Instructions to use giangkh19/Qwen3.5-4B-Financial-SQL-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 giangkh19/Qwen3.5-4B-Financial-SQL-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 giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16

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 giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16

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 giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16

Use Docker

docker model run hf.co/giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16

LM Studio
Jan

vLLM

How to use giangkh19/Qwen3.5-4B-Financial-SQL-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "giangkh19/Qwen3.5-4B-Financial-SQL-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": "giangkh19/Qwen3.5-4B-Financial-SQL-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16

Ollama
How to use giangkh19/Qwen3.5-4B-Financial-SQL-GGUF with Ollama:
```
ollama run hf.co/giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16
```
Unsloth Desktop

How to use giangkh19/Qwen3.5-4B-Financial-SQL-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16

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": "giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use giangkh19/Qwen3.5-4B-Financial-SQL-GGUF with Docker Model Runner:
```
docker model run hf.co/giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16
```

Lemonade

How to use giangkh19/Qwen3.5-4B-Financial-SQL-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16

Run and chat with the model

lemonade run user.Qwen3.5-4B-Financial-SQL-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use giangkh19/Qwen3.5-4B-Financial-SQL-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 giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16

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 giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use giangkh19/Qwen3.5-4B-Financial-SQL-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16

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 "giangkh19/Qwen3.5-4B-Financial-SQL-GGUF:BF16" \
  --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"

🦙 Qwen3.5-4B-Financial-SQL (GGUF Quantized for Ollama)

Phiên bản lượng tử hóa định dạng GGUF (Q4_K_M) tối ưu dung lượng và tốc độ, sẵn sàng chạy mượt mà trên laptop, PC (kể cả máy không có card đồ họa rời) thông qua Ollama hoặc llama.cpp.

Dung lượng: ~2.5 GB
VRAM/RAM yêu cầu: >= 4 GB RAM

🚀 Hướng dẫn chạy 1-Click với Ollama

Bước 1: Tải file GGUF

Tải file qwen3_5_4b_financial_sql.Q4_K_M.gguf từ mục Files and versions của repo này về máy.

Bước 2: Tạo file `Modelfile`

Tạo một file văn bản đặt tên là Modelfile cùng thư mục với file .gguf vừa tải, nội dung:

FROM ./qwen3_5_4b_financial_sql.Q4_K_M.gguf

TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""

SYSTEM """You are a financial SQLite expert for Vietnamese corporate financial reports.
Given the database schema for table `financial_facts`, analyze the question and return:
1. A concise reasoning block enclosed in <think>...</think>.
2. The exact ANSI SQLite query inside ```sql ... ```."""

PARAMETER temperature 0.01
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"

Bước 3: Tạo và chạy Model trong Ollama

Mở terminal và gõ:

ollama create qwen-sql -f Modelfile
ollama run qwen-sql "Doanh thu năm 2023 của FPT đạt bao nhiêu tỷ đồng?"

💻 Chạy với `llama.cpp` (CLI)

./llama-cli -m qwen3_5_4b_financial_sql.Q4_K_M.gguf \
  -p "<|im_start|>user\nLợi nhuận sau thuế năm 2023 của Hòa Phát (HPG) là bao nhiêu tỷ?<|im_end|>\n<|im_start|>assistant\n" \
  -n 512 --temp 0.0

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GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

🦙 Qwen3.5-4B-Financial-SQL (GGUF Quantized for Ollama)

🚀 Hướng dẫn chạy 1-Click với Ollama

Bước 1: Tải file GGUF

Bước 2: Tạo file Modelfile

Bước 3: Tạo và chạy Model trong Ollama

💻 Chạy với llama.cpp (CLI)

Bước 2: Tạo file `Modelfile`

💻 Chạy với `llama.cpp` (CLI)