This repository contains the code and data used to fine-tune the Llama2-Chat model using the 4-bit quantisation QLoRA (Quantization with Low Rank Approximation) PEFT technique on the OpenOrca dataset.
The OpenOrca-Clean dataset is a refined version derived from the original OpenOrca dataset.
The Llama2-OpenOrca-Clean dataset is tailored specifically for fine-tuning the Llama2-Chat model. It is derived from the OpenOrca-Clean dataset, further adapted to fit the llama prompt template. The dataset comprises a single column labeled "text," structured in the given format-
- Base Model: Llama-2-7B-Chat-hf
- Fine-tuning Technique: 4-bit quantization using QLoRA PEFT
- Dataset Used: Llama2-OpenOrca-Clean
The fine-tuning process involves training the Llama2-Chat model with 4-bit quantization using the QLoRA technique. This technique allows for efficient representation of model parameters while minimizing computational overhead.
Llama-2-7B-Chat-OpenOrca
Our latest model, fine-tuned with 1000 examples using 4-bit quantization QLoRA from Llama2-OpenOrca-Clean dataset, is now available.