Update README.md

README.md

@@ -1,15 +1,15 @@
-# Doctor GPT
+# Doctor Dignity
 <p align="center">
 
 update sep 6 - i finally compiled a custom model to ios that runs inference way more reliably, it's called rwkv and its not even a transformer, its a recurrent neural network (OMG). don't worry it's an RNN with transformer level performance. im working on android now . stay tuned !
 
-DISCLAIMER - DO NOT TAKE ANY MEDICAL ADVICE FROM DOCTORGPT SERIOUSLY!!!!! This is a work in progress and taking any advice seriously could result in serious injury or even death. 
+DISCLAIMER - DO NOT TAKE ANY MEDICAL ADVICE FROM DOCTOR Dignity SERIOUSLY!!!!! This is a work in progress and taking any advice seriously could result in serious injury or even death. 
 
 <img src="https://i.imgur.com/18jVWiV.png" width="400" height="400">
 </p>
 
 ## Overview
-DoctorGPT is a Large Language Model that can pass the US Medical Licensing Exam. This is an open-source project with a mission to provide everyone their own private doctor. DoctorGPT is a version of Meta's [Llama2](https://ai.meta.com/llama/) 7 billion parameter Large Language Model that was fine-tuned on a Medical Dialogue Dataset, then further improved using Reinforcement Learning & Constitutional AI. Since the model is only 3 Gigabytes in size, it fits on any local device, so there is no need to pay an API to use it. It's free, made for offline usage which preserves patient confidentiality, and it's available on iOS, Android, and Web. Pull requests for feature additions and improvements are encouraged.
+Doctor Dignity is a Large Language Model that can pass the US Medical Licensing Exam. This is an open-source project with a mission to provide everyone their own private doctor. Doctor Dignity is a version of Meta's [Llama2](https://ai.meta.com/llama/) 7 billion parameter Large Language Model that was fine-tuned on a Medical Dialogue Dataset, then further improved using Reinforcement Learning & Constitutional AI. Since the model is only 3 Gigabytes in size, it fits on any local device, so there is no need to pay an API to use it. It's free, made for offline usage which preserves patient confidentiality, and it's available on iOS, Android, and Web. Pull requests for feature additions and improvements are encouraged.
 
 ## Dependencies
 - [Numpy](https://numpy.org/install/)           (Use matrix math operations)
@@ -35,17 +35,17 @@ pip install numpy torch datasets huggingface_hub transformers trl bitsandbytes s
 
 ## Training
 
-In order to train the model, you can run the training.ipynb notebook locally or remotely via a cloud service like Google Colab Pro. The training process requires a GPU, and if you don't have one then the most accessible option i found was using Google Colab [Pro](https://colab.research.google.com/signup) which costs $10/month. The total training time for DoctorGPT including supervised fine-tuning of the initial LLama model on custom medical data, as well as further improving it via Reinforcement Learning from Constitional AI Feedback took 24 hours on a paid instance of Google Colab. If you're interested in learning more about how this process works, details are in the training.ipynb notebook. 
+In order to train the model, you can run the training.ipynb notebook locally or remotely via a cloud service like Google Colab Pro. The training process requires a GPU, and if you don't have one then the most accessible option i found was using Google Colab [Pro](https://colab.research.google.com/signup) which costs $10/month. The total training time for Doctor Dignity including supervised fine-tuning of the initial LLama model on custom medical data, as well as further improving it via Reinforcement Learning from Constitional AI Feedback took 24 hours on a paid instance of Google Colab. If you're interested in learning more about how this process works, details are in the training.ipynb notebook. 
 
 #### Cloud Training
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/llSourcell/DoctorGPT/blob/main/llama2.ipynb)
-click here: https://colab.research.google.com/github/llSourcell/DoctorGPT/blob/main/llama2.ipynb
+click here: https://colab.research.google.com/github/llSourcell/Doctor-Dignity/blob/main/llama2.ipynb
 
 #### Local Training
 
 ```bash
-git clone https://github.com/llSourcell/DoctorGPT.git
+git clone https://github.com/llSourcell/Doctor-Dignity.git
 jupyter training.ipynb
 ```
 Get jupyter [here](https://jupyter.org/install)
@@ -59,15 +59,15 @@ There are 2 huggingface repos, one which is quantized for mobile and one that is
 - Step 1: [Download](https://github.com/mlc-ai/mlc-llm/tree/main/ios) the iOS Machine Learning Compilation Chat Repository
 - Step 2: Follow the [installation steps](https://mlc.ai/mlc-llm/docs/deploy/ios.html) 
 - Step 3: Once the app is running on your iOS device or simulator, tap "add model variant"
-- Step 4: Enter the URL for the latest DoctorGPT model to download it: [https://huggingface.co/llSourcell/doctorGPT_mini] (https://huggingface.co/llSourcell/doctorGPT_mini)
+- Step 4: Enter the URL for the latest Doctor Dignity model to download it: [https://huggingface.co/llSourcell/doctorGPT_mini] (https://huggingface.co/llSourcell/doctorGPT_mini)
 - Step 5: Tap 'Add Model' and start chatting locally, inference runs on device. No internet connection needed!
 
 #### Android
 
 - Step 1: [Download](https://github.com/mlc-ai/mlc-llm/tree/main/android) the Android Machine Learning Compilation Chat Repository
 - Step 2: Follow the [installation steps]([https://mlc.ai/mlc-llm/docs/deploy/ios.html](https://mlc.ai/mlc-llm/docs/deploy/android.html)) 
 - Step 3: Tap "add model variant"
-- Step 4: Enter the URL for the latest DoctorGPT model to download it: [https://huggingface.co/llSourcell/doctorGPT_mini](https://huggingface.co/llSourcell/doctorGPT_mini)
+- Step 4: Enter the URL for the latest Doctor Dignity model to download it: [https://huggingface.co/llSourcell/doctorGPT_mini](https://huggingface.co/llSourcell/doctorGPT_mini)
 - Step 5: Tap 'Add Model' and start chatting locally! No internet needed. 
 
 #### Web (TODO)