Update README.md

README.md

@@ -24,6 +24,8 @@ This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://hugg
 
 You can explore the predictions of this model using this [Space](https://huggingface.co/spaces/davanstrien/iconclass-predictions).
 
+**Note:** this model is a work in progress with the goal to see how far small models can be created to excel at this kind of specific but challenging task. As a result the base model used may change over time. 
+
 ## Model Description
 
 This vision-language model has been fine-tuned to generate [Iconclass](https://iconclass.org/) classification codes from images. Iconclass is a comprehensive classification system for describing the content of images, particularly used in cultural heritage and art history contexts.
@@ -99,11 +101,10 @@ print(response)
 ### Training Dataset
 The model was trained on a reformatted version of the Brill Iconclass AI Test Set [biglam/brill_iconclass](https://huggingface.co/datasets/biglam/brill_iconclass). 
 
+The dataset was reformatted into a message format suitable for SFT training.
 
 
-The dataset was reformatted into a messages format suitable for SFT training.
-Training Procedure
-<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>
+### Training Procedure
 
 This model was trained with SFT (Supervised Fine-Tuning).
 
@@ -118,13 +119,13 @@ Tokenizers: 0.21.4
 
 ### Limitations and Biases
 
-The Iconclass classification system reflects biases from its creation period (1940s Netherlands)
-Certain categories, particularly those related to human classification, may contain outdated or problematic terminology
-Model performance may vary on images outside the Western art tradition due to dataset composition
+The Iconclass classification system reflects biases from its creation period (1940s Netherlands).
+Certain categories, particularly those related to human classification, may contain outdated or problematic terminology.
+Model performance may vary on images outside the Western art tradition due to the dataset composition.
 
 ### Citations
 
-Model and Training
+Training framework
 
 ```bibtex
 @misc{vonwerra2022trl,