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Model: yale-nlp/MDCure-Qwen2-7B-Instruct Source: Original Platform
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---
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base_model:
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- Qwen/Qwen2-7B-Instruct
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datasets:
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- yale-nlp/MDCure-72k
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language:
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- en
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license: apache-2.0
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tags:
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- multi-document
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- long-context
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- Long Context
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library_name: transformers
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pipeline_tag: text-generation
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---
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# MDCure-Qwen2-7B-Instruct
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[📄 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)
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## Introduction
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**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%.
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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.
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<p align="center">
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<img src="fig1.png" width="90%">
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</p>
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<p align="center" style="margin-top: 0; padding-top: 0;">
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<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>
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</p>
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## Model Details
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**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.
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## Requirements
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We recommend using the latest version of HF Transformers, or any `transformers>=4.45.0`, to avoid any potential errors when using this model.
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## Quickstart
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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 `
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` or `<doc-sep>` to maintain consistency with the format of the data used during training.
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```python
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model = AutoModelForCausalLM.from_pretrained("yale-nlp/MDCure-Qwen2-7B-Instruct", device_map='auto',torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained("yale-nlp/MDCure-Qwen2-7B-Instruct")
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source_text_1 = ...
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source_text_2 = ...
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source_text_3 = ...
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prompt = f"{source_text_1}
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{source_text_2}
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{source_text_3}
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What happened in CHAMPAIGN regarding Lovie Smith and the 2019 defense improvements? Respond with 1-2 sentences."
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messages = [
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{"role": "system", "content": "You are an assistant with strong multi-document processing skills."},
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{"role": "user", "content": prompt},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(**model_inputs, max_new_tokens=512)
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generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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## All MDCure Models
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We open-source our custom multi-document instruction scoring model, MDCureRM, as well as our best MDCure'd models at the following links:
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| Model | Huggingface Repo | Description |
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|---------------------------|---------------------|------------------------------|
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| **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 |
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| **MDCure-FlanT5-Base** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-FlanT5-Base) | **FlanT5-Base** fine-tuned with MDCure-72k |
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| **MDCure-FlanT5-Large** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-FlanT5-Large) | **FlanT5-Large** fine-tuned with MDCure-72k |
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| **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 |
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| **MDCure-Qwen2-7B-Instruct** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-Qwen2-7B-Instruct) | **Qwen2-7B-Instruct** fine-tuned with MDCure-72k |
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| **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 |
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| **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 |
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## Citation
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If you find our work useful, please cite our paper as:
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```bibtex
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@article{liu2024mdcure,
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title={MDCure: A Scalable Pipeline for Multi-Document Instruction-Following},
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author={Gabrielle Kaili-May Liu and Bowen Shi and Avi Caciularu and Idan Szpektor and Arman Cohan},
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journal={arXiv preprint arXiv:2410.23463},
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year={2024},
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url={https://arxiv.org/abs/2410.23463}
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}
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```
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16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
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{
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
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version https://git-lfs.github.com/spec/v1
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oid sha256:bcfe42da0a4497e8b2b172c1f9f4ec423a46dc12907f4349c55025f670422ba9
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size 11418266
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37
tokenizer_config.json
Normal file
37
tokenizer_config.json
Normal file
@@ -0,0 +1,37 @@
|
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{
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1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user