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/Qwen2-1.5B-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},
|
||
}
|
||
``` |