Add pipeline tag, library name and paper link

This PR adds the `pipeline_tag` and `library_name` to the model card metadata so the model can be found at https://huggingface.co/models?pipeline_tag=text-generation. It also adds the paper link at the beginning of the model card.

README.md

@@ -1,11 +1,13 @@
 ---
 license: apache-2.0
 inference: false
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
 # MegaBeam-Mistral-7B-512k Model
 
-`MegaBeam-Mistral-7B-512k` is a Long-Context LLM that supports 524,288 tokens in its context. `MegaBeam-Mistral-7B-512k` was trained on [Mistral-7B Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), and can be deployed using various serving frameworks like [vLLM](https://github.com/vllm-project/vllm) and Amazon SageMaker's [DJL](https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-models-frameworks-djl-serving.html) endpoint. Please refer to our [GitRepo](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/tree/main/megabeam-mistral-7b) for deployment and inference examples.
+This model, presented in [Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing](https://huggingface.co/papers/2505.08651), is a Long-Context LLM that supports 524,288 tokens in its context. `MegaBeam-Mistral-7B-512k` was trained on [Mistral-7B Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), and can be deployed using various serving frameworks like [vLLM](https://github.com/vllm-project/vllm) and Amazon SageMaker's [DJL](https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-models-frameworks-djl-serving.html) endpoint. Please refer to our [GitRepo](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/tree/main/megabeam-mistral-7b) for deployment and inference examples.
 
 **New update!** - Watch our [talk on MegaBeam](https://neurips.cc/Expo/Conferences/2024/talk%20panel/100673) at NeurIPS 2024
 
@@ -164,7 +166,7 @@ print(chat_completion)
 ```
 
 ### Deploy the model on a SageMaker Endpoint ###
-To deploy MegaBeam-Mistral-7B-512k on a SageMaker endpoint, please follow this [SageMaker DJL deployment guide](https://docs.djl.ai/docs/demos/aws/sagemaker/large-model-inference/sample-llm/vllm_deploy_mistral_7b.html).
+To deploy MegaBeam-Mistral-7B-512k on a SageMaker endpoint, please follow this [SageMaker DJL deployment guide](https://docs.djl.ai/docs/demos/aws/sagemaker/large-model-inference/sample-llm/vllm_deploy_mistral_7b.html).\
 
 Run the following Python code in a SageMaker notebook (with each block running in a separate cell)