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--- |
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license: llama3 |
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language: |
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- en |
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tags: |
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- finance |
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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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--- |
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# Instruction Pre-Training: Language Models are Supervised Multitask Learners |
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This repo contains the **finance model developed from Llama3-8B** in our paper [Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://huggingface.co/papers/2406.14491). |
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We explore supervised multitask pre-training by proposing ***Instruction Pre-Training***, a framework that scalably augments massive raw corpora with instruction-response pairs to pre-train language models. The instruction-response pairs are generated by an efficient instruction synthesizer built on open-source models. ***Instruction Pre-Training* outperforms *Vanilla Pre-training* in both general pre-training from scratch and domain-adaptive continual pre-training.** In pre-training from scratch, *Instruction Pre-Training* not only improves pre-trained base models but also benefits more from further instruction tuning. **In continual pre-training, *Instruction Pre-Training* enables Llama3-8B to be comparable to or even outperform Llama3-70B.** |
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<p align='center'> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/vRdsFIVQptbNaGiZ18Lih.png" width="400"> |
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</p> |
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**************************** **Updates** **************************** |
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* 2024/7/31: Updated pre-training suggestions in the `Advanced Usage` section of [instruction-synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer) |
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* 2024/7/15: We scaled up the pre-trained tokens from 100B to 250B, with the number of synthesized instruction-response pairs reaching 500M! Below, we show the performance trend on downstream tasks throughout the pre-training process: |
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<p align='left'> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/0okCfRkC6uALTfuNxt0Fa.png" width="500"> |
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</p> |
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* 2024/6/21: Released the [paper](https://huggingface.co/papers/2406.14491), [code](https://github.com/microsoft/LMOps), and [resources](https://huggingface.co/instruction-pretrain) |
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## Resources |
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**🤗 We share our data and models with example usages, feel free to open any discussions at [this page](https://huggingface.co/papers/2406.14491)! 🤗** |
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- Thanks to the demo [davanstrien/instruction-synthesizer](https://huggingface.co/spaces/davanstrien/instruction-synthesizer) for implementing our approach |
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- Context-Based Instruction Synthesizer: [instruction-synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer) |
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- Fine-Tuning Data for the Synthesizer: [ft-instruction-synthesizer-collection](https://huggingface.co/datasets/instruction-pretrain/ft-instruction-synthesizer-collection) |
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- General Models Pre-Trained from Scratch (on 100B tokes): |
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- [InstructLM-500M](https://huggingface.co/instruction-pretrain/InstructLM-500M) |
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- [InstructLM-1.3B](https://huggingface.co/instruction-pretrain/InstructLM-1.3B) |
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- Domain-Specific Models Pre-Trained from Llama3-8B: |
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- [Finance-Llama3-8B](https://huggingface.co/instruction-pretrain/finance-Llama3-8B) |
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- [Biomedicine-Llama3-8B](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B) |
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- General Instruction-Augmented Corpora: [general-instruction-augmented-corpora](https://huggingface.co/datasets/instruction-pretrain/general-instruction-augmented-corpora) |
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- Domain-Specific Instruction-Augmented Corpora (no finance data to avoid ethical issues): [medicine-instruction-augmented-corpora](https://huggingface.co/datasets/instruction-pretrain/medicine-instruction-augmented-corpora) |
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## Domain-Adaptive Continued Pre-Training |
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Following [AdaptLLM](https://huggingface.co/AdaptLLM/finance-chat), we augment the domain-specific raw corpora with instruction-response pairs generated by our [context-based instruction synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer). |
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### 1. chat with the finance-Llama3-8B model: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("instruction-pretrain/finance-Llama3-8B") |
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tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/finance-Llama3-8B") |
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# Put your input here, NO prompt template is required |
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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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inputs = tokenizer(user_input, return_tensors="pt", add_special_tokens=True).input_ids.to(model.device) |
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outputs = model.generate(input_ids=inputs, max_new_tokens=400)[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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### 2. evaluate any Huggingface LMs on domain-dpecific tasks (💡New!) |
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You can use the following scripts to reproduce our results and evaluate any other Huggingface models on domain-specific tasks. Note that these scripts are 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 LM name (Not applicable to models requiring specific prompt templates) |
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MODEL='instruction-pretrain/finance-Llama3-8B' |
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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=False |
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# Choose the number of GPUs from [1, 2, 4, 8] |
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N_GPU=1 |
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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=True |
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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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## Citation |
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If you find our work helpful, please cite us: |
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Instruction Pre-Training |
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```bibtex |
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@article{cheng2024instruction, |
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title={Instruction Pre-Training: Language Models are Supervised Multitask Learners}, |
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author={Cheng, Daixuan and Gu, Yuxian and Huang, Shaohan and Bi, Junyu and Huang, Minlie and Wei, Furu}, |
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journal={arXiv preprint arXiv:2406.14491}, |
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year={2024} |
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} |
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``` |
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[Adapt LLM to Domains](https://huggingface.co/papers/2309.09530) |
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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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``` |
