning process of an Alpaca 3B parameter model originally developed at Stanford University. The model was adapted using LoRA to run with fewer computational resources and training parameters and used HuggingFace's PEFT library.

Conversation AI model for open domain dialogs

In this project, I have provided code and a Colaboratory notebook that facilitates the fine-tuning process of an Alpaca 350M parameter model originally developed at Stanford University. The model was adapted using LoRA to run with fewer computational resources and training parameters and used HuggingFace's PEFT library.

This model is a fine-tuned model based on the "TinyPixel/Llama-2-7B-bf16-sharded" model and "timdettmers/openassistant-guanaco" dataset

Fine-tune the Segment Anything Model (SAM) using Huggingface's PEFT and Trainer with techniques like QLoRA, DoRA, and more. This project supports efficient training with bitsandbytes and SDPA for faster training and inference. Compatible with COCO dataset format.

一个用于在英特尔平台上加速 bitsandbytes 性能的扩展程序。

Budget Buddy is a finance chatbot built using Chainlit and the LLaMA language model. It analyzes PDF documents, such as bank statements and budget reports, to provide personalized financial advice and insights. The chatbot is integrated with Hugging Face for model management, offering an interactive way to manage personal finances.

provide specialized immigration assistance in the field of immigration law using large language model

Effortlessly quantize, benchmark, and publish Hugging Face models with cross-platform support for CPU/GPU. Reduce model size by 75% while maintaining performance.

Running small but capable language models entirely offline

This project implements a classic Retrieval-Augmented Generation (RAG) system using HuggingFace models with quantization techniques. The system processes PDF documents, extracts their content, and enables interactive question-answering through a Streamlit web application.

This Chatbot is used to answer all types of medical queries and it's locally trained using transformers so it can run in offline mode