asiansoul
/

SmartLlama-3-Ko-8B-256k-PoSE

@@ -27,7 +27,7 @@ SmartLlama-3-Ko-8B-256k-PoSE is an advanced AI model that integrates the capabil
 - **abacusai/Llama-3-Smaug-8B**: Improves the model's performance in real-world, multi-turn conversations, which is crucial for applications in customer service and interactive learning environments.
 - **beomi/Llama-3-Open-Ko-8B-Instruct-preview**: Focuses on improving understanding and generation of Korean, offering robust solutions for bilingual or multilingual applications targeting Korean-speaking audiences.
-## Key Features
 - **Extended Context Length**: Utilizes the PoSE (Positional Encoding) technique to handle up to 256,000 tokens, making it ideal for analyzing large volumes of text such as books, comprehensive reports, and lengthy communications.
@@ -35,7 +35,7 @@ SmartLlama-3-Ko-8B-256k-PoSE is an advanced AI model that integrates the capabil
 - **Advanced Integration of Models**: Combines strengths from various models including NousResearch's Meta-Llama-3-8B, the instruction-following capabilities of Llama-3-Open-Ko-8B-Instruct-preview, and specialized capabilities from models like Llama-3-Smaug-8B for nuanced dialogues and Orca-1.0-8B for technical precision.
-## Models Merged
 The following models were included in the merge:
 - **winglian/llama-3-8b-256k-PoSE**: [Extends the context handling capability](https://huggingface.co/winglian/llama-3-8b-256k-PoSE). This model uses Positional Skip-wise Training (PoSE) to enhance the handling of extended context lengths, up to 256k tokens.
@@ -45,10 +45,10 @@ The following models were included in the merge:
 - **NousResearch/Meta-Llama-3-8B-Instruct**: [Offers advanced instruction-following capabilities](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct). It is optimized to follow complex instructions, enhancing the model's utility in task-oriented dialogues and applications that require a high level of understanding and execution of user commands.
-### Merge Method
 - **DARE TIES**: This method was employed to ensure that each component model contributes effectively to the merged model, maintaining a high level of performance across diverse applications. NousResearch/Meta-Llama-3-8B served as the base model for this integration, providing a stable and powerful framework for the other models to build upon.
-### Configuration
 The YAML configuration for this model:
 ```yaml

 - **abacusai/Llama-3-Smaug-8B**: Improves the model's performance in real-world, multi-turn conversations, which is crucial for applications in customer service and interactive learning environments.
 - **beomi/Llama-3-Open-Ko-8B-Instruct-preview**: Focuses on improving understanding and generation of Korean, offering robust solutions for bilingual or multilingual applications targeting Korean-speaking audiences.
+## 🖼️ Key Features
 - **Extended Context Length**: Utilizes the PoSE (Positional Encoding) technique to handle up to 256,000 tokens, making it ideal for analyzing large volumes of text such as books, comprehensive reports, and lengthy communications.
 - **Advanced Integration of Models**: Combines strengths from various models including NousResearch's Meta-Llama-3-8B, the instruction-following capabilities of Llama-3-Open-Ko-8B-Instruct-preview, and specialized capabilities from models like Llama-3-Smaug-8B for nuanced dialogues and Orca-1.0-8B for technical precision.
+## 🎨 Models Merged
 The following models were included in the merge:
 - **winglian/llama-3-8b-256k-PoSE**: [Extends the context handling capability](https://huggingface.co/winglian/llama-3-8b-256k-PoSE). This model uses Positional Skip-wise Training (PoSE) to enhance the handling of extended context lengths, up to 256k tokens.
 - **NousResearch/Meta-Llama-3-8B-Instruct**: [Offers advanced instruction-following capabilities](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct). It is optimized to follow complex instructions, enhancing the model's utility in task-oriented dialogues and applications that require a high level of understanding and execution of user commands.
+### 🖋️ Merge Method
 - **DARE TIES**: This method was employed to ensure that each component model contributes effectively to the merged model, maintaining a high level of performance across diverse applications. NousResearch/Meta-Llama-3-8B served as the base model for this integration, providing a stable and powerful framework for the other models to build upon.
+### 🗞️ Configuration
 The YAML configuration for this model:
 ```yaml