Instructions to use PipableAI/pip-SQL-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PipableAI/pip-SQL-1B with Transformers:

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

pipe = pipeline("text-generation", model="PipableAI/pip-SQL-1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-SQL-1B")
model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-SQL-1B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PipableAI/pip-SQL-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PipableAI/pip-SQL-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PipableAI/pip-SQL-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PipableAI/pip-SQL-1B

SGLang

How to use PipableAI/pip-SQL-1B 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-1B" \
    --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": "PipableAI/pip-SQL-1B",
		"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 "PipableAI/pip-SQL-1B" \
        --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": "PipableAI/pip-SQL-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PipableAI/pip-SQL-1B with Docker Model Runner:
```
docker model run hf.co/PipableAI/pip-SQL-1B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Pipable’s pipSQL

Please refer to https://huggingface.co/PipableAI/pipSQL-1.3b for our state of the art model, that gives better performance than chatgpt and claude on sql tasks on a lot of benchmarks.

Pipable’s pipSQL is a model distilled from llama 1b to generate sql queries given prompt and schema. We used a unique pipeline which involved the model working on two objectives alternatively ----

Maximizing the log prob of all tokens in the sequence (including the prompt tokens)
Minimizng the difference between the true value and the predicted maximum value of the output tokens i.e generated tokens for the sql query slice of the entire sequence.

License

The model's new weights along with all other assets involved with it are open sourced under mit license.

How to Use

text = """<schema>{schema}</schema>
<question>{question}</question>
<sql>"""

pytorch

from transformers import AutoModelForCasualLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained("PipableAI/pipSQL1b")
tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL1b")

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])

flax

from transformers import FlaxAutoModelForCasualLM, AutoTokenizer
model = FlaxAutoModelForCausalLM.from_pretrained("PipableAI/pipSQL1b" , from_pt=True)
tokenizer = AutoTokenizer.from_pretrained("PipableAI/pipSQL1b")

The PipableAI team

Avi Kothari, Pratham Gupta, Ritvik Aryan Kalra, Rohan Bhatial, Soham Acharya

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Safetensors

Model size

1B params

Tensor type

F32

Model tree for PipableAI/pip-SQL-1B

Quantizations

1 model

PipableAI
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pip-SQL-1B

Pipable’s pipSQL

License

How to Use

The PipableAI team

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