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 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.

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.

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

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

Conversation AI model for open domain dialogs

🦖 X—LLM: Cutting Edge & Easy LLM Finetuning

bitsandbytes modify for jetson orin

A web app that allows you to select a subject and then change its background, OR keep the background and change the subject.

Orca 2 on Colab

Fine tuned code llama on CM/codexglue_code2text_javascript open source dataset from hugging face having problem statements and corresponding javascript code.

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.

Experiments in quantisation consisting of quantisation from scratch, bitsandbytes, and llama.cpp. [Assignment 4 of Advanced Natural Language Processing, IIIT-H Monsoon '24]

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 🎋

An AI-powered system for extracting cancer-related information from patient Electronic Health Record (EHR) notes

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

🦙 Llama2-FineTuning: Fine-tune LLAMA 2 with Custom Datasets Using LoRA and QLoRA Techniques

Jupyter Notebook for LLM compression via quantization (INT8, INT4, FP16) and evaluation using metrics such as ROUGE and BLEU. Facilitates efficient LLM optimization.

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