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")

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

llama-cpp-python

How to use duyntnet/sqlcoder-7b-2-imatrix-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="duyntnet/sqlcoder-7b-2-imatrix-GGUF",
	filename="sqlcoder-7b-2-IQ1_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use duyntnet/sqlcoder-7b-2-imatrix-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -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-server -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

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 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/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 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

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 Studio

How to use duyntnet/sqlcoder-7b-2-imatrix-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 duyntnet/sqlcoder-7b-2-imatrix-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 duyntnet/sqlcoder-7b-2-imatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for duyntnet/sqlcoder-7b-2-imatrix-GGUF to start chatting

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

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 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]

Downloads last month: 28

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit