Text Generation
Transformers
PyTorch
Safetensors
English
llama
finance
Eval Results
text-generation-inference
Inference Endpoints
AdaptLLM commited on
Commit
c9c7d95
1 Parent(s): ca78a94

init finance-chat

Browse files
README.md CHANGED
@@ -1,28 +1,21 @@
1
- # Adapting Large Language Models to Domains
2
  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).
3
 
4
- 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.
5
-
6
- $\textcolor{blue}{\text{🤗 We are currently working hard on developing domain-specific models across different scales and architectures, please stay tuned! 🤗}}$
7
 
8
  **************************** **Updates** ****************************
9
  * 12/8: Released our [models](https://huggingface.co/AdaptLLM/finance-chat) developed from LLaMA-2-Chat-7B.
10
  * 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.
11
 
12
- # Contents
13
- * [Domain-specific LLaMA-1](#LLaMA-1-7B)
14
- * [Domain-specific LLaMA-2-Chat](#LLaMA-2-Chat)
15
- * [Domain-specific Tasks](#Tasks)
16
- * [Citation](#citation)
17
 
18
- # Domain-specific LLaMA-1<a name="LLaMA-1-7B"></a>
19
  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:
20
 
21
  <p align='center'>
22
  <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700">
23
  </p>
24
 
25
- # Domain-specific LLaMA-2-Chat<a name="LLaMA-2-Chat"></a>
26
  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)
27
 
28
  For example, to chat with the finance model:
@@ -32,7 +25,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
32
  model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat")
33
  tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-chat")
34
 
35
- # Your input here:
36
  user_input = '''Use this fact to answer the question: Title of each class Trading Symbol(s) Name of each exchange on which registered
37
  Common Stock, Par Value $.01 Per Share MMM New York Stock Exchange
38
  MMM Chicago Stock Exchange, Inc.
@@ -42,7 +35,7 @@ MMM Chicago Stock Exchange, Inc.
42
 
43
  Which debt securities are registered to trade on a national securities exchange under 3M's name as of Q2 of 2023?'''
44
 
45
- # The prompt template for LLaMA-2-Chat demo
46
  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]"
47
 
48
  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
53
 
54
  print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
55
  ```
56
- # Domain-specific Tasks<a name="Tasks"></a>:
57
  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).
58
 
59
  **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.
60
 
61
- # Citation <a name="citation"></a>
62
  If you find our work helpful, please cite us:
63
  ```bibtex
64
  @article{adaptllm,
 
1
+ # Adapt Large Language Models to Domains
2
  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).
3
 
4
+ 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**.
 
 
5
 
6
  **************************** **Updates** ****************************
7
  * 12/8: Released our [models](https://huggingface.co/AdaptLLM/finance-chat) developed from LLaMA-2-Chat-7B.
8
  * 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.
9
 
 
 
 
 
 
10
 
11
+ ## Domain-Specific LLaMA-1
12
  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:
13
 
14
  <p align='center'>
15
  <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700">
16
  </p>
17
 
18
+ ## Domain-Specific LLaMA-2-Chat
19
  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)
20
 
21
  For example, to chat with the finance model:
 
25
  model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat")
26
  tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-chat")
27
 
28
+ # Put your input here:
29
  user_input = '''Use this fact to answer the question: Title of each class Trading Symbol(s) Name of each exchange on which registered
30
  Common Stock, Par Value $.01 Per Share MMM New York Stock Exchange
31
  MMM Chicago Stock Exchange, Inc.
 
35
 
36
  Which debt securities are registered to trade on a national securities exchange under 3M's name as of Q2 of 2023?'''
37
 
38
+ # we use the prompt template for LLaMA-2-Chat demo
39
  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]"
40
 
41
  inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
 
46
 
47
  print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
48
  ```
49
+ ## Domain-specific Tasks
50
  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).
51
 
52
  **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.
53
 
54
+ ## Citation
55
  If you find our work helpful, please cite us:
56
  ```bibtex
57
  @article{adaptllm,
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