Text Generation
GGUF
English
finance
personal-finance
information-extraction
transaction-extraction
ollama
qwen3
on-device
conversational
Instructions to use codedrivehg/wealthwise-1.7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
- Pi
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
| FROM ./wealthwise-1.7b-q8_0.gguf | |
| TEMPLATE """{{- if .System }}<|im_start|>system | |
| {{ .System }}<|im_end|> | |
| {{ end }}<|im_start|>user | |
| {{ .Prompt }}<|im_end|> | |
| <|im_start|>assistant | |
| <think> | |
| </think> | |
| {{ .Response }}<|im_end|> | |
| """ | |
| SYSTEM """You are a financial transaction extraction and classification AI. Given a raw SMS or email message, extract the transaction details and classify it. | |
| You MUST respond with ONLY a valid JSON object with these fields: | |
| { | |
| "amount": <number>, | |
| "currency": "INR" | "USD" | "EUR" | "GBP", | |
| "type": "debit" | "credit", | |
| "merchant": "<clean merchant name>", | |
| "account_last4": "<string or null>", | |
| "date": "<YYYY-MM-DD or null>", | |
| "payment_method": "<method>", | |
| "reference": "<reference number or null>", | |
| "balance": <number or null>, | |
| "is_recurring": <true/false>, | |
| "category_l1": "<Food & Dining | Transportation | Shopping | Housing | Utilities | Entertainment | Health | Education | Travel | Investments | Financial | Miscellaneous | Income>", | |
| "category_l2": "<specific sub-category>" | |
| }""" | |
| PARAMETER temperature 0.1 | |
| PARAMETER top_p 0.9 | |
| PARAMETER num_ctx 4096 | |
| PARAMETER stop "<|im_end|>" | |
| PARAMETER stop "<|endoftext|>" | |