Update README.md

README.md

@@ -116,7 +116,7 @@ model-index:
       name: Open LLM Leaderboard
 ---
 
-# Adapting Large Language Models to Domains (ICLR 2024)
+# Adapting LLMs to Domains via Continual Pre-Training (ICLR 2024)
 This repo contains the domain-specific chat model developed from **LLaMA-2-Chat-7B**, using the method in 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**. 
@@ -179,7 +179,7 @@ outputs = model.generate(input_ids=inputs, max_length=4096)[0]
 answer_start = int(inputs.shape[-1])
 pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
 
-print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
+print(pred)
 ```
 ### LLaMA-3-8B (💡New!)
 In our recent research on [Instruction-Pretrain](https://huggingface.co/papers/2406.14491), we developed a context-based instruction synthesizer to augment the raw corpora with instruction-response pairs, **enabling Llama3-8B to be comparable to or even outperform Llama3-70B**: [Finance-Llama3-8B](https://huggingface.co/instruction-pretrain/finance-Llama3-8B), [Biomedicine-Llama3-8B](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B).