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	# LongLoRA and LongAlpaca for Long-context LLMs


	[![Huggingface Models](https://img.shields.io/badge/Models-Huggingface%20Models-bron)](https://huggingface.co/Yukang)
	[![Github](https://img.shields.io/badge/Github-Repo-cyan)](https://github.com/dvlab-research/LongLoRA)
	[![Data](https://img.shields.io/badge/Data-LongAlpaca%2012k-light)](https://huggingface.co/datasets/Yukang/LongAlpaca-12k)
	[![Paper](https://img.shields.io/badge/Paper-Arvix-blue)](https://arxiv.org/abs/2309.12307)

	[![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-yellow.svg)](https://github.com/dvlab-research/LongLoRA/blob/main/LICENSE)
	[![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-orange.svg)](https://github.com/dvlab-research/LongLoRA/blob/main/DATA_LICENSE)
	[![Weight License](https://img.shields.io/badge/Weight%20License-CC%20By%20NC%204.0-red)](https://github.com/dvlab-research/LongLoRA/blob/main/WEIGHT_LICENSE)

	For detailed usage and codes, please visit the [Github project](https://github.com/dvlab-research/LongLoRA).
	## TABLE OF CONTENTS
	1. [News](#news)
	2. [Examples](#examples)
	3. [Highlights](#highlights)
	4. [How to contribute](#how-to-contribute)
	5. [Requirements](#usage-requirements)
	6. [Installation and quick guide](#installation-and-quick-guide)
	7. [LongAlpaca Data](#longalpaca-data)
	8. [Models](#models)
	9. [Training](#training)
	10. [Evaluation](#evaluation)
	11. [Demo](#demo)
	12. [Data Generation via Pdf2Text](#data-generation-via-pdf2text)
	13. [Citation](#citation)
	14. [Acknowledgement](#acknowledgement)
	15. [License](#license)

	## News
	- [x] [2023.10.8] We release the long instruction-following dataset, [LongAlpaca-12k](https://huggingface.co/datasets/Yukang/LongAlpaca-12k) and the corresponding models, [LongAlpaca-7B](https://huggingface.co/Yukang/LongAlpaca-7B), [LongAlpaca-13B](https://huggingface.co/Yukang/LongAlpaca-13B), and [LongAlpaca-70B](https://huggingface.co/Yukang/LongAlpaca-70B).
	- (The previous sft models, [Llama-2-13b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-13b-chat-longlora-32k-sft) and [Llama-2-70b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-70b-chat-longlora-32k-sft), have been depreciated.)
	- [x] [2023.10.3] We add support GPTNeoX models. Please refer to this [PR](https://github.com/dvlab-research/LongLoRA/pull/32) for usage. Thanks for @naubull2 for this contribution.
	- [x] [2023.9.22] We release all our fine-tuned [models](https://huggingface.co/Yukang), including 70B-32k models, [LLaMA2-LongLoRA-70B-32k](https://huggingface.co/Yukang/Llama-2-70b-longlora-32k), [LLaMA2-LongLoRA-7B-100k](https://huggingface.co/Yukang/Llama-2-7b-longlora-100k-ft). Welcome to check them out!
	- [x] [2023.9.22] We release [Paper](http://arxiv.org/abs/2309.12307) and this GitHub repo, including training and evaluation code.

	LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models [[Paper](http://arxiv.org/abs/2309.12307)] <br />
	[Yukang Chen](https://scholar.google.com/citations?user=6p0ygKUAAAAJ&hl=en),
	[Shengju Qian](https://scholar.google.com/citations?user=QNnWmasAAAAJ),
	[Haotian Tang](https://scholar.google.com/citations?user=WxL13BAAAAAJ&hl),
	[Xin Lai](https://scholar.google.com/citations?user=tqNDPA4AAAAJ&hl=zh-CN),
	[Zhijian Liu](https://scholar.google.com/citations?user=3coYSTUAAAAJ&hl=en),
	[Song Han](https://scholar.google.com/citations?user=E0iCaa4AAAAJ&hl=zh-CN),
	[Jiaya Jia](https://scholar.google.com/citations?user=XPAkzTEAAAAJ&hl=en)<br />

