Update README.md

Added additional details and sample question

README.md

@@ -17,11 +17,18 @@ datasets:
 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
+## Notes & Methodology
 * [Excalibur-7b](https://huggingface.co/InferenceIllusionist/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
 
-*Requires [mistral-7b-mmproj-v1.5-Q4_1](https://huggingface.co/koboldcpp/mmproj/resolve/main/mistral-7b-mmproj-v1.5-Q4_1.gguf?download=true) file to be loaded in Kobold
+
+## Sample Question - Vision
+<img src="https://i.imgur.com/7aRWtzU.jpeg" width="425"/>
+
+<b>Requires additional [mistral-7b-mmproj-v1.5-Q4_1.gguf](https://huggingface.co/koboldcpp/mmproj/tree/main) file for vision functionality</b>
+
+Select up 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:
+<img src="https://i.imgur.com/u1vo8Rs.jpeg" width="550"/>