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--- |
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license: apache-2.0 |
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--- |
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# Description |
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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. |
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# Usage |
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## Huggingface Transformers Library |
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```py |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_name = 'unSQLv1-7b-generic-lora' |
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device = 'cuda' |
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model = AutoModelForCausalLM.from_pretrained(model_name).to(device) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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example_prompt = ''' |
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### Schema and the Natural Language Query: |
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CREATE TABLE stadium ( |
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stadium_id number, |
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location text, |
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name text, |
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capacity number, |
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highest number, |
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lowest number, |
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average number |
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) |
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CREATE TABLE singer ( |
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singer_id number, |
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name text, |
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country text, |
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song_name text, |
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song_release_year text, |
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age number, |
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is_male others |
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) |
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CREATE TABLE concert ( |
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concert_id number, |
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concert_name text, |
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theme text, |
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stadium_id text, |
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year text |
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) |
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CREATE TABLE singer_in_concert ( |
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concert_id number, |
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singer_id text |
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) |
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-- Using valid SQLite, answer the following questions for the tables provided above. |
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-- What is the maximum, the average, and the minimum capacity of stadiums ? |
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''' |
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inputs = tokenizer.encode(example_prompt, return_tensors="pt").to(device) |
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outputs = model.generate(inputs, max_length=512) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Sagemaker Endpoint I/O Example |
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```py |
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payload = { |
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"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 ?", |
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"parameters": { |
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"maxNewTokens": 512, |
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"topP": 0.9, |
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"temperature": 0.2 |
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} |
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} |
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client = boto3.client('runtime.sagemaker') |
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endpoint_name = 'deployed_model_name' |
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response = client.invoke_endpoint( |
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EndpointName=endpoint_name, |
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ContentType='application/json', |
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Body=json.dumps(payload).encode('utf-8'), |
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) |
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response = response["Body"].read().decode("utf8") |
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response = json.loads(response) |
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print(response[0]['generated_text']) |
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``` |
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```json |
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{ |
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"body": [ |
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{ |
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"generated_text": "\n\n\n### Response:\nSELECT MAX(capacity), AVG(capacity), MIN(capacity) FROM stadium", |
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"details": { |
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"finish_reason": "eos_token", |
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"generated_tokens": 30, |
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"seed": 14524408611356330000, |
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"prefill": [], |
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"tokens": [ |
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{ |
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"id": 13, |
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"text": "\n", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 13, |
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"text": "\n", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 13, |
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"text": "\n", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 2277, |
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"text": "##", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 29937, |
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"text": "#", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 13291, |
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"text": " Response", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 29901, |
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"text": ":", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 13, |
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"text": "\n", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 6404, |
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"text": "SELECT", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 18134, |
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"text": " MAX", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 29898, |
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"text": "(", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 5030, |
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"text": "cap", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 5946, |
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"text": "acity", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 511, |
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"text": "),", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 16884, |
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"text": " AV", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 29954, |
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"text": "G", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 29898, |
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"text": "(", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 5030, |
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"text": "cap", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 5946, |
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"text": "acity", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 511, |
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"text": "),", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 341, |
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"text": " M", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 1177, |
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"text": "IN", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 29898, |
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"text": "(", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 5030, |
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"text": "cap", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 5946, |
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"text": "acity", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 29897, |
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"text": ")", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 3895, |
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"text": " FROM", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 10728, |
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"text": " stad", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 1974, |
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"text": "ium", |
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"logprob": 0, |
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"special": false |
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}, |
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{ |
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"id": 2, |
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"text": "</s>", |
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"logprob": 0, |
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"special": true |
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} |
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] |
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} |
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} |
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], |
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"contentType": "application/json", |
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"invokedProductionVariant": "AllTraffic" |
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} |
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``` |
