Update README.md

README.md

@@ -14,7 +14,7 @@ library_name: transformers
 
 # Model Card for Zamba2-1.2B-instruct-Dutch
 
-Zamba2-1.2B-instruct-Dutch is a Dutch language instruction-following model obtained through a two-stage fine-tuning process:
+Zamba2-1.2B-instruct-Dutch is a basic Dutch language instruction-following model obtained through a two-stage fine-tuning process:
 
 1. First stage (Base instruction model by Zyphra):
    - Zyphra fine-tuned Zamba2-1.2B to create Zamba2-1.2B-instruct through:
@@ -22,9 +22,9 @@ Zamba2-1.2B-instruct-Dutch is a Dutch language instruction-following model obtai
      - DPO training on [ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs), and [OpenHermesPreferences](https://huggingface.co/datasets/argilla/OpenHermesPreferences)
 
 2. Second stage (Dutch language adaptation):
-   - Further fine-tuning of Zyphra's Zamba2-1.2B-instruct on the [dolly-15k-dutch](https://huggingface.co/datasets/BramVanroy/dolly-15k-dutch) dataset, specifically using the training split
+   - Further fine-tuning of Zyphra's Zamba2-1.2B-instruct on the [dolly-15k-dutch](https://huggingface.co/datasets/BramVanroy/dolly-15k-dutch) dataset, specifically using the training split. While this dataset is not state-of-the-art, it provides a solid foundation for demonstrating Dutch language capabilities and fits within the 1024 token context window. The relatively small dataset size allows for quick experimentation and validation of the model's Dutch language adaptation capabilities.
 
-The model maintains the core hybrid architecture of Zamba2 while being optimized for Dutch language understanding and generation.
+The model maintains the core hybrid architecture of Zamba2 while being optimized for Dutch language understanding and generation. By building upon Zyphra's instruction-tuned model, it inherits strong general instruction-following capabilities while adding Dutch language proficiency.
 
 ## Quick start
 
@@ -68,7 +68,7 @@ The model was fine-tuned using the following approach:
 
 ### Fine-tuning Configuration
 
-The model includes an advanced learning rate optimization system for fine-tuning, implemented through the custom `LROptimizerCallback` class which can be found in _lr_optimizer.py_:
+The model includes an advanced learning rate optimization system for fine-tuning, implemented through the `LROptimizerCallback` class:
 
 ```python
 from transformers import AutoTokenizer, Trainer