readme

README.md

@@ -1,15 +1,28 @@
 ---
 language:
 - en
+license: llama2
+datasets:
+- EleutherAI/pile
+- Open-Orca/OpenOrca
+- GAIR/lima
+- WizardLM/WizardLM_evol_instruct_V2_196k
+metrics:
+- accuracy
+library_name: fairseq
+pipeline_tag: text-generation
 ---
-# Adapt Large Language Models to Domains
+
+# Adapt (Large) Language Models to Domains
 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**. 
 
+### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗
+
 **************************** **Updates** ****************************
-* 12/8: Released our [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 [models](https://huggingface.co/AdaptLLM/finance-LLM) developed from LLaMA-1-7B.
+* 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.
 
 
 ## Domain-Specific LLaMA-1
@@ -39,8 +52,8 @@ MMM Chicago Stock Exchange, Inc.
 
 Which debt securities are registered to trade on a national securities exchange under 3M's name as of Q2 of 2023?'''
 
-# we use the prompt template for LLaMA-2-Chat demo
-prompt = f"<s>[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n{user_input} [/INST]"
+# We use the prompt template of LLaMA-2-Chat demo
+prompt = f"<s>[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n{user_input} [/INST]"
 
 inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
 outputs = model.generate(input_ids=inputs, max_length=4096)[0]
@@ -50,7 +63,7 @@ pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
 
 print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
 ```
-## Domain-specific Tasks
+## Domain-Specific Tasks
 To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
 
 **Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.