Update README.md

README.md

@@ -87,9 +87,15 @@ inference:
 
 # bart-base-instructiongen
 
-Instead of generating questions from text, generate instructions for LLMs! Demo on Spaces [is here](https://huggingface.co/spaces/pszemraj/generate-instructions).
+Instead of generating questions from text, generate instructions for LLMs! 
+
+Check out a [basic demo on Spaces](https://huggingface.co/spaces/pszemraj/generate-instructions). You can find other models fine-tuned for instruction generation by [searching for the instructiongen tag](https://huggingface.co/models?other=instructiongen).
+
+
+## About
+
+This model is a fine-tuned version of the [facebook/bart-base](https://huggingface.co/facebook/bart-base) model, fine-tuned using the `pszemraj/fleece2instructions` dataset. The concept is to apply text-to-text models to unlabeled, domain-specific text to generate appropriate LLM instructions. Consequently, this facilitates domain adaptation of instruction-tuned LLMs, making them more versatile in their respective domains.
 
-This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the pszemraj/fleece2instructions dataset.
 It achieves the following results on the evaluation set:
 - Loss: 1.0034
 - Rouge1: 61.7209
@@ -100,7 +106,7 @@ It achieves the following results on the evaluation set:
 
 ## Intended uses & limitations
 
-This is just a base model/example. There is likely to be even better performance with larger models.
+This is just a base model/example. There is likely to be even better performance with larger models (click [here to see other checkpoints](https://huggingface.co/models?other=instructiongen))
 
 Additionally, this was trained on a dataset of **only** instructions+outputs, with the `inputs` filtered out. This means that text of *1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo* will **not** get you *"Rank the following ice cream flavors: oreo, mint chip, chocolate chip, cookies and cream"*.