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*

Weighted (Importance Matrix) Quants available here

Static (Legacy) quants 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 Excelsior-7b base model
Ran for a little over an hour on a single A100
Fine-tuning succeeded in making model conversational and more well-rounded
Benchmark scores increased in the following categories versus base Excelsior-7b:
- ARC: 69.71 -> 70.9
- HellaSwag: 87.56 -> 87.93
- TruthfulQA: 67.24 -> 70.82
- Average: 73.6 -> 73.84
Precision: bfloat16

Sample Question - Vision

*Requires additional mmproj file. You have two options for vision functionality (available inside this repo):

Select the gguf file of your choice in Koboldcpp 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

Excalibur-7b-DPO

Notes & Methodology

Sample Question - Vision

Prompt Format

Open LLM Leaderboard Evaluation Results

InferenceIllusionist/
Excalibur-7b-DPO