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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("defog/sqlcoder-7b-2")
model = AutoModelForCausalLM.from_pretrained("defog/sqlcoder-7b-2")

llama-cpp-python

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="defog/sqlcoder-7b-2",
	filename="sqlcoder-7b-q5_k_m.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use defog/sqlcoder-7b-2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf defog/sqlcoder-7b-2:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf defog/sqlcoder-7b-2:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf defog/sqlcoder-7b-2:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf defog/sqlcoder-7b-2:Q5_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 defog/sqlcoder-7b-2:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf defog/sqlcoder-7b-2:Q5_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 defog/sqlcoder-7b-2:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf defog/sqlcoder-7b-2:Q5_K_M

Use Docker

docker model run hf.co/defog/sqlcoder-7b-2:Q5_K_M

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/defog/sqlcoder-7b-2:Q5_K_M

SGLang

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

Ollama
How to use defog/sqlcoder-7b-2 with Ollama:
```
ollama run hf.co/defog/sqlcoder-7b-2:Q5_K_M
```

Unsloth Studio new

How to use defog/sqlcoder-7b-2 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 defog/sqlcoder-7b-2 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 defog/sqlcoder-7b-2 to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use defog/sqlcoder-7b-2 with Docker Model Runner:
```
docker model run hf.co/defog/sqlcoder-7b-2:Q5_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull defog/sqlcoder-7b-2:Q5_K_M

Run and chat with the model

lemonade run user.sqlcoder-7b-2-Q5_K_M

List all available models

lemonade list

Is using the database schema the best way to describe prime the model?

by moltar - opened Feb 7, 2024

Discussion

moltar

Feb 7, 2024

I have a use case where there's no DDL, just a description of the tables, with provided data types, and descriptions of all of the columns, in JSON.

I could generate DDL statements, of course. But wondering if this is the best way to hint at the model of the database structure.

Also, how would you go about supplying the very verbose descriptions of all of the data types? Would SQL comments be best? Postgres-style column comments?

Thanks!

rishdotblog

Defog.ai org Feb 7, 2024

The model is optimized for DDL statements – though YMMV with JSON descriptions!

And yes, SQL comments for column description work best! Example here! Also copying in full below. The join hints are not strictly required, but do improve performance if you have a very complex table.

CREATE TABLE products (
  product_id INTEGER PRIMARY KEY, -- Unique ID for each product
  name VARCHAR(50), -- Name of the product
  price DECIMAL(10,2), -- Price of each unit of the product
  quantity INTEGER  -- Current quantity in stock
);

CREATE TABLE customers (
   customer_id INTEGER PRIMARY KEY, -- Unique ID for each customer
   name VARCHAR(50), -- Name of the customer
   address VARCHAR(100) -- Mailing address of the customer
);

CREATE TABLE salespeople (
  salesperson_id INTEGER PRIMARY KEY, -- Unique ID for each salesperson 
  name VARCHAR(50), -- Name of the salesperson
  region VARCHAR(50) -- Geographic sales region 
);

CREATE TABLE sales (
  sale_id INTEGER PRIMARY KEY, -- Unique ID for each sale
  product_id INTEGER, -- ID of product sold
  customer_id INTEGER,  -- ID of customer who made purchase
  salesperson_id INTEGER, -- ID of salesperson who made the sale
  sale_date DATE, -- Date the sale occurred 
  quantity INTEGER -- Quantity of product sold
);

CREATE TABLE product_suppliers (
  supplier_id INTEGER PRIMARY KEY, -- Unique ID for each supplier
  product_id INTEGER, -- Product ID supplied
  supply_price DECIMAL(10,2) -- Unit price charged by supplier
);

-- sales.product_id can be joined with products.product_id
-- sales.customer_id can be joined with customers.customer_id 
-- sales.salesperson_id can be joined with salespeople.salesperson_id
-- product_suppliers.product_id can be joined with products.product_id

rishdotblog changed discussion status to closed Feb 13, 2024

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