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alibaba-pai/
Qwen2-7B-Instruct-Exp

Name: Qwen2-7B-Instruct-Exp
Availability: InStock

text-generation-inference

endpoints_compatible

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📖 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}, 
}