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Ko Llama3 Luxia 8B by saltlux

 »  All LLMs  »  saltlux  »  Ko Llama3 Luxia 8B   URL Share it on

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Model Card on HF 🤗: https://huggingface.co/saltlux/Ko-Llama3-Luxia-8B 

Ko Llama3 Luxia 8B Benchmarks

LLME Score: 0.22548
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Ko Llama3 Luxia 8B (saltlux/Ko-Llama3-Luxia-8B)

Ko Llama3 Luxia 8B Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
research, text generation
Primary Use Cases:
natural language generation
Additional Notes 
Ko-Llama3-Luxia-8B is optimized for Korean language usage and is trained using an extended Korean vocabulary with Llama-3 Tokenizer.
Supported Languages 
en (partial), ko (full)
Training Details 
Data Sources:
news, law, patents, medicine, history, society, culture, conversation (written/spoken)
Data Volume:
100GB
Methodology:
Pretraining
Context Length:
8000
Training Time:
unknown
Hardware Used:
NVIDIA H100 80GB * 8 units
Model Architecture:
unknown
Input Output 
Input Format:
text
Accepted Modalities:
text
Output Format:
text
LLM NameKo Llama3 Luxia 8B
Repository 🤗https://huggingface.co/saltlux/Ko-Llama3-Luxia-8B 
Model Size8b
Required VRAM16.4 GB
Updated2025-02-22
Maintainersaltlux
Model Typellama
Model Files  4.9 GB: 1-of-4   4.9 GB: 2-of-4   5.0 GB: 3-of-4   1.6 GB: 4-of-4
Supported Languagesen ko
Model ArchitectureLlamaForCausalLM
Licensellama3
Context Length8192
Model Max Length8192
Transformers Version4.37.2
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|end_of_text|>
Vocabulary Size145792
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than saltlux/Ko-Llama3-Luxia-8B.

Rank the Ko Llama3 Luxia 8B Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Release v20241227
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