Update README.md

README.md

@@ -16,15 +16,14 @@ license: apache-2.0
 
 ### What?
 
-This Gradio app is a simple interface to access [unsloth AI's](https://github.com/unslothai) fine-tuning methods but leveraging the A100 GPUs provided by [Huggingface Spaces](https://huggingface.co/docs/hub/en/spaces-overview). This outputs of this fine-tuning will be instruction tuned LoRA weights that will be uploaded into your personal huggingface models repository.
+This Gradio app is a simple interface to access [unsloth AI's](https://github.com/unslothai) fine-tuning methods hosted on [Huggingface Spaces](https://huggingface.co/docs/hub/en/spaces-overview). This outputs of this fine-tuning will be instruction tuned LoRA weights that will be uploaded into your personal huggingface models repository.
 
 ### Why?
 
 The goal of this demo is to show how you can tune your own language models leveraging industry standard compute and fine tuning methods using a simple point-and-click UI.
 
-In addition, compute, even on Google Colab's free tier is tight even with a T4 and rate limits are uncertain. This makes the use of the A100s on this demo useful for a small added boost to compute performance. For those looking to reduce the costs associated with training datasets can pull down the spaces repository to train their models at speed for $9 on The Huggingface Pro License.
+This is a demo and not a production application and is hosted here simply to . This application is subject a demand queue.
 
-This is a demo and not a production application. This application is subject a demand queue.
 
 ### How?
 
@@ -35,6 +34,8 @@ Just start by following the guide below:
 3) Upload data: Either from transformers or your local jsonl file. Please view [this guide](https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset) for best practices.
 4) Fine-tune Model: Eat a snack and wait as you train the model for your use case.
 
+For GPU runtimes longer than a minute, remove the imports to huggingface spaces and decorators and run on your local GPU or migrate this work to a workspace like [lightning AI](https://lightning.ai/).
+
 ### Coming soon!
 
 - More models and added flexibility with guardrails on hyperparameter tuning.