Update README.md

README.md

@@ -6,15 +6,19 @@ datasets:
 license: cc-by-nc-4.0
 ---
 
+<p><h1>🐋 The First OrcaPlatypus! 🐋</h1></p>
+
+![Platty](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B/resolve/main/Images/OrcaPlatypusMerge.jpg)
+
+
 # OpenOrca-Platypus2-13B
 
 OpenOrca-Platypus2-13B is a merge of [`garage-bAInd/Platypus2-13B`](https://huggingface.co/garage-bAInd/Platypus2-13B) and [`Open-Orca/OpenOrcaxOpenChat-Preview2-13B`](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B).
 
 This model is more than the sum of its parts! We are happy to be teaming up with the Platypus team to bring you a new model which once again tops the leaderboards!
 
-![Platty](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B/resolve/main/Images/OrcaPlatypusMerge.jpg)
 
-### Benchmark Metrics
+# Benchmark Metrics
 
 | Metric                | Value |
 |-----------------------|-------|
@@ -24,9 +28,10 @@ This model is more than the sum of its parts! We are happy to be teaming up with
 | TruthfulQA (0-shot)   | 52.69 |
 | Avg.                  | 64.56 |
 
-We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results.
+We use [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results.
+
 
-### Model Details
+# Model Details
 
 * **Trained by**: **Platypus2-13B** trained by Cole Hunter & Ariel Lee; **OpenOrcaxOpenChat-Preview2-13B** trained by Open-Orca
 * **Model type:**  **OpenOrca-Platypus2-13B** is an auto-regressive language model based on the LLaMA 2 transformer architecture.
@@ -34,7 +39,8 @@ We use state-of-the-art [Language Model Evaluation Harness](https://github.com/E
 * **License for Platypus2-13B base weights**: Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/))
 * **License for OpenOrcaxOpenChat-Preview2-13B base weights**: LLaMa-2 commercial
 
-### Prompt Template for base Platypus2-13B
+
+# Prompt Template for base Platypus2-13B
 ```
 ### Instruction:
 
@@ -42,10 +48,14 @@ We use state-of-the-art [Language Model Evaluation Harness](https://github.com/E
 
 ### Response:
 ```
-### Prompt Template for base OpenOrcaxOpenChat-Preview2-13B
-OpenChat Llama2 V1: see [Open-Orca's page](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B) for additional information. 
 
-### Training Datasets
+
+# Prompt Template for base OpenOrcaxOpenChat-Preview2-13B
+
+OpenChat Llama2 V1: see [OpenOrcaxOpenChat-Preview2-13B](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B) for additional information. 
+
+
+# Training Datasets
 
 `garage-bAInd/Platypus2-13B` trained using STEM and logic based dataset [`garage-bAInd/Open-Platypus`](https://huggingface.co/datasets/garage-bAInd/Open-Platypus).
 
@@ -53,11 +63,13 @@ Please see our [paper](https://platypus-llm.github.io/Platypus.pdf) and [project
 
 [`Open-Orca/OpenOrcaxOpenChat-Preview2-13B`] trained using a refined, 220k subset of the [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca).
 
-### Training Procedure
+
+# Training Procedure
 
 `garage-bAInd/Platypus2-13B` was instruction fine-tuned using LoRA on 1 A100 80GB. For training details and inference instructions please see the [Platypus](https://github.com/arielnlee/Platypus) GitHub repo.
 
-### Reproducing Evaluation Results
+
+# Reproducing Evaluation Results
 
 Install LM Evaluation Harness:
 ```
@@ -92,13 +104,15 @@ TruthfulQA:
 python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/OpenOrca-Platypus2-13B --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/OpenOrca-Platypus2-13B/truthfulqa_0shot.json --device cuda
 ```
 
-### Limitations and bias
+
+# Limitations and bias
 
 Llama 2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model.
 
 Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/
 
-### Citations
+
+# Citations
 
 ```bibtex
 @misc{touvron2023llama,