Update README.md

Update evaluation results

README.md

@@ -14,13 +14,28 @@ tags:
 
 Falcon-RW-1B-Instruct-OpenOrca is a 1B parameter, causal decoder-only model based on [Falcon-RW-1B](https://huggingface.co/tiiuae/falcon-rw-1b) and finetuned on the [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) dataset.
 
+**📊 Evaluation Results**
+
+Falcon-RW-1B-Instruct-OpenOrca is the #1 ranking model on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) in ~1.5B parameters category!
+
+| Metric     | falcon-rw-1b-instruct-openorca | falcon-rw-1b |
+|------------|-------------------------------:|-------------:|
+| ARC        |                          34.56 |        35.07 |
+| HellaSwag  |                          60.93 |        63.56 |
+| MMLU       |                          28.77 |        25.28 |
+| TruthfulQA |                          37.42 |        35.96 |
+| Winogrande |                          60.69 |        62.04 |
+| GSM8K      |                           1.21 |         0.53 |
+| DROP       |                          21.94 |         4.64 |
+| **Average**|                      **35.08** |    **32.44** |
+
 **🚀 Motivations**
-1. To create a smaller, open-source, instruction-finetuned, ready-to-use model accessible for users with limited computational resources; to make a decent ready-to-use model for running on lower-end consumer GPUs.
+1. To create a smaller, open-source, instruction-finetuned, ready-to-use model accessible for users with limited computational resources (lower-end consumer GPUs).
 2. To harness the strength of Falcon-RW-1B, a competitive model in its own right, and enhance its capabilities with instruction finetuning.
 
 ## 📖 How to Use
 
- The model operates with a structured prompt format, incorporating `<SYS>`, `<INST>`, and `<RESP>` tags to demarcate different parts of the input. The system message and instruction are placed within these tags, with the `<RESP>` tag triggering the model's response.
+The model operates with a structured prompt format, incorporating `<SYS>`, `<INST>`, and `<RESP>` tags to demarcate different parts of the input. The system message and instruction are placed within these tags, with the `<RESP>` tag triggering the model's response.
 
 ### 📝 Example Code
 
@@ -33,16 +48,16 @@ model = 'ericzzz/falcon-rw-1b-instruct-openorca'
 
 tokenizer = AutoTokenizer.from_pretrained(model)
 pipeline = transformers.pipeline(
-    "text-generation",
+    'text-generation',
     model=model,
     tokenizer=tokenizer,
     torch_dtype=torch.bfloat16,
-    device_map="auto",
+    device_map='auto',
 )
 
-system_message = "You are a helpful assistant. Give short answers."
-instruction = "What is AI? Give some examples."
-prompt = f"<SYS> {system_message} <INST> {instruction} <RESP> "
+system_message = 'You are a helpful assistant. Give short answers.'
+instruction = 'What is AI? Give some examples.'
+prompt = f'<SYS> {system_message} <INST> {instruction} <RESP> '
 
 response = pipeline(
     prompt, 
@@ -54,5 +69,5 @@ print(response[0]['generated_text'])
 # AI, or Artificial Intelligence, refers to the ability of machines and software to perform tasks that require human intelligence, such as learning, reasoning, and problem-solving. It can be used in various fields like computer science, engineering, medicine, and more. Some common applications include image recognition, speech translation, and natural language processing.
 ```
 
-## Contact
-[email protected]
+## 📬 Contact
+For further inquiries or feedback, please contact at [email protected].