metadata

license: llama3
language:
  - ko
tags:
  - korean
  - llama3
  - instruction-tuning
  - dora
datasets:
  - Acyrl
  - llm-kr-eval
  - Counter-MT-bench
base_model:
  - meta-llama/Meta-Llama-3-8B
pipeline_tag: text-generation

A-LLM: Korean Large Language Model based on Llama-3

Introduction

A-LLM is a Korean large language model built on Meta's Llama-3-8B architecture , specifically optimized for Korean language understanding and generation. The model was trained using the DoRA (Weight-Decomposed Low-Rank Adaptation) methodology on a comprehensive Korean dataset , achieving state-of-the-art performance among open-source Korean language models.

Performance Benchmarks

Horangi Korean LLM Leaderboard

The model's performance was evaluated using the Horangi Korean LLM Leaderboard , which combines two major evaluation frameworks normalized to a 1.0 scale and averages their scores.

1. LLM-KR-EVAL

A comprehensive benchmark that measures fundamental NLP capabilities across 5 core tasks:

Natural Language Inference (NLI)
Question Answering (QA)
Reading Comprehension (RC)
Entity Linking (EL)
Fundamental Analysis (FA)

The benchmark comprises 10 different datasets distributed across these tasks , providing a thorough assessment of Korean language understanding and processing capabilities.

2. MT-Bench

A diverse evaluation framework consisting of 80 questions (10 questions each from 8 categories) , evaluated using GPT-4 as the judge. Categories include:

Writing
Roleplay
Extraction
Reasoning
Math
Coding
Knowledge (STEM)
Knowledge (Humanities/social science)

Performance Results(documented on 10/04/24)

Model	Total Score	AVG_llm_kr_eval	AVG_mtbench
A-LLM (Ours)	0.6675	0.5937	7.413
GPT-4	0.7363	0.6158	8.569
Mixtral-8x7B	0.5843	0.4304	7.381
KULLM3	0.5764	0.5204	6.325
SOLAR-1-mini	0.5173	0.37	6.647

Our model achieves state-of-the-art performance among open-source Korean large language models , demonstrating strong capabilities across both general language understanding (LLM-KR-EVAL) and diverse task-specific applications (MT-Bench).

Model Components

This repository provides:

Tokenizer configuration
Model weights in safetensor format

Usage Instructions

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load tokenizer and model
model_path = "JinValentine/Jonathan-aLLM-Meta-Llama-3-8B-Instruct-Korean-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Example prompt template
def generate_prompt(instruction: str, context: str = None) -> str:
    if context:
        return f"""### Instruction:
{instruction}

### Context:
{context}

### Response:"""
    else:
        return f"""### Instruction:
{instruction}

### Response:"""

# Example usage
instruction = "다음 질문에 답변해주세요: 인공지능의 발전이 우리 사회에 미치는 영향은 무엇일까요?"
prompt = generate_prompt(instruction)

# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_length=512,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.2,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Generation Settings

# generation parameters
generation_config = {
  "bos_token_id": 128000,
  "do_sample": True,
  "eos_token_id": 128001,
  "max_length": 4096,
  "temperature": 0.6,
  "top_p": 0.9,
  "transformers_version": "4.40.1"
}

# Generate with specific config
outputs = model.generate(
    **inputs,
    **generation_config
)

Prerequisites

Python 3.8 or higher
PyTorch 2.0 or higher
Transformers library

License

Please refer to the model card on HuggingFace for licensing information.