Instructions to use RayNene/MyFinanceExpertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RayNene/MyFinanceExpertModel with Transformers:

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

pipe = pipeline("text-generation", model="RayNene/MyFinanceExpertModel")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RayNene/MyFinanceExpertModel")
model = AutoModelForCausalLM.from_pretrained("RayNene/MyFinanceExpertModel")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RayNene/MyFinanceExpertModel with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/RayNene/MyFinanceExpertModel

SGLang

How to use RayNene/MyFinanceExpertModel 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 "RayNene/MyFinanceExpertModel" \
    --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": "RayNene/MyFinanceExpertModel",
		"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 "RayNene/MyFinanceExpertModel" \
        --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": "RayNene/MyFinanceExpertModel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RayNene/MyFinanceExpertModel with Docker Model Runner:
```
docker model run hf.co/RayNene/MyFinanceExpertModel
```

MyFinanceExpertModel

Helping people make more informed financial decisions through accessible AI.

A finance-specialized LoRA adaptation of Llama 3.3 70B Instruct.

Why this model exists

Money influences almost every major life decision, yet financial education often arrives too late—or not at all.

Many people receive their first salary without ever learning how to build a budget. Others sign loan agreements without fully understanding interest rates, struggle to compare financial products, or fall victim to increasingly sophisticated scams. For many, professional financial advice is either too expensive, difficult to access, or filled with technical language that makes important decisions even harder.

At the same time, AI has become one of the first places people turn to for answers.

That creates an opportunity—and a responsibility.

MyFinanceExpertModel was built to make financial knowledge more approachable by helping language models explain financial concepts with greater clarity, context, and practical guidance. Rather than simply generating answers, the goal is to support better financial understanding and encourage informed decision-making.

🚀 How to Use

This repository contains LoRA adapter weights for Llama 3.3 70B Instruct.

First, load the base model, then apply the adapter:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE_MODEL = "meta-llama/Llama-3.3-70B-Instruct"
ADAPTER = "RayNene/MyFinanceExpertModel"

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    device_map="auto"
)

model = PeftModel.from_pretrained(
    base_model,
    ADAPTER
)

💬 Prompting Tips

The model performs best when given realistic financial scenarios rather than short keyword prompts.

Good prompts

I'm 23 and just received my first salary. Can you help me create a monthly budget that balances rent, savings, emergency funds, and discretionary spending?

Compare a fixed-rate mortgage and an adjustable-rate mortgage. Which situations is each best suited for?

I have a credit card with a 22% annual interest rate and a personal loan with a lower interest rate. Which debt should I prioritize paying off first, and why?

A company reports revenue growth but declining net income. What financial factors could explain this?

How can I identify common investment scams before committing my money?

💡 Best Practices

For the highest-quality responses:

Provide relevant financial context.
Specify your country or region if asking about taxes or regulations.
Mention your goals (saving, investing, budgeting, debt repayment, retirement, etc.).
Ask follow-up questions for deeper explanations.

For example, instead of asking:

"Should I invest?"

Try:

"I'm a recent graduate with $5,000 in savings, no debt, and a five-year investment horizon. What investment approaches should I research, and what are the risks of each?"

⚠️ Important Notice

MyFinanceExpertModel is intended for education and research. Although adapted for financial topics, it should not replace advice from licensed financial, tax, legal, or investment professionals.

Always verify important financial information before making significant financial decisions.

What it specializes in

The model has been adapted for a wide range of financial topics, including:

Personal finance
Budgeting
Saving and investing
Banking
Credit cards and loans
Mortgages
Insurance
Taxes
Accounting
Corporate finance
Financial literacy
Fraud detection
Scam awareness
Risk management

Model Information

Base Model

meta-llama/Llama-3.3-70B-Instruct

Training Data

The model was adapted using FinanceExpert Dataset, a finance-focused instruction tuning dataset containing enhanced prompts, enriched responses, and semantically processed examples covering real-world financial scenarios.

The dataset is publicly available on Hugging Face.

Intended Use

This model is designed for:

Financial education
Personal finance assistants
Banking support chatbots
Financial literacy applications
Research into domain-adapted language models
Conversational financial guidance

Limitations

Although adapted for financial topics, this model is not a substitute for licensed financial, tax, legal, or investment professionals.

Responses should be treated as educational information and verified before making significant financial decisions.

Vision

Access to financial knowledge should not depend on income, geography, or prior experience.

By openly releasing both the model and its training dataset, this project aims to contribute to a future where trustworthy financial education is easier to access for everyone.

Acknowledgements

Developed as part of the Adaptation Labs AutoScientist Challenge, supporting the development of open, domain-specialized language models.

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Base model

meta-llama/Llama-3.1-70B

Finetuned

meta-llama/Llama-3.3-70B-Instruct

Adapter

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this model

RayNene
/

MyFinanceExpertModel