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 Settings
- 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
Incomplete response using API
Hi numberstation team.
Im using the HF API with the following input data (data = query({"inputs": text, "wait_for_model" : True}, model_id, api_token)):
CREATE TABLE stadium ( stadium_id number, location text, name text, capacity number, highest number, lowest number, average number )
CREATE TABLE singer ( singer_id number, name text, country text, song_name text, song_release_year text, age number, is_male others )
CREATE TABLE concert ( concert_id number, concert_name text, theme text, stadium_id text, year text )
CREATE TABLE singer_in_concert ( concert_id number, singer_id text )
-- Using valid SQLite, answer the following questions for the tables provided above.
-- What is the maximum, the average, and the minimum capacity of stadiums ?
SELECT
and getting the following response:
CREATE TABLE stadium ( stadium_id number, location text, name text, capacity number, highest number, lowest number, average number )
CREATE TABLE singer ( singer_id number, name text, country text, song_name text, song_release_year text, age number, is_male others )
CREATE TABLE concert ( concert_id number, concert_name text, theme text, stadium_id text, year text )
CREATE TABLE singer_in_concert ( concert_id number, singer_id text )
-- Using valid SQLite, answer the following questions for the tables provided above.
-- What is the maximum, the average, and the minimum capacity of stadiums ?
SELECT MAX
do i need to add more parameters?
Thanks