Instructions to use omeryentur/llama-3-sqlcoder-8b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use omeryentur/llama-3-sqlcoder-8b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="omeryentur/llama-3-sqlcoder-8b-GGUF",
	filename="llama-3-sqlcoder-8b.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 omeryentur/llama-3-sqlcoder-8b-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 omeryentur/llama-3-sqlcoder-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf omeryentur/llama-3-sqlcoder-8b-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 omeryentur/llama-3-sqlcoder-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf omeryentur/llama-3-sqlcoder-8b-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 omeryentur/llama-3-sqlcoder-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf omeryentur/llama-3-sqlcoder-8b-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 omeryentur/llama-3-sqlcoder-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf omeryentur/llama-3-sqlcoder-8b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/omeryentur/llama-3-sqlcoder-8b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use omeryentur/llama-3-sqlcoder-8b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "omeryentur/llama-3-sqlcoder-8b-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": "omeryentur/llama-3-sqlcoder-8b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/omeryentur/llama-3-sqlcoder-8b-GGUF:Q4_K_M

Ollama
How to use omeryentur/llama-3-sqlcoder-8b-GGUF with Ollama:
```
ollama run hf.co/omeryentur/llama-3-sqlcoder-8b-GGUF:Q4_K_M
```

Unsloth Studio

How to use omeryentur/llama-3-sqlcoder-8b-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 omeryentur/llama-3-sqlcoder-8b-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 omeryentur/llama-3-sqlcoder-8b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for omeryentur/llama-3-sqlcoder-8b-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use omeryentur/llama-3-sqlcoder-8b-GGUF with Docker Model Runner:
```
docker model run hf.co/omeryentur/llama-3-sqlcoder-8b-GGUF:Q4_K_M
```

Lemonade

How to use omeryentur/llama-3-sqlcoder-8b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull omeryentur/llama-3-sqlcoder-8b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama-3-sqlcoder-8b-GGUF-Q4_K_M

List all available models

lemonade list

llama-3-sqlcoder-8b-GGUF / README.md

omeryentur

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	---
	license: cc-by-sa-4.0
	language:
	- en
	library_name: gguf
	pipeline_tag: text-generation
	base_model: defog/llama-3-sqlcoder-8b
	tags:
	- text-to-sql
	- sql
	- gguf
	- llama-3
	- quantized
	- llama-cpp
	- code-generation
	---

	# llama-3-sqlcoder-8b — GGUF

	GGUF (llama.cpp) build of [`defog/llama-3-sqlcoder-8b`](https://huggingface.co/defog/llama-3-sqlcoder-8b),
	a Llama-3 8B model fine-tuned for text-to-SQL generation. This repo packages a
	Q4_K_M quantization so the model runs efficiently on CPU/GPU through
	[llama.cpp](https://github.com/ggerganov/llama.cpp), Ollama, LM Studio, and other
	GGUF-compatible runtimes.

	## Files

	\| File \| Quant \| Approx. size \| Notes \|
	\|------\|-------\|--------------\|-------\|
	\| `llama-3-sqlcoder-8b.Q4_K_M.gguf` \| Q4_K_M \| ~4.9 GB \| 4-bit, good quality/size trade-off \|

	## Usage

	llama.cpp

	```bash
	./llama-cli -m llama-3-sqlcoder-8b.Q4_K_M.gguf -p "Generate a SQL query to answer the question."
	```

	Ollama (Modelfile)

	```
	FROM ./llama-3-sqlcoder-8b.Q4_K_M.gguf
	```

	Python (llama-cpp-python)

	```python
	from llama_cpp import Llama

	llm = Llama.from_pretrained(
	repo_id="omeryentur/llama-3-sqlcoder-8b-GGUF",
	filename="llama-3-sqlcoder-8b.Q4_K_M.gguf",
	)
	out = llm("### Task\nGenerate a SQL query to answer the question.\n### Question\nHow many users signed up in 2024?\n### SQL\n")
	print(out["choices"][0]["text"])
	```

	Use the prompt format expected by the base `defog/llama-3-sqlcoder-8b` model for best results.

	## Credits

	- Base model: [defog/llama-3-sqlcoder-8b](https://huggingface.co/defog/llama-3-sqlcoder-8b)
	- Quantization tooling: [llama.cpp](https://github.com/ggerganov/llama.cpp)

	See more text-to-SQL work on this profile, including the
	[Text-to-PostgreSQL dataset](https://huggingface.co/datasets/omeryentur/text-to-postgresql).