Instructions to use nguyentd/FinancialAdvice-Qwen2.5-7B.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use nguyentd/FinancialAdvice-Qwen2.5-7B.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 nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M

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 nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M

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 nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M

Use Docker

docker model run hf.co/nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use nguyentd/FinancialAdvice-Qwen2.5-7B.gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nguyentd/FinancialAdvice-Qwen2.5-7B.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": "nguyentd/FinancialAdvice-Qwen2.5-7B.gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M

Ollama
How to use nguyentd/FinancialAdvice-Qwen2.5-7B.gguf with Ollama:
```
ollama run hf.co/nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M
```
Unsloth Desktop

How to use nguyentd/FinancialAdvice-Qwen2.5-7B.gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nguyentd/FinancialAdvice-Qwen2.5-7B.gguf with Docker Model Runner:
```
docker model run hf.co/nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M
```

Lemonade

How to use nguyentd/FinancialAdvice-Qwen2.5-7B.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M

Run and chat with the model

lemonade run user.FinancialAdvice-Qwen2.5-7B.gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use nguyentd/FinancialAdvice-Qwen2.5-7B.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 nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M

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 nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nguyentd/FinancialAdvice-Qwen2.5-7B.gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "nguyentd/FinancialAdvice-Qwen2.5-7B.gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

This is quantized version of nguyentd/FinancialAdvice-Qwen2.5-7B created using llama.cpp

Model Description:

This model, nguyentd/FinancialAdvice-Qwen2.5-7B, is a fine-tuned version of the Qwen-2.5-7B language model. It has been trained on a dataset of questions and answers from the Reddit community r/AskEconomics. This specialization aims to improve the model's ability to provide helpful and informed responses to questions related to economics and personal finance.

Model Source: This model is based on the Qwen-2.5-7B model and has been further trained by nguyentd.
Fine-tuning Dataset:
- The model was fine-tuned using a dataset comprised of posts and top-voted answers from the subreddit r/AskEconomics. This dataset covers a range of economic and financial topics, including:
  - Macroeconomics
  - Microeconomics
  - Investing
  - Personal Finance
  - Career Advice (related to economics)
Intended Use Cases: This model is best suited for:
- Generating informative responses to questions about economic principles.
- Offering potential solutions to personal finance dilemmas.
- Providing explanations of economic concepts and events.
- Assisting with understanding economic discussions and debates.
Limitations:
- Not a Financial Advisor: This model is intended for informational purposes only and should not be considered a substitute for professional financial advice. Always consult with a qualified financial advisor before making any financial decisions.
- Bias and Subjectivity: The training data from r/AskEconomics may reflect the biases and subjective opinions of the community members. The model's responses may therefore contain similar biases. Critically evaluate the information provided and consider seeking diverse perspectives.
- Factual Accuracy: While the model strives to provide accurate information, it is not guaranteed to be error-free. Always verify information from reputable sources before making decisions based on the model's output.
- Limited Scope: The model's knowledge is limited to the information present in the r/AskEconomics dataset. It may not be able to answer questions on highly specialized or niche economic topics.
Ethical Considerations:
- Misinformation: The potential for the model to generate misleading or incorrect information underscores the importance of verifying its output.
- Financial Responsibility: Users should be aware that relying solely on the model's advice for financial decisions can lead to negative consequences.

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Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for nguyentd/FinancialAdvice-Qwen2.5-7B.gguf

Base model

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Finetuned

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Finetuned

nguyentd/FinancialAdvice-Qwen2.5-7B

Quantized

(2)

this model