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Model: pai/Qwen2-7B-Instruct-Exp
Source: Original Platform

README.md

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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}, 
+}
+```