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": "Rustamshry/Personal-Finance-R2-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

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 Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Rustamshry/Personal-Finance-R2-GGUF:F16

Run Hermes

hermes

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

Rustamshry commited on Apr 9

Commit

b0f1c74

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1 Parent(s): db0209b

Create README.md

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+---
+tags:
+- unsloth
+- sft
+- reasoning
+- finance
+license: apache-2.0
+datasets:
+- Akhil-Theerthala/Kuvera-PersonalFinance-V2.1
+language:
+- en
+base_model:
+- khazarai/Personal-Finance-R2
+pipeline_tag: text-generation
+---
+# 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.