How to use from
vLLMUse Docker
docker model run hf.co/unsqldotai/unSQLv1-7b-sqlite-loraQuick Links
Description
This is a LoRA-finetuned codellama/CodeLlama-7b-hf text2SQL model that generates SQLite queries. This is a relatively small model that was fine-tuned on 8 x A10Gs with a total GPU memory of 192GB for over 4 days for 3 epochs. For databases with different SQL syntaxes that do not adhere to SQLite's syntax, we plan to launch other models specifically catered to them.
Usage
Huggingface Transformers Library
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = 'unSQLv1-7b-generic-lora'
device = 'cuda'
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name)
example_prompt = '''
### Schema and the Natural Language Query:
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 ?
'''
inputs = tokenizer.encode(example_prompt, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Sagemaker Endpoint I/O Example
payload = {
"inputs": "### Schema and the Natural Language Query:\nCREATE TABLE stadium (\n stadium_id number,\n location text,\n name text,\n capacity number,\n highest number,\n lowest number,\n average number\n)\n\nCREATE TABLE singer (\n singer_id number,\n name text,\n country text,\n song_name text,\n song_release_year text,\n age number,\n is_male others\n)\n\nCREATE TABLE concert (\n concert_id number,\n concert_name text,\n theme text,\n stadium_id text,\n year text\n)\n\nCREATE TABLE singer_in_concert (\n concert_id number,\n singer_id text\n)\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- What is the maximum, the average, and the minimum capacity of stadiums ?",
"parameters": {
"maxNewTokens": 512,
"topP": 0.9,
"temperature": 0.2
}
}
client = boto3.client('runtime.sagemaker')
endpoint_name = 'deployed_model_name'
response = client.invoke_endpoint(
EndpointName=endpoint_name,
ContentType='application/json',
Body=json.dumps(payload).encode('utf-8'),
)
response = response["Body"].read().decode("utf8")
response = json.loads(response)
print(response[0]['generated_text'])
{
"body": [
{
"generated_text": "\n\n\n### Response:\nSELECT MAX(capacity), AVG(capacity), MIN(capacity) FROM stadium",
"details": {
"finish_reason": "eos_token",
"generated_tokens": 30,
"seed": 14524408611356330000,
"prefill": [],
"tokens": [
{
"id": 13,
"text": "\n",
"logprob": 0,
"special": false
},
{
"id": 13,
"text": "\n",
"logprob": 0,
"special": false
},
{
"id": 13,
"text": "\n",
"logprob": 0,
"special": false
},
{
"id": 2277,
"text": "##",
"logprob": 0,
"special": false
},
{
"id": 29937,
"text": "#",
"logprob": 0,
"special": false
},
{
"id": 13291,
"text": " Response",
"logprob": 0,
"special": false
},
{
"id": 29901,
"text": ":",
"logprob": 0,
"special": false
},
{
"id": 13,
"text": "\n",
"logprob": 0,
"special": false
},
{
"id": 6404,
"text": "SELECT",
"logprob": 0,
"special": false
},
{
"id": 18134,
"text": " MAX",
"logprob": 0,
"special": false
},
{
"id": 29898,
"text": "(",
"logprob": 0,
"special": false
},
{
"id": 5030,
"text": "cap",
"logprob": 0,
"special": false
},
{
"id": 5946,
"text": "acity",
"logprob": 0,
"special": false
},
{
"id": 511,
"text": "),",
"logprob": 0,
"special": false
},
{
"id": 16884,
"text": " AV",
"logprob": 0,
"special": false
},
{
"id": 29954,
"text": "G",
"logprob": 0,
"special": false
},
{
"id": 29898,
"text": "(",
"logprob": 0,
"special": false
},
{
"id": 5030,
"text": "cap",
"logprob": 0,
"special": false
},
{
"id": 5946,
"text": "acity",
"logprob": 0,
"special": false
},
{
"id": 511,
"text": "),",
"logprob": 0,
"special": false
},
{
"id": 341,
"text": " M",
"logprob": 0,
"special": false
},
{
"id": 1177,
"text": "IN",
"logprob": 0,
"special": false
},
{
"id": 29898,
"text": "(",
"logprob": 0,
"special": false
},
{
"id": 5030,
"text": "cap",
"logprob": 0,
"special": false
},
{
"id": 5946,
"text": "acity",
"logprob": 0,
"special": false
},
{
"id": 29897,
"text": ")",
"logprob": 0,
"special": false
},
{
"id": 3895,
"text": " FROM",
"logprob": 0,
"special": false
},
{
"id": 10728,
"text": " stad",
"logprob": 0,
"special": false
},
{
"id": 1974,
"text": "ium",
"logprob": 0,
"special": false
},
{
"id": 2,
"text": "</s>",
"logprob": 0,
"special": true
}
]
}
}
],
"contentType": "application/json",
"invokedProductionVariant": "AllTraffic"
}
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Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "unsqldotai/unSQLv1-7b-sqlite-lora"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsqldotai/unSQLv1-7b-sqlite-lora", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'