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<h1 align="center"> <p>🤗 PEFT</p></h1>
<h3 align="center">
    <p>State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods</p>
</h3>

Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. Fine-tuning large-scale PLMs is often prohibitively costly. In this regard, PEFT methods only fine-tune a small number of (extra) model parameters, thereby greatly decreasing the computational and storage costs. Recent State-of-the-Art PEFT techniques achieve performance comparable to that of full fine-tuning. 

Seamlessly integrated with 🤗 Accelerate for large scale models leveraging DeepSpeed and Big Model Inference. 

Supported methods:

1. LoRA: [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/abs/2106.09685)
2. Prefix Tuning: [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://aclanthology.org/2021.acl-long.353/), [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://arxiv.org/pdf/2110.07602.pdf)
3. P-Tuning: [GPT Understands, Too](https://arxiv.org/abs/2103.10385)
4. Prompt Tuning: [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691)
5. AdaLoRA: [Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning](https://arxiv.org/abs/2303.10512)  
6. $(IA)^3$: [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://arxiv.org/abs/2205.05638)
7. MultiTask Prompt Tuning: [Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning](https://arxiv.org/abs/2303.02861)
8. LoHa: [FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning](https://arxiv.org/abs/2108.06098)
9. LoKr: [KronA: Parameter Efficient Tuning with Kronecker Adapter](https://arxiv.org/abs/2212.10650) based on [Navigating Text-To-Image Customization:From LyCORIS Fine-Tuning to Model Evaluation](https://arxiv.org/abs/2309.14859) implementation
10. LoftQ: [LoftQ: LoRA-Fine-Tuning-aware Quantization for Large Language Models](https://arxiv.org/abs/2310.08659)
11. OFT: [Controlling Text-to-Image Diffusion by Orthogonal Finetuning](https://arxiv.org/abs/2306.07280)

## Getting started

```python
from transformers import AutoModelForSeq2SeqLM
from peft import get_peft_config, get_peft_model, LoraConfig, TaskType
model_name_or_path = "bigscience/mt0-large"
tokenizer_name_or_path = "bigscience/mt0-large"

peft_config = LoraConfig(
    task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1
)

model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# output: trainable params: 2359296 || all params: 1231940608 || trainable%: 0.19151053100118282
```

## Use Cases

### Get comparable performance to full finetuning by adapting LLMs to downstream tasks using consumer hardware

GPU memory required for adapting LLMs on the few-shot dataset [`ought/raft/twitter_complaints`](https://huggingface.co/datasets/ought/raft/viewer/twitter_complaints). Here, settings considered
are full finetuning, PEFT-LoRA using plain PyTorch and PEFT-LoRA using DeepSpeed with CPU Offloading. 

Hardware: Single A100 80GB GPU with CPU RAM above 64GB

|   Model         | Full Finetuning | PEFT-LoRA PyTorch  | PEFT-LoRA DeepSpeed with CPU Offloading |
| --------- | ---- | ---- | ---- |
| bigscience/T0_3B (3B params) | 47.14GB GPU / 2.96GB CPU  | 14.4GB GPU / 2.96GB CPU | 9.8GB GPU / 17.8GB CPU |
| bigscience/mt0-xxl (12B params) | OOM GPU | 56GB GPU / 3GB CPU | 22GB GPU / 52GB CPU |
| bigscience/bloomz-7b1 (7B params) | OOM GPU | 32GB GPU / 3.8GB CPU | 18.1GB GPU / 35GB CPU |

Performance of PEFT-LoRA tuned [`bigscience/T0_3B`](https://huggingface.co/bigscience/T0_3B) on [`ought/raft/twitter_complaints`](https://huggingface.co/datasets/ought/raft/viewer/twitter_complaints) leaderboard. 
A point to note is that we didn't try to squeeze performance by playing around with input instruction templates, LoRA hyperparams and other training related hyperparams. Also, we didn't use the larger 13B [mt0-xxl](https://huggingface.co/bigscience/mt0-xxl) model.
So, we are already seeing comparable performance to SoTA with parameter efficient tuning. Also, the final additional checkpoint size is just `19MB` in comparison to `11GB` size of the backbone [`bigscience/T0_3B`](https://huggingface.co/bigscience/T0_3B) model, but one still has to load the original full size model.

