uploaded readme

README.md

@@ -0,0 +1,122 @@
+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+InstructLM-1.3B - AWQ
+- Model creator: https://huggingface.co/instruction-pretrain/
+- Original model: https://huggingface.co/instruction-pretrain/InstructLM-1.3B/
+
+
+
+
+Original model description:
+---
+license: apache-2.0
+datasets:
+- tiiuae/falcon-refinedweb
+- instruction-pretrain/ft-instruction-synthesizer-collection
+- instruction-pretrain/general-instruction-augmented-corpora
+language:
+- en
+---
+# Instruction Pre-Training: Language Models are Supervised Multitask Learners (EMNLP 2024)
+This repo contains the **general models pre-trained from scratch** (on 100B tokens) 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. In our experiments, we synthesize 200M instruction-response pairs covering 40+ task categories to verify the effectiveness of *Instruction Pre-Training*. 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>
+
+### [2024/11/29] 🤗 Introduce the multimodal version of instruction synthesizer at [AdaMLLM](https://huggingface.co/papers/2411.19930), for synthesizing visual instruction tasks 🤗
+
+**************************** **Updates** ****************************
+* 2024/11/30: Released the multimodal version of the instruction synthesizer: [Visual Instruction Synthesizer](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains)
+* 2024/9/20: Our paper has been accepted by EMNLP 2024 main conference🎉
+* 2024/9/11: Updated [FAQ on continual pre-training from Llama3](https://huggingface.co/instruction-pretrain/instruction-synthesizer)
+* 2024/8/29: Updated [guidelines](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B) on evaluating any 🤗Huggingface models on the domain-specific tasks
+* 2024/7/31: Updated pre-training suggestions in the `Advanced Usage` section of [instruction-synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer)
+* 2024/7/15: We scaled up the pre-trained tokens from 100B to 250B, with the number of synthesized instruction-response pairs reaching 500M. The performance trend on downstream tasks throughout the pre-training process:
+<p align='left'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/0okCfRkC6uALTfuNxt0Fa.png" width="500">
+</p>
+* 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)
+
+## Resources
+**🤗 We share our data and models with example usages, feel free to open any discussions at [this page](https://huggingface.co/papers/2406.14491)! 🤗**
+
+- Thanks to the demo [davanstrien/instruction-synthesizer](https://huggingface.co/spaces/davanstrien/instruction-synthesizer) for implementing our approach
+- 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 (on 100B tokes):
+  - [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)
+
+
+## General Pre-Training From Scratch
+We augment the [RefinedWeb corproa](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) with instruction-response pairs generated by our [context-based instruction synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer) to pre-train general langauge models from scratch.
+
+To evaluate our general base model using the [lm-evaluation-harness framework](https://github.com/EleutherAI/lm-evaluation-harness)
+
+1. Setup dependencies:
+```bash
+git clone https://github.com/EleutherAI/lm-evaluation-harness
+cd lm-evaluation-harness
+pip install -e .
+```
+
+2. Evalaute:
+```bash
+MODEL=instruction-pretrain/InstructLM-1.3B
+add_bos_token=True # this flag is needed because lm-eval-harness set add_bos_token to False by default, but ours require add_bos_token to be True
+
+accelerate launch -m lm_eval --model hf \
+    --model_args pretrained=${MODEL},add_bos_token=${add_bos_token},dtype=float16  \
+    --gen_kwargs do_sample=False \
+    --tasks piqa,hellaswag,winogrande \
+    --batch_size auto \
+    --num_fewshot 0
+
+accelerate launch -m lm_eval --model hf \
+    --model_args pretrained=${MODEL},add_bos_token=${add_bos_token},dtype=float16 \
+    --gen_kwargs do_sample=False \
+    --tasks social_iqa,ai2_arc,openbookqa,boolq,mmlu \
+    --batch_size auto \
+    --num_fewshot 5
+```
+
+## Citation
+If you find our work helpful, please cite us:
+
+[Instruction Pre-Training](https://huggingface.co/papers/2406.14491) (EMNLP 2024)
+```bibtex
+@article{cheng2024instruction,
+  title={Instruction Pre-Training: Language Models are Supervised Multitask Learners},
+  author={Cheng, Daixuan and Gu, Yuxian and Huang, Shaohan and Bi, Junyu and Huang, Minlie and Wei, Furu},
+  journal={arXiv preprint arXiv:2406.14491},
+  year={2024}
+}
+```
+
+[Adapt LLM to Domains](https://huggingface.co/papers/2309.09530)（ICLR 2024）
+```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}
+}
+```
+