	## Highlights
	1. In LongLoRA approach, The proposed shifted short attention is easy to implement, compatible with Flash-Attention, and is not required during inference.
	2. We released all our models, including models from 7B to 70B, context length from 8k to 100k, including [LLaMA2-LongLoRA-7B-100k](https://huggingface.co/Yukang/Llama-2-7b-longlora-100k-ft), [LLaMA2-LongLoRA-13B-64k](https://huggingface.co/Yukang/Llama-2-13b-longlora-64k), and [LLaMA2-LongLoRA-70B-32k](https://huggingface.co/Yukang/Llama-2-70b-longlora-32k).
	3. We built up a long-context instruction-following dataset, [LongAlpaca-12k](#longalpaca-data). We released the corresponding [LongAlpaca-7B](https://huggingface.co/Yukang/LongAlpaca-7B), [LongAlpaca-13B](https://huggingface.co/Yukang/LongAlpaca-13B) and [LongAlpaca-70B](https://huggingface.co/Yukang/LongAlpaca-70B) models. To our best knowledge, this is the first open-sourced long-context 70B model.

	## How to Contribute
	- Make sure to have git installed.
	- Create your own [fork](https://github.com/dvlab-research/LongLoRA/fork) of the project.
	- Clone the repository on your local machine, using git clone and pasting the url of this project.
	- Read both the `Requirements` and `Installation and Quick Guide` sections below.
	- Commit and push your changes.
	- Make a pull request when finished modifying the project.


	## Usage Requirements
	To download and use the [pre-trained weights](#pre-trained-weights) you will need:
	1. Hugging Face (HF) account with valid email. Note, the email used for HF must alse be used for the license agreement.
	2. Accept the Meta [license and acceptable use policy](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)


	## Installation and Quick Guide
	To install and run the application:
	1. [Fork this repo](https://github.com/dvlab-research/LongLoRA/fork) on github
	2. Clone the repository on your local machine, using git clone and pasting the url of this project.
	3. Run the following code:
	```
	pip install -r requirements.txt
	pip install flash-attn --no-build-isolation
	```
	4. Use either a [Released model](#released-models) or [Fine tune](#fine-tuning) a model to fit your preferences.
	5. Test your model by chat.
	6. Deploy your own demo.

	## LongAlpaca Data

	LongAlpaca-12k contains 9k long QA data that we collected and 3k short QA sampled from the original [Alpaca data](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json). This is to avoid the case that the model might degrade at short instruction following. The data we collect contains various types and amounts as the following figure.

	\| Data \| Short QA \| Long QA \| Total \| Download \|
	\|:---------------\|----------\|----------\|----------\|----------\|
	\| LongAlpaca-12k \| 3k \| 9k \| 12k \| [Link](https://huggingface.co/datasets/Yukang/LongAlpaca-12k) \|

	Following the original Alpaca format, our Long QA data uses the following prompts for fine-tuning:
	- `instruction`: `str`, describes the task the model should perform. For example, to answer a question after reading a book section or paper. We vary the contents and questions to make instructions diverse.
	- `output`: `str`, the answer to the instruction.

	We did not use the `input` format in the Alpaca format for simplicity.

	## Models

	### Models with supervised fine-tuning
	\| Model \| Size \| Context \| Train \| Link \|
	\|:---------------\|------\|---------\|---------\|-----------------------------------------------------------------------------------------------------------------------\|
	\| LongAlpaca-7B \| 7B \| 32768 \| Full FT \| [Model](https://huggingface.co/Yukang/LongAlpaca-7B) \|
	\| LongAlpaca-13B \| 13B \| 32768 \| Full FT \| [Model](https://huggingface.co/Yukang/LongAlpaca-13B) \|
	\| LongAlpaca-70B \| 70B \| 32768 \| LoRA+ \| [Model](https://huggingface.co/Yukang/LongAlpaca-70B) [(LoRA-weight)](https://huggingface.co/Yukang/LongAlpaca-70B-lora) \|


	### Models with context extension via fully fine-tuning
	\| Model \| Size \| Context \| Train \| Link \|
	\|:----------------------------\|------\|---------\|-------\|-------------------------------------------------------------------\|
	\| Llama-2-7b-longlora-8k-ft \| 7B \| 8192 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-8k-ft) \|
	\| Llama-2-7b-longlora-16k-ft \| 7B \| 16384 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-16k-ft) \|
	\| Llama-2-7b-longlora-32k-ft \| 7B \| 32768 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-32k-ft) \|
	\| Llama-2-7b-longlora-100k-ft \| 7B \| 100000 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-100k-ft) \|
	\| Llama-2-13b-longlora-8k-ft \| 13B \| 8192 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-13b-longlora-8k-ft) \|
	\| Llama-2-13b-longlora-16k-ft \| 13B \| 16384 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-13b-longlora-16k-ft) \|
	\| Llama-2-13b-longlora-32k-ft \| 13B \| 32768 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-13b-longlora-32k-ft) \|

