Create README.md

README.md

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+---
+license: llama3
+language:
+- en
+tags:
+- finance
+datasets:
+- Open-Orca/OpenOrca
+- GAIR/lima
+- WizardLM/WizardLM_evol_instruct_V2_196k
+---
+
+Quantized to exl2 using Exllamav2 0.0.2
+
+# Instruction Pre-Training: Language Models are Supervised Multitask Learners
+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).
+
+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.**
+
+<p align='center'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/vRdsFIVQptbNaGiZ18Lih.png" width="400">
+</p>
+
+## Resources
+**🤗 We share our data and models with example usages, feel free to open any issues or discussions! 🤗**
+
+- Context-Based Instruction Synthesizer: [instruction-synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer)
+- Fine-Tuning Data for the Synthesizer: [ft-instruction-synthesizer-collection](https://huggingface.co/datasets/instruction-pretrain/ft-instruction-synthesizer-collection)
+- General Models Pre-Trained from Scratch:
+  - [InstructLM-500M](https://huggingface.co/instruction-pretrain/InstructLM-500M)
+  - [InstructLM-1.3B](https://huggingface.co/instruction-pretrain/InstructLM-1.3B)
+- Domain-Specific Models Pre-Trained from Llama3-8B:
+  - [Finance-Llama3-8B](https://huggingface.co/instruction-pretrain/finance-Llama3-8B)
+  - [Biomedicine-Llama3-8B](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B)
+- General Instruction-Augmented Corpora: [general-instruction-augmented-corpora](https://huggingface.co/datasets/instruction-pretrain/general-instruction-augmented-corpora)
+- 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)
+
+
+## Domain-Adaptive Continued Pre-Training
+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).
+
+### 1. To chat with the finance-Llama3-8B model:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("instruction-pretrain/finance-Llama3-8B")
+tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/finance-Llama3-8B")
+
+# Put your input here, NO prompt template is required
+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.
+1.500% Notes due 2026 MMM26 New York Stock Exchange
+1.750% Notes due 2030 MMM30 New York Stock Exchange
+1.500% Notes due 2031 MMM31 New York Stock Exchange
+
+Which debt securities are registered to trade on a national securities exchange under 3M's name as of Q2 of 2023?'''
+
+inputs = tokenizer(user_input, return_tensors="pt", add_special_tokens=True).input_ids.to(model.device)
+outputs = model.generate(input_ids=inputs, max_new_tokens=400)[0]
+
+answer_start = int(inputs.shape[-1])
+pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
+
+print(pred)
+```
+
+### 2. To evaluate our models on the domain-specific tasks
+1. Set up dependencies
+```bash
+git clone https://github.com/microsoft/LMOps
+cd LMOps/adaptllm
+pip install -r requirements.txt
+```
+
+2. Evaluate
+```bash
+DOMAIN='finance'
+
+# if the model can fit on a single GPU: set MODEL_PARALLEL=False
+# elif the model is too large to fit on a single GPU: set MODEL_PARALLEL=True
+MODEL_PARALLEL=False
+
+# number of GPUs, chosen from [1,2,4,8]
+N_GPU=1
+
+# Set as True
+add_bos_token=True
+
+bash scripts/inference.sh ${DOMAIN} 'instruction-pretrain/finance-Llama3-8B' ${add_bos_token} ${MODEL_PARALLEL} ${N_GPU}
+```
+
+## Citation
+If you find our work helpful, please cite us:
+
+[AdaptLLM](https://huggingface.co/papers/2309.09530)
+```bibtex
+@inproceedings{
+cheng2024adapting,
+title={Adapting Large Language Models via Reading Comprehension},
+author={Daixuan Cheng and Shaohan Huang and Furu Wei},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024},
+url={https://openreview.net/forum?id=y886UXPEZ0}
+}
+```