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iamkushagratomar
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Model Details
Model Provider
iamkushagratomar
Model Tree
unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit
Input Modalities
Output Modalities
Supported Functionality
bash
pip install transformers torchbash
from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name = "iamkushagratomar/Qwen2.5-0.5B-text2sql" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")bash
def generate_sql(question, schema): # Format input as: "System: [message]\n\nUser: [question + schema]" system_message = "You are a SQL expert. Convert natural language questions into SQL queries." user_message = f"### Question: \n{question}\n\n### Schema: \n{schema}" full_input = f"System: {system_message}\n\nUser: {user_message}" inputs = tokenizer(full_input, return_tensors="pt").to(model.device) outputs = model.generate( input_ids=inputs.input_ids, max_new_tokens=100, temperature=0.2, do_sample=True, use_cache=True ) return tokenizer.decode(outputs[0], skip_special_tokens=True) # Example usagequestion = "Find the total revenue from orders placed in the last 30 days."schema = "CREATE TABLE orders (order_id INT, order_date DATE, order_total DECIMAL, customer_id INT);"sql_query = generate_sql(question, schema)print(sql_query)This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
def generate_sql(question, schema): # Format input as: "System: [message]\n\nUser: [question + schema]" system_message = "You are a SQL expert. Convert natural language questions into SQL queries." user_message = f"### Question: \n{question}\n\n### Schema: \n{schema}" full_input = f"System: {system_message}\n\nUser: {user_message}" inputs = tokenizer(full_input, return_tensors="pt").to(model.device) outputs = model.generate( input_ids=inputs.input_ids, max_new_tokens=100, temperature=0.2, do_sample=True, use_cache=True ) return tokenizer.decode(outputs[0], skip_special_tokens=True) # Example usagequestion = "Find the total revenue from orders placed in the last 30 days."schema = "CREATE TABLE orders (order_id INT, order_date DATE, order_total DECIMAL, customer_id INT);"sql_query = generate_sql(question, schema)print(sql_query)