LFM2.5-1.2B-Text2SQL

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "hybridaione/LFM2.5-1.2B-Text2SQL",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("furukama/LFM2.5-1.2B-Text2SQL", trust_remote_code=True)

# Example query
prompt = '''CREATE TABLE employees (id INT, name VARCHAR, salary DECIMAL);

Question: What are the names of employees earning more than 50000?'''

messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))