82 lines
2.9 KiB
Markdown
82 lines
2.9 KiB
Markdown
|
## 📖 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.
|
|||
|
|
|
|||
|
|
```python
|
|||
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
|||
|
|
device = "cuda" # the device to load the model onto
|
|||
|
|
|
|||
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|||
|
|
"alibaba-pai/Qwen2-7B-Instruct-Exp",
|
|||
|
|
torch_dtype="auto",
|
|||
|
|
device_map="auto"
|
|||
|
|
)
|
|||
|
|
tokenizer = AutoTokenizer.from_pretrained("alibaba-pai/Qwen2-7B-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},
|
|||
|
|
}
|
|||
|
|
```
|