EEVE Korean Instruct 10.8B V1.0 Grade Retrieval

🚀 EEVE-Korean-Instruct-10.8B-v1.0-Grade-Retrieval

This model is fine - tuned to evaluate whether the retrieved context for a question in RAG is correct, providing a yes - or - no answer.

🚀 Quick Start

This model has been fine - tuned to evaluate whether the retrieved context for a question in RAG is correct with a yes or no answer.

The base model for this model is yanolja/EEVE-Korean-Instruct-10.8B-v1.0.

✨ Features

• Fine - Tuned for RAG Evaluation: Capable of evaluating if the retrieved context for a question in RAG is correct with a simple yes or no answer.
• Based on a Strong Foundation: Built upon the yanolja/EEVE-Korean-Instruct-10.8B-v1.0 base model.

📦 Installation

The installation mainly involves setting up the necessary Python libraries. You need to have torch and transformers installed. You can install them using the following commands:

pip install torch
pip install transformers

💻 Usage Examples

Basic Usage

import torch
from transformers import (
    BitsAndBytesConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
)

model_path = "sinjy1203/EEVE-Korean-Instruct-10.8B-v1.0-Grade-Retrieval"
nf4_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.float16,
)

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path, quantization_config=nf4_config, device_map={'': 'cuda:0'}
)

prompt_template = '주어진 질문과 정보가 주어졌을 때 질문에 답하기에 충분한 정보인지 평가해줘.\n정보가 충분한지를 평가하기 위해 "예" 또는 "아니오"로 답해줘.\n\n### 질문:\n{question}\n\n### 정보:\n{context}\n\n### 평가:\n'
query = {
    "question": "동아리 종강총회가 언제인가요?",
    "context": "종강총회 날짜는 6월 21일입니다."
}

model_inputs = tokenizer(prompt_template.format_map(query), return_tensors='pt')
output = model.generate(**model_inputs, max_new_tokens=100, max_length=200)
print(output)

Example Output

주어진 질문과 정보가 주어졌을 때 질문에 답하기에 충분한 정보인지 평가해줘.
정보가 충분한지를 평가하기 위해 "예" 또는 "아니오"로 답해줘.

### 질문:
동아리 종강총회가 언제인가요?

### 정보:
종강총회 날짜는 6월 21일입니다.

### 평가:
예<|end_of_text|>

📚 Documentation

Prompt Template

주어진 질문과 정보가 주어졌을 때 질문에 답하기에 충분한 정보인지 평가해줘.
정보가 충분한지를 평가하기 위해 "예" 또는 "아니오"로 답해줘. 

### 질문: 
{question}

### 정보: 
{context}

### 평가: 

Training Data

• Referenced generated_instruction by stanford_alpaca
• use yanolja/EEVE-Korean-Instruct-10.8B-v1.0 as the model for question generation.

📊 Metrics

Korean LLM Benchmark

Generated Dataset

📄 License

This project is licensed under the Apache - 2.0 license.