Instructions to use codedrivehg/wealthwise-1.7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codedrivehg/wealthwise-1.7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="codedrivehg/wealthwise-1.7b-GGUF",
	filename="wealthwise-1.7b-q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use codedrivehg/wealthwise-1.7b-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf codedrivehg/wealthwise-1.7b-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf codedrivehg/wealthwise-1.7b-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf codedrivehg/wealthwise-1.7b-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf codedrivehg/wealthwise-1.7b-GGUF:Q8_0

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 codedrivehg/wealthwise-1.7b-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf codedrivehg/wealthwise-1.7b-GGUF:Q8_0

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 codedrivehg/wealthwise-1.7b-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf codedrivehg/wealthwise-1.7b-GGUF:Q8_0

Use Docker

docker model run hf.co/codedrivehg/wealthwise-1.7b-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use codedrivehg/wealthwise-1.7b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codedrivehg/wealthwise-1.7b-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": "codedrivehg/wealthwise-1.7b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/codedrivehg/wealthwise-1.7b-GGUF:Q8_0

Ollama
How to use codedrivehg/wealthwise-1.7b-GGUF with Ollama:
```
ollama run hf.co/codedrivehg/wealthwise-1.7b-GGUF:Q8_0
```

Unsloth Studio

How to use codedrivehg/wealthwise-1.7b-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 codedrivehg/wealthwise-1.7b-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 codedrivehg/wealthwise-1.7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for codedrivehg/wealthwise-1.7b-GGUF to start chatting

How to use codedrivehg/wealthwise-1.7b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf codedrivehg/wealthwise-1.7b-GGUF:Q8_0

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": "codedrivehg/wealthwise-1.7b-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use codedrivehg/wealthwise-1.7b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf codedrivehg/wealthwise-1.7b-GGUF:Q8_0

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 codedrivehg/wealthwise-1.7b-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use codedrivehg/wealthwise-1.7b-GGUF with Docker Model Runner:
```
docker model run hf.co/codedrivehg/wealthwise-1.7b-GGUF:Q8_0
```

Lemonade

How to use codedrivehg/wealthwise-1.7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull codedrivehg/wealthwise-1.7b-GGUF:Q8_0

Run and chat with the model

lemonade run user.wealthwise-1.7b-GGUF-Q8_0

List all available models

lemonade list

WealthWise 1.7B (GGUF)

A compact, on-device model that extracts and classifies financial transactions from SMS and email into structured JSON. Fine-tuned from Qwen3-1.7B for the WealthWise local-first personal-finance app.

🚧 Status: experimental / under active development. WealthWise's larger 14B model is the most accurate today; this 1.7B is the phone-sized variant that is catching up. On controlled benchmarks it already matches or beats the 14B; on the long tail of real-world mail it is still improving via distillation from the 14B teacher and user corrections. It is ~8× smaller and several× faster and is intended for laptops, low-power machines, and mobile.

What it does

Given a raw SMS or email message, it returns a single JSON object:

{
  "amount": 437.0, "currency": "INR", "type": "debit",
  "merchant": "Swiggy - Behrouz Biryani", "account_last4": null,
  "date": "2026-06-12", "payment_method": "UPI", "reference": null,
  "balance": null, "is_recurring": false,
  "category_l1": "Food & Dining", "category_l2": "Food Delivery"
}

category_l1 is one of: Food & Dining, Transportation, Shopping, Housing, Utilities, Entertainment, Health, Education, Travel, Investments, Financial, Income, Miscellaneous. Non-transactional messages return {"is_transaction": false}.

Files

wealthwise-1.7b-q8_0.gguf — Q8_0 quantized weights (~1.8 GB, near-lossless).
Modelfile — Ollama recipe (correct non-thinking Qwen3 template + system prompt).

Run it

Ollama (recommended)

# from this folder:
ollama create wealthwise-1.7b -f Modelfile
ollama run wealthwise-1.7b
# or, if published to the Ollama registry:
# ollama pull codedrivehg/wealthwise-1.7b

Download the GGUF:

hf download codedrivehg/wealthwise-1.7b-GGUF --local-dir wealthwise-1.7b

llama.cpp

llama-cli -m wealthwise-1.7b-q8_0.gguf -p "<your message>"

Important: this model is trained for non-thinking output (direct JSON). The bundled Modelfile pre-fills the empty <think></think> block Qwen3 expects — use it (or replicate that template) or the model may emit garbage.

Training

Base: Qwen/Qwen3-1.7B (Apache-2.0).
Method: LoRA fine-tune (attention + MLP) on financial extraction data, then merged to 16-bit and exported to GGUF.
Improving via knowledge distillation from the WealthWise 14B teacher (and a larger teacher), an expanding curated merchant database, and in-app corrections.

Limitations

Out-of-distribution real-world emails (unusual formats, multilingual, receipts with many line items) are where it still trails the 14B.
Use the 14B for maximum accuracy on capable hardware.

License

Apache-2.0 (inherits the Qwen3-1.7B base license).

Downloads last month: 20

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

8-bit

Model tree for codedrivehg/wealthwise-1.7b-GGUF

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

(298)

this model