Instructions to use PipableAI/pip-sql-1.3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PipableAI/pip-sql-1.3b with Transformers:

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

pipe = pipeline("text-generation", model="PipableAI/pip-sql-1.3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-sql-1.3b")
model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-sql-1.3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PipableAI/pip-sql-1.3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PipableAI/pip-sql-1.3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PipableAI/pip-sql-1.3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PipableAI/pip-sql-1.3b

SGLang

How to use PipableAI/pip-sql-1.3b 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 "PipableAI/pip-sql-1.3b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PipableAI/pip-sql-1.3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "PipableAI/pip-sql-1.3b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PipableAI/pip-sql-1.3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PipableAI/pip-sql-1.3b with Docker Model Runner:
```
docker model run hf.co/PipableAI/pip-sql-1.3b
```

rvk7895 commited on Feb 15, 2024

Commit

620a28f

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Update README.md

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README.md CHANGED Viewed

@@ -109,4 +109,81 @@ tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL-1.3b")
 inputs = tokenizer(text, return_tensors="pt")
 outputs = model.generate(**inputs, max_new_tokens=200)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
 ```

 inputs = tokenizer(text, return_tensors="pt")
 outputs = model.generate(**inputs, max_new_tokens=200)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
+```
+## Examples
+### Schema
+```sql
+CREATE TABLE Products (
+  product_id number,
+  parent_product_id number,
+  product_name text,
+  product_price number,
+  product_color text,
+  product_size text,
+  product_description text);
+CREATE TABLE Customers (
+  customer_id number,
+  gender_code text,
+  customer_first_name text,
+  customer_middle_initial text,
+  customer_last_name text,
+  email_address text,
+  login_name text,
+  login_password text,
+  phone_number text,
+  address_line_1 text,
+  town_city text,
+  county text,
+  country text);
+CREATE TABLE Customer_Payment_Methods (
+  customer_id number,
+  payment_method_code text);
+CREATE TABLE Invoices (
+  invoice_number number,
+  invoice_status_code text,
+  invoice_date time);
+CREATE TABLE Orders (
+  order_id number,
+  customer_id number,
+  order_status_code text,
+  date_order_placed time);
+CREATE TABLE Order_Items (
+  order_item_id number,
+  product_id number,
+  order_id number,
+  order_item_status_code text);
+CREATE TABLE Shipments (
+  shipment_id number,
+  order_id number,
+  invoice_number number,
+  shipment_tracking_number text,
+  shipment_date time);
+CREATE TABLE Shipment_Items (
+  shipment_id number,
+  order_item_id number);
+```
+### Questions
+What is the most popular payment method?
+```sql
+SELECT payment_method_code FROM Customer_Payment_Methods GROUP BY payment_method_code ORDER BY count(*) DESC LIMIT 1;
+```
+What are the product price and the product size of the products whose price is above average?
+```sql
+SELECT product_price ,  product_size FROM products WHERE product_price  > (SELECT avg(product_price) FROM products)
+```
+What is the most uncommon order status?
+```sql
+SELECT order_status_code FROM orders GROUP BY order_status_code ORDER BY count(*) ASC LIMIT 1;
 ```