Upload folder using huggingface_hub

README.md

@@ -217,9 +217,10 @@ We explore **continued pre-training on domain-specific corpora** for large langu
 ### 🤗 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.
+* 2024/1/16: 🎉 Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!🎉
+* 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B.
+* 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B.
+* 2023/9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/law-tasks), and [base models](https://huggingface.co/AdaptLLM/law-LLM) developed from LLaMA-1-7B.
 
 ## Domain-Specific LLaMA-1
 ### LLaMA-1-7B
@@ -235,12 +236,12 @@ Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is si
 ## Domain-Specific LLaMA-2-Chat
 Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat)
 
-For example, to chat with the finance model:
+For example, to chat with the finance-chat model:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat")
-tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-chat", use_fast=False)
+tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-chat")
 
 # Put your input here:
 user_input = '''Use this fact to answer the question: Title of each class Trading Symbol(s) Name of each exchange on which registered
@@ -252,8 +253,14 @@ 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 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]"
+# Apply the prompt template and system prompt of LLaMA-2-Chat demo for chat models (NOTE: NO prompt template is required for base models!)
+our_system_prompt = "\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" # Please do NOT change this
+prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{user_input} [/INST]"
+
+# # NOTE:
+# # If you want to apply your own system prompt, please integrate it into the instruction part following our system prompt like this:
+# your_system_prompt = "Please, check if the answer can be inferred from the pieces of context provided."
+# prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\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]