Update README.md

README.md

@@ -1,29 +1,41 @@
-# AIPI 590 Large Language Models
-## Project 1 - Fine Tuning LLM
-
-### Files:
-- model.ipynb 
-  - notebook containing the code for fine tuning the Llama 3 model using QLoRa
-- data/train.json 
-  - json file containing the training set provided in the FINQA paper  
-- data/test.json 
-  - json file containing the validation set provided in the FINQA paper  
-
-### Process:
-The focal property of interest is analysis financial documents for numerical reasoning. Specifically numerical reasoning over quarterly financial filings with the SEC. The Llama-3-8B model was chosen to fine tune using the QLoRa approach. This approach was chosen due to the paper's findings of a performance increase while utilizing minimal memory and hardware. The aggressive quantization seemed to significantly decreased training time while offering increased performance on financial analysis. 
-
-### Evaluation:
-#### Rogue Score
-| ROUGE Score | Base Model | QLoRa Fine Tuned Model |
-| ------------- | ------------- | ------------- |
-| ROUGE-1	| 0.05104785 | 0.25257307 |
-| ROUGE-2	| 0.01158752 | 0.10479990 |
-| ROUGE-L	| 0.05104785 | 0.25175429 |
-
-### Collaborators:
-- Keese Phillips
-
-### Attribution:
-- [FINQA: A Dataset of Numerical Reasoning over Financial Data](https://arxiv.org/pdf/2109.00122v3)  
-- [LORA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/pdf/2106.09685)  
+---
+license: mit
+language:
+- en
+metrics:
+- rouge
+base_model:
+- meta-llama/Meta-Llama-3-8B
+pipeline_tag: text-generation
+tags:
+- finance
+---
+# AIPI 590 Large Language Models
+## Project 1 - Fine Tuning LLM
+
+### Files:
+- model.ipynb 
+  - notebook containing the code for fine tuning the Llama 3 model using QLoRa
+- data/train.json 
+  - json file containing the training set provided in the FINQA paper  
+- data/test.json 
+  - json file containing the validation set provided in the FINQA paper  
+
+### Process:
+The focal property of interest is analysis financial documents for numerical reasoning. Specifically numerical reasoning over quarterly financial filings with the SEC. The Llama-3-8B model was chosen to fine tune using the QLoRa approach. This approach was chosen due to the paper's findings of a performance increase while utilizing minimal memory and hardware. The aggressive quantization seemed to significantly decreased training time while offering increased performance on financial analysis. 
+
+### Evaluation:
+#### Rouge Score
+| ROUGE Score | Base Model | QLoRa Fine Tuned Model |
+| ------------- | ------------- | ------------- |
+| ROUGE-1	| 0.05104785 | 0.25257307 |
+| ROUGE-2	| 0.01158752 | 0.10479990 |
+| ROUGE-L	| 0.05104785 | 0.25175429 |
+
+### Collaborators:
+- Keese Phillips
+
+### Attribution:
+- [FINQA: A Dataset of Numerical Reasoning over Financial Data](https://arxiv.org/pdf/2109.00122v3)  
+- [LORA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/pdf/2106.09685)  
 - [QLORA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/pdf/2305.14314)