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 # Adapt (Large) Language Models to Domains
-This repo contains the evaluation datasets for our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
+This repo contains the **evaluation datasets** for our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
 
 We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**. 
 
 ### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗
 
 **************************** **Updates** ****************************
+* 12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/finance-LLM-13B) developed from LLaMA-1-13B.
 * 12/8: Released our [chat models](https://huggingface.co/AdaptLLM/finance-chat) developed from LLaMA-2-Chat-7B.
 * 9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [base models](https://huggingface.co/AdaptLLM/finance-LLM) developed from LLaMA-1-7B.