Instructions to use NumbersStation/nsql-350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NumbersStation/nsql-350M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NumbersStation/nsql-350M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NumbersStation/nsql-350M") model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-350M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use NumbersStation/nsql-350M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NumbersStation/nsql-350M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NumbersStation/nsql-350M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/NumbersStation/nsql-350M
- SGLang
How to use NumbersStation/nsql-350M 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 "NumbersStation/nsql-350M" \ --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": "NumbersStation/nsql-350M", "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 "NumbersStation/nsql-350M" \ --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": "NumbersStation/nsql-350M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use NumbersStation/nsql-350M with Docker Model Runner:
docker model run hf.co/NumbersStation/nsql-350M
Accuracy on Spider
I wrote code to generate predictions for the 1000 questions in here;
https://github.com/taoyds/spider/blob/master/evaluation_examples/dev.sql
Then I use the following Python file to evaluate execution accuracy.
https://github.com/taoyds/spider/blob/master/evaluation.py
NSQL-350m gets 22% execution accuracy on easy questions which is quite a bit lower than the reported 51% accuracy on Spider reported in the blog post below.
https://www.numbersstation.ai/post/introducing-nsql-open-source-sql-copilot-foundation-models
What is the reason for this discrepancy?