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--- |
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language: |
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- en |
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datasets: |
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- Open-Orca/OpenOrca |
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- GAIR/lima |
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- WizardLM/WizardLM_evol_instruct_V2_196k |
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metrics: |
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- accuracy |
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pipeline_tag: text-generation |
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tags: |
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- finance |
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--- |
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# Adapting LLMs to Domains via Continual Pre-Training (ICLR 2024) |
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This repo contains the domain-specific base model developed from **LLaMA-1-13B**, using the method in our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530). |
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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**. |
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### [2024/6/21] 🤗 We release the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain), effective for both pre-training from scratch and continual pre-training 🤗 |
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**************************** **Updates** **************************** |
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* 2024/8/29: Updated [guidelines](https://huggingface.co/datasets/AdaptLLM/finance-tasks) on evaluating any 🤗Huggingface models on the domain-specific tasks |
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* 2024/6/22: Released the [benchmarking code](https://github.com/microsoft/LMOps/tree/main/adaptllm) |
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* 2024/6/21: Released the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain) |
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* 2024/4/2: Released the [raw data splits (train and test)](https://huggingface.co/datasets/AdaptLLM/ConvFinQA) of all the evaluation datasets |
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* 2024/1/16: Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024 |
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* 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B |
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* 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B |
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* 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 |
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## 1. Domain-Specific Models |
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### LLaMA-1-7B |
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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: |
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<p align='center'> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700"> |
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</p> |
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### LLaMA-1-13B |
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Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is similarly effective for larger-scale models**, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B). |
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### LLaMA-2-Chat |
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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). |
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For example, to chat with the finance model: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-LLM-13B") |
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tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-LLM-13B", use_fast=False) |
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# Put your input here: |
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user_input = '''Use this fact to answer the question: Title of each class Trading Symbol(s) Name of each exchange on which registered |
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Common Stock, Par Value $.01 Per Share MMM New York Stock Exchange |
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MMM Chicago Stock Exchange, Inc. |
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1.500% Notes due 2026 MMM26 New York Stock Exchange |
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1.750% Notes due 2030 MMM30 New York Stock Exchange |
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1.500% Notes due 2031 MMM31 New York Stock Exchange |
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Which debt securities are registered to trade on a national securities exchange under 3M's name as of Q2 of 2023?''' |
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# Simply use your input as the prompt for base models |
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prompt = user_input |
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inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device) |
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outputs = model.generate(input_ids=inputs, max_length=2048)[0] |
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answer_start = int(inputs.shape[-1]) |
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pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True) |
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print(pred) |
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``` |
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### LLaMA-3-8B (💡New!) |
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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). |
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## 2. Domain-Specific Tasks |
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### Pre-templatized Testing Splits |
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To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test 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). |
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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. |
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### Evaluating Any Huggingface LMs on Domain-Specific Tasks (💡New!) |
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You can use the following script to reproduce our results and evaluate any other Huggingface models on domain-specific tasks. Note that the script is NOT applicable to models that require specific prompt templates (e.g., Llama2-chat, Llama3-Instruct). |
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1). **Set Up Dependencies** |
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```bash |
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git clone https://github.com/microsoft/LMOps |
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cd LMOps/adaptllm |
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pip install -r requirements.txt |
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``` |
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2). **Evaluate the Model** |
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```bash |
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# Select the domain from ['biomedicine', 'finance', 'law'] |
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DOMAIN='finance' |
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# Specify any Huggingface model name (Not applicable to chat models) |
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MODEL='AdaptLLM/finance-LLM-13B' |
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# Model parallelization: |
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# - Set MODEL_PARALLEL=False if the model fits on a single GPU. |
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# We observe that LMs smaller than 10B always meet this requirement. |
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# - Set MODEL_PARALLEL=True if the model is too large and encounters OOM on a single GPU. |
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MODEL_PARALLEL=True |
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# Choose the number of GPUs from [1, 2, 4, 8] |
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N_GPU=2 |
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# Whether to add a BOS token at the beginning of the prompt input: |
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# - Set to False for AdaptLLM. |
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# - Set to True for instruction-pretrain models. |
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# If unsure, we recommend setting it to False, as this is suitable for most LMs. |
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add_bos_token=False |
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# Run the evaluation script |
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bash scripts/inference.sh ${DOMAIN} ${MODEL} ${add_bos_token} ${MODEL_PARALLEL} ${N_GPU} |
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``` |
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### Raw Datasets |
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We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages: [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt), [RCT](https://huggingface.co/datasets/AdaptLLM/RCT), [ConvFinQA](https://huggingface.co/datasets/AdaptLLM/ConvFinQA), [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA), [Headline](https://huggingface.co/datasets/AdaptLLM/Headline), [NER](https://huggingface.co/datasets/AdaptLLM/NER), [FPB](https://huggingface.co/datasets/AdaptLLM/FPB) |
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### Domain Knowledge Probing |
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Our pre-processed knowledge probing datasets are available at: [med_knowledge_prob](https://huggingface.co/datasets/AdaptLLM/med_knowledge_prob) and [law_knowledge_prob](https://huggingface.co/datasets/AdaptLLM/law_knowledge_prob) |
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## Citation |
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If you find our work helpful, please cite us: |
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```bibtex |
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@inproceedings{ |
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cheng2024adapting, |
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title={Adapting Large Language Models via Reading Comprehension}, |
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author={Daixuan Cheng and Shaohan Huang and Furu Wei}, |
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booktitle={The Twelfth International Conference on Learning Representations}, |
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year={2024}, |
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url={https://openreview.net/forum?id=y886UXPEZ0} |
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} |
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``` |
