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