Update README.md

Initial Model Card

README.md

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-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# gemma-2b-lahacks 💻
 
-# gemma-2b-lahacks
-
-This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on the None dataset.
+This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it).
 It achieves the following results on the evaluation set:
 - Loss: 2.3061
 
-## Model description
+## Model description 📝
+
+This model was fine-tuned during LAHacks 2024, the intention of this model is to be able to diagnose a patient appropratiely
+based on the information in their previous medical records, current symptoms, age, sex, and more.
+
+## Intended uses & limitations ⁉️
+
+Sample code snippet:
+```py
+
+```
 
-More information needed
+Uses: To use Artificial Intelligence technology to diagnose patient based off of multiple parameters ranging from their age to their 
+medical record. 
 
-## Intended uses & limitations
+Limitation: There's a highly likelyhood that the model will NOT be great at diagnosing it's users, the amount of time it took to fine-tune
+this model limited how much data we could train it on. With more time a more accurate model would be expected.
 
-More information needed
+## Training and evaluation data 📈
 
-## Training and evaluation data
+The model was trained on data from the research paper 'A New Dataset For Automatic Medical Diagnosis' by Arsène Fansi Tchango, Rishab Goel, 
+Zhi Wen, Julien Martel, Joumana Ghosn. The 'release_train_patients.csv' dataset was reduced from it's original 1.3 million rows of data to a
+mere 500-1000 rows of data. This was due to the time it took to fine-tune a model which depended on how big the dataset provided was.
 
-More information needed
+## Training procedure 🏋️
 
-## Training procedure
+The fine-tuning took MULTIPLE, and I mean MULTIPLE tries. Sometimes the dataset provided was very big so the kernel had to be restarted multiple times.
+Additionally, the model was tuned on the default data that Intel offers in their guide to fine-tune a gemma model.
 
-### Training hyperparameters
+### Training hyperparameters 🔍
 
 The following hyperparameters were used during training:
 - learning_rate: 1e-05