|   Submission Name        | Accuracy |
| --------- | ---- |
| Human baseline (crowdsourced) |	0.897 |
| Flan-T5 | 0.892 |
| lora-t0-3b | 0.863 |

**Therefore, we can see that performance comparable to SoTA is achievable by PEFT methods with consumer hardware such as 16GB and 24GB GPUs.**

An insightful blogpost explaining the advantages of using PEFT for fine-tuning FlanT5-XXL: [https://www.philschmid.de/fine-tune-flan-t5-peft](https://www.philschmid.de/fine-tune-flan-t5-peft)

### Parameter Efficient Tuning of Diffusion Models

GPU memory required by different settings during training is given below. The final checkpoint size is `8.8 MB`.

Hardware: Single A100 80GB GPU with CPU RAM above 64GB

|   Model         | Full Finetuning | PEFT-LoRA  | PEFT-LoRA with Gradient Checkpointing  |
| --------- | ---- | ---- | ---- |
| CompVis/stable-diffusion-v1-4 | 27.5GB GPU / 3.97GB CPU | 15.5GB GPU / 3.84GB CPU | 8.12GB GPU / 3.77GB CPU | 


**Training**
An example of using LoRA for parameter efficient dreambooth training is given in [`examples/lora_dreambooth/train_dreambooth.py`](examples/lora_dreambooth/train_dreambooth.py)

```bash
export MODEL_NAME= "CompVis/stable-diffusion-v1-4" #"stabilityai/stable-diffusion-2-1"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"

accelerate launch train_dreambooth.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --instance_data_dir=$INSTANCE_DIR \
  --class_data_dir=$CLASS_DIR \
  --output_dir=$OUTPUT_DIR \
  --train_text_encoder \
  --with_prior_preservation --prior_loss_weight=1.0 \
  --instance_prompt="a photo of sks dog" \
  --class_prompt="a photo of dog" \
  --resolution=512 \
  --train_batch_size=1 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --num_class_images=200 \
  --use_lora \
  --lora_r 16 \
  --lora_alpha 27 \
  --lora_text_encoder_r 16 \
  --lora_text_encoder_alpha 17 \
  --learning_rate=1e-4 \
  --gradient_accumulation_steps=1 \
  --gradient_checkpointing \
  --max_train_steps=800
```

Try out the 🤗 Gradio Space which should run seamlessly on a T4 instance:
[smangrul/peft-lora-sd-dreambooth](https://huggingface.co/spaces/smangrul/peft-lora-sd-dreambooth).

![peft lora dreambooth gradio space](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/peft_lora_dreambooth_gradio_space.png)

**NEW** ✨ Multi Adapter support and combining multiple LoRA adapters in a weighted combination 
![peft lora dreambooth weighted adapter](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/weighted_adapter_dreambooth_lora.png)

**NEW** ✨ Dreambooth training for Stable Diffusion using LoHa and LoKr adapters [`examples/stable_diffusion/train_dreambooth.py`](examples/stable_diffusion/train_dreambooth.py)

### Parameter Efficient Tuning of LLMs for RLHF components such as Ranker and Policy
- Here is an example in [trl](https://github.com/lvwerra/trl) library using PEFT+INT8 for tuning policy model: [gpt2-sentiment_peft.py](https://github.com/lvwerra/trl/blob/main/examples/sentiment/scripts/gpt2-sentiment_peft.py) and corresponding [Blog](https://huggingface.co/blog/trl-peft)
- Example using PEFT for Instruction finetuning, reward model and policy : [stack_llama](https://github.com/lvwerra/trl/tree/main/examples/research_projects/stack_llama/scripts) and corresponding [Blog](https://huggingface.co/blog/stackllama) 

### INT8 training of large models in Colab using PEFT LoRA and bitsandbytes

- Here is now a demo on how to fine tune [OPT-6.7b](https://huggingface.co/facebook/opt-6.7b) (14GB in fp16) in a Google Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o?usp=sharing)

- Here is now a demo on how to fine tune [whisper-large](https://huggingface.co/openai/whisper-large-v2) (1.5B params) (14GB in fp16) in a Google Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DOkD_5OUjFa0r5Ik3SgywJLJtEo2qLxO?usp=sharing) and [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1vhF8yueFqha3Y3CpTHN6q9EVcII9EYzs?usp=sharing)

### Save compute and storage even for medium and small models

Save storage by avoiding full finetuning of models on each of the downstream tasks/datasets,
With PEFT methods, users only need to store tiny checkpoints in the order of `MBs` all the while retaining 
performance comparable to full finetuning.

