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*

FP16 available here

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 mmproj file. You have two options for vision functionality (available inside original repo or linked below):

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.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.84
AI2 Reasoning Challenge (25-Shot)	70.90
HellaSwag (10-Shot)	87.93
MMLU (5-Shot)	65.46
TruthfulQA (0-shot)	70.82
Winogrande (5-shot)	82.48
GSM8k (5-shot)	65.43

InferenceIllusionist/Excalibur-7b-DPO-GGUF

Excalibur-7b-DPO-GGUF

Notes & Methodology

Sample Question - Vision

Prompt Format

Open LLM Leaderboard Evaluation Results

InferenceIllusionist/
Excalibur-7b-DPO-GGUF