	### Models with context extension via improved LoRA fine-tuning
	\| Model \| Size \| Context \| Train \| Link \|
	\|:----------------------------\|------\|---------\|-------\|---------------------------------------------------------------------\|
	\| Llama-2-7b-longlora-8k \| 7B \| 8192 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-7b-longlora-8k) \|
	\| Llama-2-7b-longlora-16k \| 7B \| 16384 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-7b-longlora-16k) \|
	\| Llama-2-7b-longlora-32k \| 7B \| 32768 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-7b-longlora-32k) \|
	\| Llama-2-13b-longlora-8k \| 13B \| 8192 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-8k) \|
	\| Llama-2-13b-longlora-16k \| 13B \| 16384 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-16k) \|
	\| Llama-2-13b-longlora-32k \| 13B \| 32768 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-32k) \|
	\| Llama-2-13b-longlora-64k \| 13B \| 65536 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-64k) \|
	\| Llama-2-70b-longlora-32k \| 70B \| 32768 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-70b-longlora-32k) \|
	\| Llama-2-70b-chat-longlora-32k \| 70B \| 32768 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-70b-chat-longlora-32k) \|

	## Training
	### Pre-trained weights
	We use LLaMA2 models as the pre-trained weights and fine-tune them to long context window sizes. Download based on your choices.

	\| Pre-trained weights \|
	\|:-------------------------------------------------------------------------------------\|
	\| [Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) \|
	\|[Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) \|
	\| [Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf) \|
	\| [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) \|
	\| [Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) \|
	\| [Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) \|

	This project also supports GPTNeoX models as the base model architecture. Some candidate pre-trained weights may include [GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b), [Polyglot-ko-12.8B](https://huggingface.co/EleutherAI/polyglot-ko-12.8b) and other variants.

	### Fine-tuning
	```
	torchrun --nproc_per_node=8 fine-tune.py \
	--model_name_or_path path_to/Llama-2-7b-hf \
	--bf16 True \
	--output_dir path_to_saving_checkpoints \
	--cache_dir path_to_cache \
	--model_max_length 8192 \
	--use_flash_attn True \
	--low_rank_training False \
	--num_train_epochs 1 \
	--per_device_train_batch_size 1 \
	--per_device_eval_batch_size 2 \
	--gradient_accumulation_steps 8 \
	--evaluation_strategy "no" \
	--save_strategy "steps" \
	--save_steps 1000 \
	--save_total_limit 2 \
	--learning_rate 2e-5 \
	--weight_decay 0.0 \
	--warmup_steps 20 \
	--lr_scheduler_type "constant_with_warmup" \
	--logging_steps 1 \
	--deepspeed "ds_configs/stage2.json" \
	--tf32 True \
	--max_steps 1000
	```

	- Please remember to change `path_to/Llama-2-7b-hf`, `path_to_saving_checkpoints`, `path_to_cache` to your own directory.
	- Note that you can change `model_max_length` to other values.
	- You could change `ds_configs/stage2.json` to `ds_configs/stage3.json` if you want.
	- Please set `use_flash_attn` as `False` if you use V100 machines or do not install flash attention.
	- You can set `low_rank_training` as `False` if you want to use fully fine-tuning. It will cost more GPU memory and slower, but the performance will be a bit better.
	- When training is finished, to get the full model weight:
	```
	cd path_to_saving_checkpoints && python zero_to_fp32.py . pytorch_model.bin
	```

	### Supervised Fine-tuning
	```
	torchrun --nproc_per_node=8 supervised-fine-tune.py \
	--model_name_or_path path_to_Llama2_chat_models \
	--bf16 True \
	--output_dir path_to_saving_checkpoints \
	--model_max_length 32768 \
	--use_flash_attn True \
	--data_path LongAlpaca-12k.json \
	--low_rank_training True \
	--num_train_epochs 3 \
	--per_device_train_batch_size 1 \
	--per_device_eval_batch_size 2 \
	--gradient_accumulation_steps 1 \
	--evaluation_strategy "no" \
	--save_strategy "steps" \
	--save_steps 1000 \
	--save_total_limit 2 \
	--learning_rate 2e-5 \
	--weight_decay 0.0 \
	--warmup_steps 20 \
	--lr_scheduler_type "constant_with_warmup" \
	--logging_steps 1 \
	--deepspeed "ds_configs/stage2.json" \
	--tf32 True
	```
	- There is no need to make supervised fine-tuning upon the fine-tuned context extended models. It is all right to directly use base model as Llama2-chat models, as the amount of long instruction following data is enough for SFT.
	- Our long instruction following data can be found in [LongAlpaca-12k.json](https://huggingface.co/datasets/Yukang/LongAlpaca-12k).


