metadata

base_model:
  - InferenceIllusionist/Excalibur-7b
library_name: transformers
tags:
  - finetune
  - dpo
  - chatml
license: apache-2.0
datasets:
  - Intel/orca_dpo_pairs

Excalibur-7b-DPO

An initial foray into the world of fine-tuning. The goal of this release was to amplify the quality of the original model's responses, in particular for vision use cases*

Notes & Methodology

Excalibur-7b fine-tuned with Direct Preference Optimization (DPO) using Intel/orca_dpo_pairs
This is a quick experiment to determine the impact of DPO finetuning on the original base model
Ran for a little over an hour on a single A100
Internal benchmarks showed improvement over base model, awaiting final results
Precision: bfloat16

Sample Question - Vision

Requires additional mistral-7b-mmproj-v1.5-Q4_1.gguf file for vision functionality

Select the gguf file of your choice in Kobold as usual, then make sure to choose the mmproj file above in the LLaVA mmproj field of the model submenu:

Prompt Format

For best results please use ChatML for the prompt format. Alpaca may also work.