Update README.md (#2)

- Update README.md (655c2c526292f6ff36e006fdaed5cc5df4a30567)


Co-authored-by: Ariel Lee <[email protected]>

README.md

@@ -3,34 +3,26 @@ language:
 - en
 tags:
 - llama
-license: apache-2.0
+license: other
 metrics:
 - MMLU
 - ARC 
 - HellaSwag
 - TruthfulQA
-- ReClor
 ---
 
-# 🥳 Platypus30B has arrived! 
+# 🥳 Platypus-30B has arrived! 
+
+Platypus-30B is an instruction fine-tuned model based on the LLaMA-30b transformer architecture.
 
 | Metric                | Value |
 |-----------------------|-------|
-| MMLU (5-shot)         | 64.2  |
-| ARC (25-shot)         | 76.7  |
+| MMLU (5-shot)         | 65.4  |
+| ARC (25-shot)         | 64.6  |
 | HellaSwag (10-shot)   | 84.3  |
-| TruthfulQA (0-shot)   | 37.4  |
-| ReClor (0-shot)       | 70    |
-
-## Model Description
-
-Platypus30B is an instruction fine-tuned LlaMa model.
-
-## Apply Delta Weights
-
-```sh
-ADD
-```
+| TruthfulQA (0-shot)   | 45.8  |
+|-----------------------|-------|
+| Avg.                  | 65    | 💥
 
 ## Usage
 
@@ -41,7 +33,7 @@ ADD
 ## Model Details
 
 * **Trained by**: [Ariel Lee & Cole Hunter, LINK TO WEBSITES]
-* **Model type:**  **Platypus30B** is an auto-regressive language model based on the LLaMA transformer architecture.
+* **Model type:**  **Platypus-30B** is an auto-regressive language model based on the LLaMA transformer architecture.
 * **Language(s)**: English
 * **License for base weights**: License for the base LLaMA model's weights is Meta's [non-commercial bespoke license](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md).
 
@@ -52,15 +44,13 @@ ADD
 | \\(n_\text{layers}\\)     | 60    |
 | \\(n_\text{heads}\\)      | 52    |
 
-## Training
-
-### Training Dataset
+## Training Dataset
 
 Dataset of highly filtered and curated question and answer pairs. Release TBD.
 
-### Training Procedure
+## Training Procedure
 
-`lilloukas/Platypus30b` was instruction fine-tuned using lora [CITE REPO] on 2 A100 80GB with the following configuration:
+`lilloukas/Platypus-30b` was instruction fine-tuned using lora [CITE REPO] on 4 A100 80GB with the following configuration:
 
 | Hyperparameter      | Value |
 |---------------------|-------|