	### Get trainable weights in low-rank training
	In low-rank training, we set embedding and normalization layers as trainable. Please use the following line to extract the trainable weights `trainable_params.bin` from `pytorch_model.bin`
	```
	python3 get_trainable_weights.py --checkpoint_path path_to_saving_checkpoints --trainable_params "embed,norm"
	```

	### Merge LoRA Weight
	Merge the LoRA weights of `pytorch_model.bin` and trainable parameters `trainable_params.bin`, save the resulting model into your desired path in the Hugging Face format:
	```
	python3 merge_lora_weights_and_save_hf_model.py \
	--base_model path_to/Llama-2-7b-hf \
	--peft_model path_to_saving_checkpoints \
	--context_size 8192 \
	--save_path path_to_saving_merged_model
	```
	For example,
	```
	python3 merge_lora_weights_and_save_hf_model.py \
	--base_model /dataset/pretrained-models/Llama-2-7b-hf \
	--peft_model /dataset/yukangchen/hf_models/lora-models/Llama-2-7b-longlora-8k \
	--context_size 8192 \
	--save_path /dataset/yukangchen/models/Llama-2-7b-longlora-8k-merged
	```


	## Evaluation
	### Perplexity Validation
	To evaluate a model that is trained in the low-rank setting, please set both `base_model` and `peft_model`. `base_model` is the pre-trained weight. `peft_model` is the path to the saved checkpoint, which should contain `trainable_params.bin`, `adapter_model.bin` and `adapter_config.json`. For example,
	```
	python3 eval.py --seq_len 8192 --context_size 8192 --batch_size 1 --base_model path_to/Llama-2-7b-hf --peft_model path_to_saving_checkpoints --data_path pg19/test.bin
	```

	To evaluate a model that is fully fine-tuned, you only need to set `base_model` as the path to the saved checkpoint, which should contain `pytorch_model.bin` and `config.json`. `peft_model` should be ignored.
	```
	python3 eval.py --seq_len 8192 --context_size 8192 --batch_size 1 --base_model path_to_saving_checkpoints --data_path pg19/test.bin
	```

	- Note that `--seq_len` is to set the sequence length for evaluation. `--context_size` is to set the context length of the model during fine-tuning. `--seq_len` should not be larger than `--context_size`.

	- We have already tokenized the validation and test splits of PG19 and proof-pile dataset into `pg19/validation.bin`, `pg19/test.bin`, and `proof-pile/test_sampled_data.bin`, with the tokenizer of LLaMA. `proof-pile/test_sampled_data.bin` contains 128 documents that are randomly sampled from the total proof-pile test split. For each document, it has at least 32768 tokens. We also release the sampled ids in [proof-pile/test_sampled_ids.bin](https://drive.google.com/file/d/1cnzWODLRQYAd7HeugzLCIhaqzaLZv7J5/view?usp=share_link). You can download them from the links below.

	\| Dataset \| Split \| Link \|
	\|:-----------\|------------\|--------------------------------------------------------------------------------------------------------------\|
	\| PG19 \| validation \| [pg19/validation.bin](https://drive.google.com/file/d/1rbJvb0qRIf2mQoN2ON7S93TbTzMnlrN6/view?usp=share_link) \|
	\| PG19 \| test \| [pg19/test.bin](https://drive.google.com/file/d/1QANDMdctpacPAYgS04adDXqByGEq-Ret/view?usp=share_link) \|
	\| Proof-pile \| test \| [proof-pile/test_sampled_data.bin](https://drive.google.com/file/d/1bUI5lPDvrqzY_XXJJ2sSuvZx0Y9AZClE/view?usp=share_link) \|


	### Passkey Retrieval
	We provide a manner to test the passkey retrieval accuracy. For example,
	```
	python3 passkey_retrivial.py \
	--context_size 32768 \
	--base_model path_to/Llama-2-7b-longlora-32k \
	--max_tokens 32768 \
	--interval 1000
	```
	- Note that the `context_size` is the context length during fine-tuning.
	- `max_tokens` is maximum length for the document in passkey retrieval evaluation.
	- `interval` is the interval during the document length increasing. It is a rough number because the document increases by sentences.

