---
base_model:
- winglian/llama-3-8b-256k-PoSE
- Locutusque/Llama-3-Orca-1.0-8B
- NousResearch/Meta-Llama-3-8B
- abacusai/Llama-3-Smaug-8B
- beomi/Llama-3-Open-Ko-8B-Instruct-preview
- NousResearch/Meta-Llama-3-8B-Instruct
library_name: transformers
tags:
- mergekit
- merge
---
# 🇰🇷 SmartLlama-3-Ko-8B-256k-PoSE
SmartLlama-3-Ko-8B-256k-[PoSE](https://huggingface.co/papers/2309.10400) is an advanced AI model that integrates the capabilities of several advanced language models, designed to excel in a variety of tasks ranging from technical problem-solving to multilingual communication, especially with its extended context length of 256k tokens. This model is uniquely positioned to handle larger and more complex datasets and longer conversational contexts, making it ideal for deep learning applications requiring extensive text understanding and generation.
## 📕 Merge Details
### Component Models and Contributions
- **NousResearch/Meta-Llama-3-8B and Meta-Llama-3-8B-Instruct**: These models provide a solid foundation for general language understanding and instruction-following capabilities.
- **winglian/llama-3-8b-256k-PoSE**: Utilizes Positional Skip-wise Training (PoSE) to extend Llama's context length to 256k, significantly improving the model's ability to handle extensive texts and complex instructions, enhancing performance in tasks requiring long-duration focus and memory.
- **Locutusque/Llama-3-Orca-1.0-8B**: Specializes in mathematical, coding, and writing tasks, bringing precision to technical and creative outputs.
- **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.
- **Multilingual Support**: While primarily focused on Korean language processing, this model also provides robust support for multiple languages, enhancing its utility in global applications.
- **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.
- **Locutusque/Llama-3-Orca-1.0-8B**: [Enhances abilities in handling technical content](https://huggingface.co/Locutusque/Llama-3-Orca-1.0-8B). Specialized in computational, scientific, and technical tasks, improving the model's ability to process complex academic and technical language.
- **abacusai/Llama-3-Smaug-8B**: [Improves multi-turn conversational abilities](https://huggingface.co/abacusai/Llama-3-Smaug-8B). Boosts performance in engaging in lengthy, context-aware dialogues necessary for effective customer service and interactive learning.
- **beomi/Llama-3-Open-Ko-8B-Instruct-preview**: [Provides enhanced capabilities for Korean language processing](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview). This model is fine-tuned to understand and generate Korean, making it ideal for applications targeting Korean-speaking users.
- **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
models:
- model: NousResearch/Meta-Llama-3-8B
# Base model providing a general foundation without specific parameters
- model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
density: 0.60
weight: 0.25
- model: winglian/llama-3-8b-256k-PoSE
parameters:
density: 0.60
weight: 0.20
- model: Locutusque/Llama-3-Orca-1.0-8B
parameters:
density: 0.55
weight: 0.15
- model: abacusai/Llama-3-Smaug-8B
parameters:
density: 0.55
weight: 0.15
- model: beomi/Llama-3-Open-Ko-8B-Instruct-preview
parameters:
density: 0.55
weight: 0.30
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
int8_mask: true
dtype: bfloat16
```
### 🎊 Test Result
**SmartLlama-3-Ko-8B-256k-PoSE Summary Ability**
consideration
Long sentences seemed to summarize well, but I observed that answers came in English. And when I asked for it to be translated into Korean, I confirmed that it was translated well. The summary seems to work well, but you can take into account the fact that there are times when it cannot be summarized directly in Korean.
**Source**: [Korea Institute for Industrial Economics and Trade: Macroeconomic Outlook for 2024](https://kocham.org/announcement/%EC%82%B0%EC%97%85%EC%97%B0%EA%B5%AC%EC%9B%90-2024%EB%85%84-%EA%B1%B0%EC%8B%9C%EA%B2%BD%EC%A0%9C-%EC%A0%84%EB%A7%9D).