updated README

README.md

@@ -1,14 +1,3 @@
----
-title: Iris
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.0.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
 This project was created as part of a course on Scalable ML ID2223 @ KTH
 
 Purpose 
@@ -20,16 +9,29 @@ personalized investment opportunities.
 
 The base models we used for fine-tuning:
 --- 
+Base models were retrieved from huggingface.co
+
 "unsloth/Llama-3.2-1B-Instruct-bnb-4bit"
 
 "unsloth/Llama-3.2-1B-Instruct"
 
+"unsloth/Phi-3.5-mini-instruct"
+
 Datasets
 ---
+Datasets were retrieved from huggingface.co
+
 "mlabonne/FineTome-100k"
 
 "gbharti/finance-alpaca"
 
+Interface
+---
+The interface was designed to allow the user get stock recommendations
+with other ways of interaction that purely chatting.
+
+![img_1.png](interface_demo.png)
+
 
 Improvement - model centric approach:
 ---
@@ -37,7 +39,53 @@ To improve on the performance of the model a full fine-tuning of the model could
 
 Improvement - data centric approach:
 ---
-To improve on the initial model a dataset which is more suited for our objective as a financial adivisor could boost the models performance
+To improve on the initial model (Version1) a dataset which is more suited for our objective as a financial adivisor could boost the models performance
 by receiving more domain-specific knowledge during the fine-tuning.
 We found a dataset to that end with: https://huggingface.co/datasets/gbharti/finance-alpaca which we have used to train the second iteration of
 our financial advisor.
+
+Model iteration 2 - results
+---
+
+In the second iteration we used as mentioned above a finance dataset
+and another base model: "unsloth/Phi-3.5-mini-instruct" which is a model with more parameters. Our hypothesis
+was that these two changes would increase the performance and helpfulness of the model.
+
+<h3>First impressions</h3>
+It was quickly realized that this model was requiring a lot more computional power
+and had issues running inference on huggingface's free CPU. In contrast the 
+previous model had been usable under the same circumstances.
+
+<h3> Test example</h3>
+---
+<img src="example_input.png" alt="img.png" width="450" height="300">
+
+This was the input parameters for our example (see img above): 5 swedish stocks in the information tech sector with a 3/5 risk level. No additional comments.
+
+The result were as follows:
+
+**Version 1 (unsloth/Llama-3.2-1B-Instruct-bnb-4bit, mlabonne/FineTome-100k)**
+>Yes, here are 5 stocks from Sweden in the Information Technology sector:<br>
+>1. Ericsson (SE:ISOF) - A leading provider of telecommunications equipment and services.<br>
+>2. Spotify (SE:SPOT) - A music streaming service provider.<br>
+>3. Spotify's parent company, Intercom (SE:ICOM) - A leading provider of customer relationship management (CRM) software.<br>
+>4. Spotify's parent company, Spotify for Business (SE:SBFB) - A leading provider of music streaming services for businesses.<br>
+>5. Spotify's parent company, Spotify for Developers (SE:SFDB) - A leading provider of tools and resources for developers.<br>
+>
+>These stocks are all listed on the Stockholm Stock Exchange (Bolsas y Mercados de Comercio de Suiza, BZX) and are considered to be stable and growth-oriented. They are also diversified across various industries and sectors, including telecommunications, music streaming, customer relationship management, music streaming services for businesses, and tools and resources for developers.
+
+**Version 2 (unsloth/Phi-3.5-mini-instruct, gbharti/finance-alpaca)**
+>Sure, here are 5 stocks from Sweden in the Information Technology sector:
+>1. Ericsson 
+>2. Nokia 
+>3. Hexagon AB 
+>4. ABB 
+>5. AstraZeneca<br>
+>
+>These stocks are all listed on the Stockholm Stock Exchange and are considered to be relatively low risk investments.<br><br>I hope this helps!
+
+
+In summary the second iteration of the model responds in more correct manner,
+does less duplication and the output is more straight to the point.
+
+We consider this as substantial performance increase, although it is more computational intensive to run and thus takes longer.