Add library_name and pipeline_tag (#1)

- Add library_name and pipeline_tag (b3b7ff725b5e4f31979d1cd565c734876f43e167)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,23 +1,23 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen2-7B-Instruct
 datasets:
 - yale-nlp/MDCure-72k
 language:
 - en
-base_model:
-- Qwen/Qwen2-7B-Instruct
+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%.
@@ -41,7 +41,9 @@ We recommend using the latest version of HF Transformers, or any `transformers>=
 
 ## 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 `\n\n` or `<doc-sep>` to maintain consistency with the format of the data used during training.
+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")
@@ -50,7 +52,13 @@ tokenizer = AutoTokenizer.from_pretrained("yale-nlp/MDCure-Qwen2-7B-Instruct")
 source_text_1 = ...
 source_text_2 = ...
 source_text_3 = ...
-prompt = f"{source_text_1}\n\n{source_text_2}\n\n{source_text_3}\n\nWhat happened in CHAMPAIGN regarding Lovie Smith and the 2019 defense improvements? Respond with 1-2 sentences."
+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."},
@@ -78,7 +86,7 @@ We open-source our custom multi-document instruction scoring model, MDCureRM, as
 | **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-72 |
+| **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