--- library_name: transformers tags: - text-to-sql datasets: - kristiannordby/function_call_sql - kristiannordby/claude_question-sql_pairs base_model: - defog/sqlcoder-7b-2 --- # Model Card for Model ID This text-to-sql model was finetuned on army-aviation-specific data. ## Model Details To load the model: ``` # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kristiannordby/llama3-sqlcoder-ft") model = AutoModelForCausalLM.from_pretrained("kristiannordby/llama3-sqlcoder-ft") ``` Prompt: ``` ### Task Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION] ### Database Schema The query will run on a database with the following schema: {table_metadata_string_DDL_statements} ### Answer Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION] [SQL] ``` To prompt the model for generation: ``` def build_prompt(user_question, create_table_statements): return ( "### Task\n" f"Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION]\n\n" "### Database Schema\n" "The query will run on a database with the following schema:\n" f"{create_table_statements}\n\n" "### Answer\n" f"Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION]\n" "[SQL]\n" ) def build_output(sql): # Add a newline at end; if the data has a closing "[/SQL]", add it here! return f"{sql.strip()}\n" create_table_statements = "YOUR TABLE SCHEMA HERE" def sqllamma(question): input_ids = tokenizer(build_prompt(question, create_table_statements), return_tensors="pt", padding = True, truncation = True, max_length = 512).input_ids.to(model.device) outputs = model.generate(input_ids, max_new_tokens=100) output = tokenizer.decode(outputs[0]) sql = output.split("###")[3].split("[SQL]")[1].strip() return sql sqllama("YOUR QUESTION HERE") ``` ### Model Description This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** @kristiannordby - **Funded by [optional]:** AI2C, USMA D/MATH - **Shared by [optional]:** [More Information Needed] - **Model type:** [Text-to-SQL] - **Language(s) (NLP):** English, SQL - **License:** [More Information Needed] - **Finetuned from model [optional]:** Defog/sqlcoder-7b-2 ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses This model was finetuned on an Army Aviation Question-SQL dataset. ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]