Update README.md

README.md

@@ -12,13 +12,29 @@ base_model: AIDC-ai-business/Luban-13B
 <!-- 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. -->
 
-# Panda-Coder
+# Panda-Coder 🐼
 
 Panda Coder is a state-of-the-art LLM capable of generating code on the NLP based Instructions
 
 ## Model description
 
-This model is a fine-tuned version of [AIDC-ai-business/Luban-13B](https://huggingface.co/AIDC-ai-business/Luban-13B) on Evol Instruct Code 80k-v1 dataset
+🤖 Model Description: Panda-Coder is a state-of-the-art LLM, a fine-tuned model, specifically designed to generate code based on natural language instructions. It's the result of relentless innovation and meticulous fine-tuning, all to make coding easier and more accessible for everyone.
+
+🔗 Key Features:
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+🌟 NLP-Based Coding: With Panda-Coder, you can transform your plain text instructions into functional code effortlessly. No need to grapple with syntax and semantics - it understands your language.
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+🎯 Precision and Efficiency: The model is tailored for accuracy, ensuring your code is not just functional but also efficient.
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+✨ Unleash Creativity: Whether you're a novice or an expert coder, Panda-Coder is here to support your coding journey, offering creative solutions to your programming challenges.
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+📚 Evol Instruct Code: It's built on the robust Evol Instruct Code 80k-v1 dataset, guaranteeing top-notch code generation.
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+📢 What's Next?: We believe in continuous improvement and are excited to announce that in our next release, Panda-Coder will be enhanced with a custom dataset. This dataset will not only expand the language support but also include hardware programming languages like MATLAB, Embedded C, and Verilog. 🧰💡
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+Stay tuned for more updates and be a part of the coding evolution. Join us on this exciting journey as we make AI accessible to all at AI Planet!
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 ## Training and evaluation data
 
@@ -40,10 +56,6 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_ratio: 0.03
 - training_steps: 512
 
-### Training results
-
-
-
 ### Framework versions
 
 - Transformers 4.33.3