---
base_model:
- Qwen/Qwen2-7B-Instruct
datasets:
- yale-nlp/MDCure-72k
language:
- en
license: apache-2.0
tags:
- multi-document
- long-context
- Long Context
library_name: transformers
pipeline_tag: text-generation
---

# MDCure-Qwen2-7B-Instruct

[📄 Paper](https://arxiv.org/pdf/2410.23463) | [🤗 HF Collection](https://huggingface.co/collections/yale-nlp/mdcure-6724914875e87f41e5445395) | [⚙️ GitHub Repo](https://github.com/yale-nlp/MDCure)

## Introduction

**MDCure** is an effective and scalable procedure for generating high-quality multi-document (MD) instruction tuning data to improve MD capabilities of LLMs. Using MDCure, we construct a suite of MD instruction datasets complementary to collections such as [FLAN](https://github.com/google-research/FLAN) and fine-tune a variety of already instruction-tuned LLMs from the FlanT5, Qwen2, and LLAMA3.1 model families, up to 70B parameters in size. We additionally introduce **MDCureRM**, an evaluator model specifically designed for the MD setting to filter and select high-quality MD instruction data in a cost-effective, RM-as-a-judge fashion. Extensive evaluations on a wide range of MD and long-context benchmarks spanning various tasks show MDCure consistently improves performance over pre-trained baselines and over corresponding base models by up to 75.5%.

We release MDCure datasets of size 12k, 36k, and 72k. We also release MDCureRM and the best MDCure'd model for each architecture/size combination. To access all our models and datasets, please visit our [HF Collection](https://huggingface.co/collections/yale-nlp/mdcure-6724914875e87f41e5445395). For further details regarding dataset construction, please see our [paper](https://arxiv.org/pdf/2410.23463) and [Github repo](https://github.com/yale-nlp/MDCure). For additional details regarding how to use **yale-nlp/MDCure-Qwen2-7B-Instruct**, please see below.

<p align="center">
<img src="fig1.png" width="90%">
</p>
<p align="center" style="margin-top: 0; padding-top: 0;">
<em>The MDCure pipeline generates diverse multi-document instructions, filters them via fine-grained scoring by MDCureRM, and tunes a base LLM to enhance its multi-document capabilities.</em>
</p>

## Model Details

**yale-nlp/MDCure-Qwen2-7B-Instruct** is initialized from [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) and fine-tuned on the [MDCure-72k](https://huggingface.co/datasets/yale-nlp/MDCure-72k) dataset.

## Requirements

We recommend using the latest version of HF Transformers, or any `transformers>=4.45.0`, to avoid any potential errors when using this model.

## Quickstart

Below we provide a code snippet demonstrating how to load the tokenizer and model and generate content in response to an input context concerning multiple source documents and a related question or instruction. We strongly recommend to separate the texts and/or instruction using `

` or `<doc-sep>` to maintain consistency with the format of the data used during training.

```python
model = AutoModelForCausalLM.from_pretrained("yale-nlp/MDCure-Qwen2-7B-Instruct", device_map='auto',torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("yale-nlp/MDCure-Qwen2-7B-Instruct")

source_text_1 = ...
source_text_2 = ...
source_text_3 = ...
prompt = f"{source_text_1}

{source_text_2}

{source_text_3}

What happened in CHAMPAIGN regarding Lovie Smith and the 2019 defense improvements? Respond with 1-2 sentences."

messages = [
      {"role": "system", "content": "You are an assistant with strong multi-document processing skills."},
      {"role": "user", "content": prompt},
      ]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device) 

generated_ids = model.generate(**model_inputs, max_new_tokens=512)
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]
print(response)
```

## All MDCure Models
We open-source our custom multi-document instruction scoring model, MDCureRM, as well as our best MDCure'd models at the following links:

| Model                     | Huggingface Repo    | Description                  |
|---------------------------|---------------------|------------------------------|
| **MDCureRM**  | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCureRM) | Multi-objective reward model to score and filter MD instruction data more cheaply and effectively than GPT-3.5-Turbo |
| **MDCure-FlanT5-Base**       | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-FlanT5-Base) | **FlanT5-Base** fine-tuned with MDCure-72k          |
| **MDCure-FlanT5-Large**   | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-FlanT5-Large) | **FlanT5-Large** fine-tuned with MDCure-72k         |
| **MDCure-Qwen2-1.5B-Instruct**    | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-Qwen2-1.5B-Instruct) | **Qwen2-1.5B-Instruct** fine-tuned with MDCure-72k  |
| **MDCure-Qwen2-7B-Instruct**      | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-Qwen2-7B-Instruct) | **Qwen2-7B-Instruct** fine-tuned with MDCure-72k    |
| **MDCure-LLAMA3.1-8B-Instruct**   | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-LLAMA3.1-8B-Instruct) | **LLAMA3.1-8B-Instruct** fine-tuned with MDCure-72k  |
| **MDCure-LLAMA3.1-70B-Instruct**  | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-LLAMA3.1-70B-Instruct) | **LLAMA3.1-70B-Instruct** fine-tuned with MDCure-72k |

## Citation

If you find our work useful, please cite our paper as:

```bibtex
@article{liu2024mdcure,
    title={MDCure: A Scalable Pipeline for Multi-Document Instruction-Following},
    author={Gabrielle Kaili-May Liu and Bowen Shi and Avi Caciularu and Idan Szpektor and Arman Cohan},
    journal={arXiv preprint arXiv:2410.23463},
    year={2024},
    url={https://arxiv.org/abs/2410.23463}
}
```