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RichardErkhov/
asiansoul_-_SmartLlama-3-Ko-8B-256k-PoSE-gguf

Name: asiansoul_-_SmartLlama-3-Ko-8B-256k-PoSE-gguf
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SmartLlama-3-Ko-8B-256k-PoSE - GGUF

Model creator: https://huggingface.co/asiansoul/
Original model: https://huggingface.co/asiansoul/SmartLlama-3-Ko-8B-256k-PoSE/

Name	Quant method	Size
SmartLlama-3-Ko-8B-256k-PoSE.Q2_K.gguf	Q2_K	2.96GB
SmartLlama-3-Ko-8B-256k-PoSE.IQ3_XS.gguf	IQ3_XS	3.28GB
SmartLlama-3-Ko-8B-256k-PoSE.IQ3_S.gguf	IQ3_S	3.43GB
SmartLlama-3-Ko-8B-256k-PoSE.Q3_K_S.gguf	Q3_K_S	3.41GB
SmartLlama-3-Ko-8B-256k-PoSE.IQ3_M.gguf	IQ3_M	3.52GB
SmartLlama-3-Ko-8B-256k-PoSE.Q3_K.gguf	Q3_K	3.74GB
SmartLlama-3-Ko-8B-256k-PoSE.Q3_K_M.gguf	Q3_K_M	3.74GB
SmartLlama-3-Ko-8B-256k-PoSE.Q3_K_L.gguf	Q3_K_L	4.03GB
SmartLlama-3-Ko-8B-256k-PoSE.IQ4_XS.gguf	IQ4_XS	4.18GB
SmartLlama-3-Ko-8B-256k-PoSE.Q4_0.gguf	Q4_0	4.34GB
SmartLlama-3-Ko-8B-256k-PoSE.IQ4_NL.gguf	IQ4_NL	4.38GB
SmartLlama-3-Ko-8B-256k-PoSE.Q4_K_S.gguf	Q4_K_S	4.37GB
SmartLlama-3-Ko-8B-256k-PoSE.Q4_K.gguf	Q4_K	4.58GB
SmartLlama-3-Ko-8B-256k-PoSE.Q4_K_M.gguf	Q4_K_M	4.58GB
SmartLlama-3-Ko-8B-256k-PoSE.Q4_1.gguf	Q4_1	4.78GB
SmartLlama-3-Ko-8B-256k-PoSE.Q5_0.gguf	Q5_0	5.21GB
SmartLlama-3-Ko-8B-256k-PoSE.Q5_K_S.gguf	Q5_K_S	5.21GB
SmartLlama-3-Ko-8B-256k-PoSE.Q5_K.gguf	Q5_K	5.34GB
SmartLlama-3-Ko-8B-256k-PoSE.Q5_K_M.gguf	Q5_K_M	5.34GB
SmartLlama-3-Ko-8B-256k-PoSE.Q5_1.gguf	Q5_1	5.65GB
SmartLlama-3-Ko-8B-256k-PoSE.Q6_K.gguf	Q6_K	6.14GB
SmartLlama-3-Ko-8B-256k-PoSE.Q8_0.gguf	Q8_0	7.95GB

Original model description:

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 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. 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. 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. 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. 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. 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.

RichardErkhov/asiansoul_-_SmartLlama-3-Ko-8B-256k-PoSE-gguf