Instructions to use build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf",
	filename="lfed-qwen2.5-coder-7b-sql-Q4_K_M.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 build-small-hackathon/lfed-qwen2.5-coder-7b-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 build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf build-small-hackathon/lfed-qwen2.5-coder-7b-sql-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 build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf build-small-hackathon/lfed-qwen2.5-coder-7b-sql-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 build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf build-small-hackathon/lfed-qwen2.5-coder-7b-sql-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 build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M

Use Docker

docker model run hf.co/build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "build-small-hackathon/lfed-qwen2.5-coder-7b-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": "build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M

Ollama
How to use build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf with Ollama:
```
ollama run hf.co/build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M
```

Unsloth Studio

How to use build-small-hackathon/lfed-qwen2.5-coder-7b-sql-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 build-small-hackathon/lfed-qwen2.5-coder-7b-sql-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 build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf to start chatting

How to use build-small-hackathon/lfed-qwen2.5-coder-7b-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 build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M

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": "build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use build-small-hackathon/lfed-qwen2.5-coder-7b-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 build-small-hackathon/lfed-qwen2.5-coder-7b-sql-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 build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use build-small-hackathon/lfed-qwen2.5-coder-7b-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 build-small-hackathon/lfed-qwen2.5-coder-7b-sql-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 "build-small-hackathon/lfed-qwen2.5-coder-7b-sql-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"

Docker Model Runner
How to use build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf with Docker Model Runner:
```
docker model run hf.co/build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M
```

Lemonade

How to use build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull build-small-hackathon/lfed-qwen2.5-coder-7b-sql-gguf:Q4_K_M

Run and chat with the model

lemonade run user.lfed-qwen2.5-coder-7b-sql-gguf-Q4_K_M

List all available models

lemonade list

LFED — Qwen2.5-Coder-7B Text-to-SQL (GGUF)

Fine-tuned on Q4_K_M for duckdb SQL generation from natural-language questions about school district data (enrollment, attendance, chronic absenteeism).

Base model: Qwen2.5-Coder-7B-Instruct Fine-tuning: Unsloth QLoRA (r=16, alpha=16) on ~~1,200 synthetic NL→SQL pairs Format: GGUF Q4_K_M (~~4.4 GB) Use with: llama.cpp, Ollama, LM Studio

Usage

from llama_cpp import Llama

llm = Llama(
    model_path="lfed-qwen2.5-coder-7b-sql-Q4_K_M.gguf",
    n_ctx=4096,
)

Schema

enrollment(school_year, school_name, grade_level, student_count)
attendance(student_id, school_name, school_year, absence_count, is_chronically_absent)

Downloads last month: 356

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

16-bit