init finance-chat

README.md

@@ -1,28 +1,21 @@
-# Adapting 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**. Moreover, our domain-specific reading comprehension texts enhance model performance even on general benchmarks, indicating potential for developing a general LLM across more domains.
-
-$\textcolor{blue}{\text{🤗 We are currently working hard on developing domain-specific models across different scales and architectures, please stay tuned! 🤗}}$
+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**. 
 
 **************************** **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.
 
-# Contents
-* [Domain-specific LLaMA-1](#LLaMA-1-7B)
-* [Domain-specific LLaMA-2-Chat](#LLaMA-2-Chat)
-* [Domain-specific Tasks](#Tasks)
-* [Citation](#citation)
 
-# Domain-specific LLaMA-1<a name="LLaMA-1-7B"></a>
+## Domain-Specific LLaMA-1
 In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
 
 <p align='center'>
     <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700">
 </p>
 
-# Domain-specific LLaMA-2-Chat<a name="LLaMA-2-Chat"></a>
+## 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:
@@ -32,7 +25,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat")
 tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-chat")
 
-# Your input here:
+# 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
 Common Stock, Par Value $.01 Per Share MMM New York Stock Exchange
 MMM Chicago Stock Exchange, Inc.
@@ -42,7 +35,7 @@ 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?'''
 
-# The prompt template for LLaMA-2-Chat demo
+# 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]"
 
 inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
@@ -53,12 +46,12 @@ pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True, do_sam
 
 print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
 ```
-# Domain-specific Tasks<a name="Tasks"></a>:
+## 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.
 
-# Citation <a name="citation"></a>
+## Citation
 If you find our work helpful, please cite us:
 ```bibtex
 @article{adaptllm,

config.json

@@ -0,0 +1,25 @@
+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
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+    "eos_token_id": 2,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 11008,
+    "max_position_embeddings": 4096,
+    "model_type": "llama",
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+    "num_key_value_heads": 32,
+    "pad_token_id": 32000,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": null,
+    "tie_word_embeddings": false,
+    "torch_dtype": "float16",
+    "transformers_version": "4.31.0.dev0",
+    "use_cache": true,
+    "vocab_size": 32001
+}

generation_config.json

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+{
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+    "pad_token_id": 32000,
+    "temperature": 0.9,
+    "top_p": 0.6,
+    "transformers_version": "4.31.0.dev0"
+}

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