Instructions to use SUBEAN7/FinAI-Llama3-Ko-Adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SUBEAN7/FinAI-Llama3-Ko-Adapter with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SUBEAN7/FinAI-Llama3-Ko-Adapter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SUBEAN7/FinAI-Llama3-Ko-Adapter", device_map="auto")

PEFT
How to use SUBEAN7/FinAI-Llama3-Ko-Adapter with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SUBEAN7/FinAI-Llama3-Ko-Adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SUBEAN7/FinAI-Llama3-Ko-Adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SUBEAN7/FinAI-Llama3-Ko-Adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SUBEAN7/FinAI-Llama3-Ko-Adapter

SGLang

How to use SUBEAN7/FinAI-Llama3-Ko-Adapter with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SUBEAN7/FinAI-Llama3-Ko-Adapter" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SUBEAN7/FinAI-Llama3-Ko-Adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SUBEAN7/FinAI-Llama3-Ko-Adapter" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SUBEAN7/FinAI-Llama3-Ko-Adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SUBEAN7/FinAI-Llama3-Ko-Adapter with Docker Model Runner:
```
docker model run hf.co/SUBEAN7/FinAI-Llama3-Ko-Adapter
```

FinAI-Llama3-Ko

🚀 Korean Financial Recommendation LLM fine-tuned with LoRA and enhanced with customer/product retrieval-based RAG.

Model Overview

FinAI-Llama3-Ko is a Korean financial recommendation model fine-tuned from Llama-3-Open-Ko-8B using LoRA (Low-Rank Adaptation).

The model is designed to provide personalized financial and insurance recommendations by analyzing customer profiles, investment tendencies, and financial goals.

This repository contains LoRA adapter weights only.

To use this model, load the adapter together with the base model:

Base Model: beomi/Llama-3-Open-Ko-8B

Key Features

Personalized Financial Consultation

The model generates recommendations based on:

Age
Gender
Income level
Occupation
Credit rating
Risk preference
Existing financial products
Financial goals

Explainable Recommendation Generation

Instead of recommending products directly, the model is trained to generate:

Customer Analysis
Investment Profile Analysis
Recommendation Rationale
Final Recommendation

This enables more transparent and explainable financial advice.

Insurance & Securities Support

Supported domains:

Insurance products
Securities products
Retirement planning
Savings planning
Investment recommendations
Lifecycle-based financial consultation

Dataset

This model was fine-tuned using the labeled dataset from the AI Hub project:

Financial Products, Services and Consumer Characteristics Dataset

Provider: AI Hub (Korea)
Language: Korean
Domain: Finance / Insurance / Securities

Training Samples

Category	Samples
Securities	6,400
Insurance	1,600
Total	8,000

The dataset contains:

Customer profiles
Financial product information
Investment preferences
Recommendation rationales
Consultation scenarios

Data Processing

The original structured consultation data was transformed into an instruction-following format suitable for Supervised Fine-Tuning (SFT).

Training examples follow the format:

<s>[INST]
당신은 생애주기 기반 금융/보험 전문 AI 어드바이저입니다.

[분야]
보험 또는 증권

[고객질문]
...
[/INST]

[고객분석]
...

[투자성향]
...

[추천근거]
...

[최종답변]
...
</s>

Reasoning structure:

고객질문
    ↓
고객분석
    ↓
투자성향
    ↓
추천근거
    ↓
최종답변

Training Configuration

Parameter	Value
Base Model	beomi/Llama-3-Open-Ko-8B
Fine-tuning Method	LoRA
Epochs	2
Learning Rate	2e-4
Batch Size	1
Max Sequence Length	2048
LoRA Rank (r)	16
LoRA Alpha	32
GPU	NVIDIA A100
Framework	Transformers + PEFT

Example

Input

[분야]
증권

[고객질문]
현재 자산은 약 4천만원이며, 결혼자금 마련이 목표입니다. 투자기간은 5년 이상이고, 원금 손실은 크지 않았으면 좋겠습니다.

Output

고객님께서는 결혼자금 마련이라는 명확한 목적과 함께, 비교적 짧은 투자 기간(5년 이상)을 갖고 계시며, 큰 원금손실에는 부담을 느끼시는 것으로 파악됩니다.
이에 따라 단기적인 변동성이 낮고 중도 환매가 가능한 유동성을 중요하게 고려하셨으며, 세제혜택이나 현금흐름에도 관심을 두고 있습니다.
'OOOOOO OO'는 국공채 등 우량 채권에 투자해 위험이 매우 낮고, 평균 1.62%의 과거 성과를 보여왔습니다.
'OOO OOOOOOO' 역시 만기가 없고 언제 든 해지가 가능하며, 신용도가 높은 기업어음 중심으로 운용되어 낮은 위험을 추구합니다.
두 상품 모두 위험등급이 6등급(매우낮은위험)으로 분류되어 있어, 고객님의 요구 조건인 원금 손실 최소화와 자금 활용 유연성을 동시에 만족할 수 있는 선택지라고 할 수 있겠습니다.
다만 각 상품마다 세제혜택 제공 여부 또는 추가 납입 방식, 그리고 실제 투자 대상 차이가 있을 수 있으므로, 구체적인 장단점까지 비교하신 후 결정하시면 더욱 좋겠습니다.

RAG Pipeline (Implemented)

FinAI-Llama3-Ko incorporates a RAG-based recommendation system.

Customer Vector Database (335,506 customer records)
Product Vector Database
FAISS-based Semantic Search
Similar Customer Retrieval
Personalized Recommendation Generation

Future Work

Intended Use

Suitable for:

Financial recommendation research
Korean financial NLP research
Financial chatbot development
Insurance recommendation systems
Retrieval-Augmented Generation experiments

Not intended for:

Real financial advice
Investment guarantees
Production financial services without human review

Limitations

Recommendations are generated from historical training data.
Financial products and regulations may change over time.
Outputs should be reviewed by qualified financial professionals before use in real-world financial decision-making.

Disclaimer

This model is intended for research and educational purposes only.

The generated outputs should not be interpreted as professional financial, investment, insurance, or legal advice.

Users are responsible for verifying all recommendations before making financial decisions.

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