Instructions to use duyntnet/sqlcoder-7b-2-imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use duyntnet/sqlcoder-7b-2-imatrix-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="duyntnet/sqlcoder-7b-2-imatrix-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("duyntnet/sqlcoder-7b-2-imatrix-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use duyntnet/sqlcoder-7b-2-imatrix-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 duyntnet/sqlcoder-7b-2-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf duyntnet/sqlcoder-7b-2-imatrix-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 duyntnet/sqlcoder-7b-2-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf duyntnet/sqlcoder-7b-2-imatrix-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf duyntnet/sqlcoder-7b-2-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf duyntnet/sqlcoder-7b-2-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/duyntnet/sqlcoder-7b-2-imatrix-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use duyntnet/sqlcoder-7b-2-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "duyntnet/sqlcoder-7b-2-imatrix-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/sqlcoder-7b-2-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/duyntnet/sqlcoder-7b-2-imatrix-GGUF:Q4_K_M

SGLang

How to use duyntnet/sqlcoder-7b-2-imatrix-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "duyntnet/sqlcoder-7b-2-imatrix-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/sqlcoder-7b-2-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "duyntnet/sqlcoder-7b-2-imatrix-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/sqlcoder-7b-2-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use duyntnet/sqlcoder-7b-2-imatrix-GGUF with Ollama:
```
ollama run hf.co/duyntnet/sqlcoder-7b-2-imatrix-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use duyntnet/sqlcoder-7b-2-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/duyntnet/sqlcoder-7b-2-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use duyntnet/sqlcoder-7b-2-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull duyntnet/sqlcoder-7b-2-imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.sqlcoder-7b-2-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

sqlcoder-7b-2-imatrix-GGUF / README.md

duyntnet

Upload README.md with huggingface_hub

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---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- sqlcoder-7b-2
---
Quantizations of https://huggingface.co/defog/sqlcoder-7b-2


# From original readme

## Uses

This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.

This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.

## How to Get Started with the Model

Use the code [here](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) to get started with the model.

## Prompt

Please use the following prompt for optimal results. Please remember to use `do_sample=False` and `num_beams=4` for optimal results.

```
### Task
Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION]
### Database Schema
The query will run on a database with the following schema:
{table_metadata_string_DDL_statements}
### Answer
Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION]
[SQL]
```

	---
	license: other
	language:
	- en
	pipeline_tag: text-generation
	inference: false
	tags:
	- transformers
	- gguf
	- imatrix
	- sqlcoder-7b-2
	---
	Quantizations of https://huggingface.co/defog/sqlcoder-7b-2


	# From original readme

	## Uses

	This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.

	This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.

	## How to Get Started with the Model

	Use the code [here](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) to get started with the model.

	## Prompt

	Please use the following prompt for optimal results. Please remember to use `do_sample=False` and `num_beams=4` for optimal results.

	```
	### Task
	Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION]

	### Database Schema
	The query will run on a database with the following schema:
	{table_metadata_string_DDL_statements}

	### Answer
	Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION]
	[SQL]
	```