An example of using LoRA for the task of adapting `LayoutLMForTokenClassification` on `FUNSD` dataset is given in `~examples/token_classification/PEFT_LoRA_LayoutLMForTokenClassification_on_FUNSD.py`. We can observe that with only `0.62 %` of parameters being trainable, we achieve performance (F1 0.777) comparable to full finetuning (F1 0.786) (without any hyperparam tuning runs for extracting more performance), and the checkpoint of this is only `2.8MB`. Now, if there are `N` such datasets, just have these PEFT models one for each dataset and save a lot of storage without having to worry about the problem of catastrophic forgetting or overfitting of backbone/base model.

Another example is fine-tuning [`roberta-large`](https://huggingface.co/roberta-large) on [`MRPC` GLUE](https://huggingface.co/datasets/glue/viewer/mrpc) dataset using different PEFT methods. The notebooks are given in `~examples/sequence_classification`. 


## PEFT + 🤗 Accelerate

PEFT models work with 🤗 Accelerate out of the box. Use 🤗 Accelerate for Distributed training on various hardware such as GPUs, Apple Silicon devices, etc during training.
Use 🤗 Accelerate for inferencing on consumer hardware with small resources.

### Example of PEFT model training using 🤗 Accelerate's DeepSpeed integration

DeepSpeed version required `v0.8.0`. An example is provided in `~examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py`. 
  a. First, run `accelerate config --config_file ds_zero3_cpu.yaml` and answer the questionnaire. 
  Below are the contents of the config file.
  ```yaml
  compute_environment: LOCAL_MACHINE
  deepspeed_config:
    gradient_accumulation_steps: 1
    gradient_clipping: 1.0
    offload_optimizer_device: cpu
    offload_param_device: cpu
    zero3_init_flag: true
    zero3_save_16bit_model: true
    zero_stage: 3
  distributed_type: DEEPSPEED
  downcast_bf16: 'no'
  dynamo_backend: 'NO'
  fsdp_config: {}
  machine_rank: 0
  main_training_function: main
  megatron_lm_config: {}
  mixed_precision: 'no'
  num_machines: 1
  num_processes: 1
  rdzv_backend: static
  same_network: true
  use_cpu: false
  ```
  b. run the below command to launch the example script
  ```bash
  accelerate launch --config_file ds_zero3_cpu.yaml examples/peft_lora_seq2seq_accelerate_ds_zero3_offload.py
  ```

  c. output logs:
  ```bash
  GPU Memory before entering the train : 1916
  GPU Memory consumed at the end of the train (end-begin): 66
  GPU Peak Memory consumed during the train (max-begin): 7488
  GPU Total Peak Memory consumed during the train (max): 9404
  CPU Memory before entering the train : 19411
  CPU Memory consumed at the end of the train (end-begin): 0
  CPU Peak Memory consumed during the train (max-begin): 0
  CPU Total Peak Memory consumed during the train (max): 19411
  epoch=4: train_ppl=tensor(1.0705, device='cuda:0') train_epoch_loss=tensor(0.0681, device='cuda:0')
  100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:27<00:00,  3.92s/it]
  GPU Memory before entering the eval : 1982
  GPU Memory consumed at the end of the eval (end-begin): -66
  GPU Peak Memory consumed during the eval (max-begin): 672
  GPU Total Peak Memory consumed during the eval (max): 2654
  CPU Memory before entering the eval : 19411
  CPU Memory consumed at the end of the eval (end-begin): 0
  CPU Peak Memory consumed during the eval (max-begin): 0
  CPU Total Peak Memory consumed during the eval (max): 19411
  accuracy=100.0
  eval_preds[:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']
  dataset['train'][label_column][:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']
  ```

### Example of PEFT model inference using 🤗 Accelerate's Big Model Inferencing capabilities
An example is provided in [this notebook](https://github.com/huggingface/peft/blob/main/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb).


