Updating model files

README.md

@@ -8,6 +8,17 @@ tags:
 - medical
 inference: false
 ---
+<div style="width: 100%;">
+    <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<div style="display: flex; justify-content: space-between; width: 100%;">
+    <div style="display: flex; flex-direction: column; align-items: flex-start;">
+        <p><a href="https://discord.gg/UBgz4VXf">Chat & support: my new Discord server</a></p>
+    </div>
+    <div style="display: flex; flex-direction: column; align-items: flex-end;">
+        <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? Patreon coming soon!</a></p>
+    </div>
+</div>
 
 # medalpaca-13B GPTQ 4bit
 
@@ -90,35 +101,46 @@ The above commands assume you have installed all dependencies for GPTQ-for-LLaMa
 
 If you can't update GPTQ-for-LLaMa to the latest Triton branch, or don't want to, you can use `medalpaca-13B-GPTQ-4bit-128g.no-act-order.safetensors` as mentioned above, which should work without any upgrades to text-generation-webui.
 
+## Want to support my work?
+
+I've had a lot of people ask if they can contribute. I love providing models and helping people, but it is starting to rack up pretty big cloud computing bills.
+
+So if you're able and willing to contribute, it'd be most gratefully received and will help me to keep providing models, and work on various AI projects.
+
+Donaters will get priority support on any and all AI/LLM/model questions, and I'll gladly quantise any model you'd like to try.
+
+* Patreon: coming soon! (just awaiting approval)
+* Ko-Fi: https://ko-fi.com/TheBlokeAI
+* Discord: https://discord.gg/UBgz4VXf
 # Original model card: MedAlpaca 13b
 
 
 ## Table of Contents
 
-[Model Description](#model-description)  
-- [Architecture](#architecture)    
-- [Training Data](#trainig-data)  
-[Model Usage](#model-usage)  
-[Limitations](#limitations)  
+[Model Description](#model-description)
+- [Architecture](#architecture)
+- [Training Data](#trainig-data)
+[Model Usage](#model-usage)
+[Limitations](#limitations)
 
 ## Model Description
 ### Architecture
-`medalpaca-13b` is a large language model specifically fine-tuned for medical domain tasks. 
-It is based on LLaMA (Large Language Model Meta AI) and contains 13 billion parameters. 
+`medalpaca-13b` is a large language model specifically fine-tuned for medical domain tasks.
+It is based on LLaMA (Large Language Model Meta AI) and contains 13 billion parameters.
 The primary goal of this model is to improve question-answering and medical dialogue tasks.
 
 ### Training Data
-The training data for this project was sourced from various resources. 
-Firstly, we used Anki flashcards to automatically generate questions, 
-from the front of the cards and anwers from the back of the card. 
-Secondly, we generated medical question-answer pairs from [Wikidoc](https://www.wikidoc.org/index.php/Main_Page). 
-We extracted paragraphs with relevant headings, and used Chat-GPT 3.5 
-to generate questions from the headings and using the corresponding paragraphs 
-as answers. This dataset is still under development and we believe 
-that approximately 70% of these question answer pairs are factual correct. 
-Thirdly, we used StackExchange to extract question-answer pairs, taking the 
-top-rated question from five categories: Academia, Bioinformatics, Biology, 
-Fitness, and Health. Additionally, we used a dataset from [ChatDoctor](https://arxiv.org/abs/2303.14070) 
+The training data for this project was sourced from various resources.
+Firstly, we used Anki flashcards to automatically generate questions,
+from the front of the cards and anwers from the back of the card.
+Secondly, we generated medical question-answer pairs from [Wikidoc](https://www.wikidoc.org/index.php/Main_Page).
+We extracted paragraphs with relevant headings, and used Chat-GPT 3.5
+to generate questions from the headings and using the corresponding paragraphs
+as answers. This dataset is still under development and we believe
+that approximately 70% of these question answer pairs are factual correct.
+Thirdly, we used StackExchange to extract question-answer pairs, taking the
+top-rated question from five categories: Academia, Bioinformatics, Biology,
+Fitness, and Health. Additionally, we used a dataset from [ChatDoctor](https://arxiv.org/abs/2303.14070)
 consisting of 200,000 question-answer pairs, available at https://github.com/Kent0n-Li/ChatDoctor.
 
 | Source                      | n items |
@@ -152,7 +174,7 @@ print(answer)
 
 ## Limitations
 The model may not perform effectively outside the scope of the medical domain.
-The training data primarily targets the knowledge level of medical students, 
+The training data primarily targets the knowledge level of medical students,
 which may result in limitations when addressing the needs of board-certified physicians.
-The model has not been tested in real-world applications, so its efficacy and accuracy are currently unknown. 
-It should never be used as a substitute for a doctor's opinion and must be treated as a research tool only.
+The model has not been tested in real-world applications, so its efficacy and accuracy are currently unknown.
+It should never be used as a substitute for a doctor's opinion and must be treated as a research tool only.