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

Libraries

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

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

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

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

llama-cpp-python

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/sqlcoder-7b-2-GGUF",
	filename="sqlcoder-7b-2.Q2_K.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 QuantFactory/sqlcoder-7b-2-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 QuantFactory/sqlcoder-7b-2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/sqlcoder-7b-2-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 QuantFactory/sqlcoder-7b-2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/sqlcoder-7b-2-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 QuantFactory/sqlcoder-7b-2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/sqlcoder-7b-2-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 QuantFactory/sqlcoder-7b-2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/sqlcoder-7b-2-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

How to use QuantFactory/sqlcoder-7b-2-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 "QuantFactory/sqlcoder-7b-2-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": "QuantFactory/sqlcoder-7b-2-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 "QuantFactory/sqlcoder-7b-2-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": "QuantFactory/sqlcoder-7b-2-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use QuantFactory/sqlcoder-7b-2-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/sqlcoder-7b-2-GGUF:Q4_K_M
```

Unsloth Studio

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

Using HuggingFace Spaces for Unsloth

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

Atomic Chat new
Docker Model Runner
How to use QuantFactory/sqlcoder-7b-2-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/sqlcoder-7b-2-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

sqlcoder-7b-2-GGUF

File size: 3,470 Bytes

80aacf1

---
license: cc-by-sa-4.0
library_name: transformers
pipeline_tag: text-generation
base_model: defog/sqlcoder-7b-2
---

# QuantFactory/sqlcoder-7b-2-GGUF
This is quantized version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) created using llama.cpp

# Model Card for SQLCoder-7B-2

A capable large language model for natural language to SQL generation.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/603bbad3fd770a9997b57cb6/AYUE2y14vy2XkD9MZpScu.png)

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [Defog, Inc](https://defog.ai)
- **Model type:** [Text to SQL]
- **License:** [CC-by-SA-4.0]
- **Finetuned from model:** [CodeLlama-7B]

### Model Sources [optional]
- [**HuggingFace:**](https://huggingface.co/defog/sqlcoder-70b-alpha)
- [**GitHub:**](https://github.com/defog-ai/sqlcoder)
- [**Demo:**](https://defog.ai/sqlcoder-demo/)

## 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]
```

## Evaluation

This model was evaluated on [SQL-Eval](https://github.com/defog-ai/sql-eval), a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.

You can read more about the methodology behind SQLEval [here](https://defog.ai/blog/open-sourcing-sqleval/).

### Results

We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.

|                | date | group_by | order_by | ratio | join | where |
| -------------- | ---- | -------- | -------- | ----- | ---- | ----- |
| sqlcoder-70b   | 96   | 91.4     | 97.1     | 85.7  | 97.1 | 91.4  |
| sqlcoder-7b-2  | 96   | 91.4     | 94.3     | 91.4  | 94.3 | 77.1  |
| sqlcoder-34b   | 80   | 94.3     | 85.7     | 77.1  | 85.7 | 80    |
| gpt-4          | 72   | 94.3     | 97.1     | 80    | 91.4 | 80    |
| gpt-4-turbo    | 76   | 91.4     | 91.4     | 62.8  | 88.6 | 77.1  |
| natural-sql-7b | 56   | 88.6     | 85.7     | 60    | 88.6 | 80    |
| sqlcoder-7b    | 64   | 82.9     | 74.3     | 54.3  | 74.3 | 74.3  |
| gpt-3.5        | 72   | 77.1     | 82.8     | 34.3  | 65.7 | 71.4  |
| claude-2       | 52   | 71.4     | 74.3     | 57.1  | 65.7 | 62.9  |

## Model Card Contact

Contact us on X at [@defogdata](https://twitter.com/defogdata), or on email at [founders@defog.ai](mailto:founders@defog.ai)