	## Demo
	### Local Inference
	To chat with [Llama-2-13b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-13b-chat-longlora-32k-sft) or [Llama-2-70b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-70b-chat-longlora-32k-sft), you need to run `merge_lora_weights_and_save_hf_model.py` first, and then:
	```
	python3 inference.py \
	--base_model path_to_model \
	--question $question \
	--context_size $context_length \
	--max_gen_len $max_gen_len \
	--flash_attn True \
	--material $material_content \
	--material_type $material_type \
	--material_title $material_title
	```
	To ask a question related to a book:
	```
	python3 inference.py \
	--base_model /data/models/Llama-2-13b-chat-longlora-32k-sft \
	--question "Why doesn't Professor Snape seem to like Harry?" \
	--context_size 32768 \
	--max_gen_len 512 \
	--flash_attn True \
	--material "materials/Harry Potter and the Philosophers Stone_section2.txt" \
	--material_type "book" \
	--material_title "Harry Potter and the Philosophers Stone"
	```
	Note that you can ignore `material_type` or `material_title`.

	To ask a question related to a paper:
	```
	python3 inference.py \
	--base_model /data/models/Llama-2-13b-chat-longlora-32k-sft \
	--question "What are the main contributions and novelties of this work?" \
	--context_size 32768 \
	--max_gen_len 512 \
	--flash_attn True \
	--material "materials/paper1.txt" \
	--material_type "paper"
	```

	### Online Demo
	To deploy your own demo run
	```
	python3 demo.py \
	--base_model path_to_model \
	--context_size $context_size \
	--max_gen_len $max_gen_len \
	--flash_attn True
	```
	Example
	```
	python3 demo.py \
	--base_model /data/models/Llama-2-13b-chat-longlora-32k-sft \
	--context_size 32768 \
	--max_gen_len 512 \
	--flash_attn True
	```
	- Note that `flash_attn=True` will make the generation slow but save much GPU memory.

	## Data Generation via Pdf2text
	During our dataset collection, we convert paper and books from pdf to text. The conversion quality has a large influence on the final model quality. We think that this step is non-trivial. We release the tool for the pdf2txt conversion, in the folder `pdf2txt`. It is built upon `pdf2image`, `easyocr`, `ditod` and `detectron2`. Please refer to the [README.md](pdf2txt/README.md) in `pdf2txt` for more details.

	## Citation
	If you find this project useful in your research, please consider citing:

	```
	@article{longlora,
	title={LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models},
	author={Yukang Chen and Shengju Qian and Haotian Tang and Xin Lai and Zhijian Liu and Song Han and Jiaya Jia},
	journal={arXiv:2309.12307},
	year={2023}
	}
	```


	```
	@misc{long-alpaca,
	author = {Yukang Chen and Shaozuo Yu and Shengju Qian and Haotian Tang and Xin Lai and Zhijian Liu and Song Han and Jiaya Jia},
	title = {Long Alpaca: Long-context Instruction-following models},
	year = {2023},
	publisher = {GitHub},
	journal = {GitHub repository},
	howpublished = {\url{https://github.com/dvlab-research/LongLoRA}},
	}
	```
	## Acknowledgement
	- This work is built upon the [LLaMA2](https://ai.meta.com/llama) as the pre-trained models.
	- This work can also be built upon the [GPTNeoX-HF](https://huggingface.co/docs/transformers/model_doc/gpt_neox) which is based upon [EleutherAI/GPTNeoX](https://github.com/EleutherAI/gpt-neox) as the pre-trained model architecture.
	- This work is based on [DeepSpeed](https://github.com/microsoft/DeepSpeed), [peft](https://github.com/huggingface/peft), and [Flash-Attention2](https://github.com/Dao-AILab/flash-attention) for acceleration.
	- Some evaluation code is modified upon [Landmark Attention](https://github.com/epfml/landmark-attention).
	- We use [LongChat](https://github.com/DachengLi1/LongChat) for the retrieval evaluation.

	## License
	- LongLoRA is licensed under the Apache License 2.0. This means that it requires the preservation of copyright and license notices.
	- Data and weights are under CC-BY-NC 4.0 License. They are licensed for research use only, and allowed only non-commercial. Models trained using the dataset should not be used outside of research purposes.