Instructions to use Rustamshry/Personal-Finance-R2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rustamshry/Personal-Finance-R2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Rustamshry/Personal-Finance-R2-GGUF",
	filename="Personal-Finance-R2-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Rustamshry/Personal-Finance-R2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rustamshry/Personal-Finance-R2-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Rustamshry/Personal-Finance-R2-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rustamshry/Personal-Finance-R2-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Rustamshry/Personal-Finance-R2-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Rustamshry/Personal-Finance-R2-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Rustamshry/Personal-Finance-R2-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Rustamshry/Personal-Finance-R2-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Rustamshry/Personal-Finance-R2-GGUF:F16

Use Docker

docker model run hf.co/Rustamshry/Personal-Finance-R2-GGUF:F16

LM Studio
Jan

vLLM

How to use Rustamshry/Personal-Finance-R2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rustamshry/Personal-Finance-R2-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rustamshry/Personal-Finance-R2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rustamshry/Personal-Finance-R2-GGUF:F16

Ollama
How to use Rustamshry/Personal-Finance-R2-GGUF with Ollama:
```
ollama run hf.co/Rustamshry/Personal-Finance-R2-GGUF:F16
```

Unsloth Studio new

How to use Rustamshry/Personal-Finance-R2-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Rustamshry/Personal-Finance-R2-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Rustamshry/Personal-Finance-R2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Rustamshry/Personal-Finance-R2-GGUF to start chatting

Pi new

How to use Rustamshry/Personal-Finance-R2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Rustamshry/Personal-Finance-R2-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Personal-Finance-R2-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Rustamshry/Personal-Finance-R2-GGUF with Docker Model Runner:
```
docker model run hf.co/Rustamshry/Personal-Finance-R2-GGUF:F16
```

Lemonade

How to use Rustamshry/Personal-Finance-R2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Rustamshry/Personal-Finance-R2-GGUF:F16

Run and chat with the model

lemonade run user.Personal-Finance-R2-GGUF-F16

List all available models

lemonade list

Model Card for Model ID

GGUF version of https://huggingface.co/khazarai/Personal-Finance-R2

This model is fine-tuned for instruction-following in the domain of personal finance, with a focus on:

Budgeting advice
Investment strategies
Credit management
Retirement planning
Insurance and financial planning concepts
Personalized financial reasoning

Model Description

License: MIT
Finetuned from model: unsloth/Qwen3-1.7B
Dataset: The model was fine-tuned on the Kuvera-PersonalFinance-V2.1, curated and published by Akhil-Theerthala.

Model Capabilities

Understands and provides contextual financial advice based on user queries.
Responds in a chat-like conversational format.
Trained to follow multi-turn instructions and deliver clear, structured, and accurate financial reasoning.
Generalizes well to novel personal finance questions and explanations.

Uses

Direct Use

Chatbots for personal finance
Educational assistants for financial literacy
Decision support for simple financial planning
Interactive personal finance Q&A systems

Bias, Risks, and Limitations

Not a substitute for licensed financial advisors.
The model's advice is based on training data and may not reflect region-specific laws, regulations, or financial products.
May occasionally hallucinate or give generic responses in ambiguous scenarios.
Assumes user input is well-formed and relevant to personal finance.

Training Data

Dataset Overview: Kuvera-PersonalFinance-V2.1 is a collection of high-quality instruction-response pairs focused on personal finance topics. It covers a wide range of subjects including budgeting, saving, investing, credit management, retirement planning, insurance, and financial literacy.
Data Format: The dataset consists of conversational-style prompts paired with detailed and well-structured responses. It is formatted to enable instruction-following language models to understand and generate coherent financial advice and reasoning.