📖 Introduction
Qwen2-7B-Instruct-Exp and Qwen2-1.5B-Instruct-Exp are powerful large language models that can expand instructions with same task type but of different content.
We fine-tuned Qwen2-7B-Instruct and Qwen2-1.5B-Instruct-Exp to obtain Qwen2-7B-Instruct-Exp and Qwen2-1.5B-Instruct-Exp. We sampled the dataset from OpenHermes and the LCCD dataset, ensuring a balanced task distribution. For training set annotations, we used Qwen-max with incorporated our handwritten examples as in-context prompts.
Example Input
Plan an in depth tour itinerary of France that includes Paris, Lyon, and Provence.
Example Output 1
Describe a classic road trip itinerary along the California coastline in the United States.
Example Output 2
Create a holiday plan that combines cultural experiences in Bangkok, Thailand, with beach relaxation in Phuket.
🚀 Quick Start
Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"alibaba-pai/Qwen2-1.5B-Instruct-Exp",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("alibaba-pai/Qwen2-1.5B-Instruct-Exp")
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=2048，
eos_token_id=151645，
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
🔍 Evaluation
We evaluated the data augmentation effect of our model on the Elementary Math and Implicature datasets.
| Model | Math | Impl. |
|---|---|---|
| Qwen2-1.5B-Instruct | 57.90% | 28.96% |
| + Qwen2-1.5B-Instruct-Exp | 59.15% | 31.22% |
| + Qwen2-7B-Instruct-Exp | 58.32% | 39.37% |
| Qwen2-7B-Instruct | 71.40% | 28.85% |
| + Qwen2-1.5B-Instruct-Exp | 73.90% | 35.41% |
| + Qwen2-7B-Instruct-Exp | 72.53% | 32.92% |
📜 Citation
If you find our work helpful, please cite it!
@misc{data-augmentation-family,
title={Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud},
author={Yuanhao Yue and Chengyu Wang and Jun Huang and Peng Wang},
year={2024},
eprint={2412.04871},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.04871},
}