Qwen2.5 7B Data-to-Text

Published by @tabular_talker · Community adapter

Converts structured data (JSON tables, CSV rows, database query results) into natural language narratives. Strong on KPI summaries, sports result reports, and financial data commentary.

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base, "modelforgelab/qwen25-7b-data-to-text-lora")

inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Compatibility

• transformers>=4.40
• vLLM