## Models support matrix

Find models that are supported out of the box below. Note that PEFT works with almost all models -- if it is not listed, you just need to [do some manual configuration](https://huggingface.co/docs/peft/developer_guides/custom_models).

### Causal Language Modeling
| Model        | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | IA3 |
|--------------| ---- | ---- | ---- | ----  | ----  |
| GPT-2        | ✅  | ✅  | ✅  | ✅  | ✅  |
| Bloom        | ✅  | ✅  | ✅  | ✅  | ✅  |
| OPT          | ✅  | ✅  | ✅  | ✅  | ✅  |
| GPT-Neo      | ✅  | ✅  | ✅  | ✅  | ✅  |
| GPT-J        | ✅  | ✅  | ✅  | ✅  | ✅  |
| GPT-NeoX-20B | ✅  | ✅  | ✅  | ✅  | ✅  |
| LLaMA        | ✅  | ✅  | ✅  | ✅  | ✅  |
| ChatGLM      | ✅  | ✅  | ✅  | ✅  | ✅  |
| Mistral      | ✅  |    |    |    |    |

### Conditional Generation
|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | IA3 |
| --------- | ---- | ---- | ---- | ---- | ---- |
| T5        | ✅   | ✅   | ✅   | ✅   | ✅   |
| BART      | ✅   | ✅   | ✅   | ✅   | ✅   |

### Sequence Classification
|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | IA3 |
| --------- | ---- | ---- | ---- | ----  | ----  |
| BERT           | ✅  | ✅  | ✅  | ✅  |  ✅  |  
| RoBERTa        | ✅  | ✅  | ✅  | ✅  | ✅  |
| GPT-2          | ✅  | ✅  | ✅  | ✅  |   |
| Bloom          | ✅  | ✅  | ✅  | ✅  |   |
| OPT            | ✅  | ✅  | ✅  | ✅  |   |
| GPT-Neo        | ✅  | ✅  | ✅  | ✅  |   |
| GPT-J          | ✅  | ✅  | ✅  | ✅  |   |
| Deberta        | ✅  |     | ✅  | ✅  |   | 
| Deberta-v2     | ✅  |     | ✅  | ✅  |   |

### Token Classification
|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | IA3 |
| --------- | ---- | ---- | ---- | ----  | ----  |
| BERT           | ✅  | ✅  |   |   |   |  
| RoBERTa        | ✅  | ✅  |   |   |   |
| GPT-2          | ✅  | ✅  |   |   |   |
| Bloom          | ✅  | ✅  |   |   |   |
| OPT            | ✅  | ✅  |   |   |   |
| GPT-Neo        | ✅  | ✅  |   |   |   |
| GPT-J          | ✅  | ✅  |   |   |   |
| Deberta        | ✅  |     |   |   |   |
| Deberta-v2     | ✅  |     |   |   |   |

### Text-to-Image Generation

|   Model         | LoRA | LoHa | LoKr | OFT | Prefix Tuning  | P-Tuning | Prompt Tuning  | IA3 |
| --------- | ---- | ---- | ---- | ---- | ---- | ---- | ----  | ----  |
| Stable Diffusion           | ✅  | ✅  | ✅  | ✅  |  |   |   |


### Image Classification

|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | IA3 |
| --------- | ---- | ---- | ---- | ----  | ----  |
| ViT           | ✅  |   |   |   |    | 
| Swin           | ✅  |   |   |   |   |  

### Image to text (Multi-modal models)

|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | IA3
| --------- | ---- | ---- | ---- | ----  | ----  |
| Blip-2           | ✅  |   |   |   |   |

___Note that we have tested LoRA for [ViT](https://huggingface.co/docs/transformers/model_doc/vit) and [Swin](https://huggingface.co/docs/transformers/model_doc/swin) for fine-tuning on image classification. However, it should be possible to use LoRA for any compatible model [provided](https://huggingface.co/models?pipeline_tag=image-classification&sort=downloads&search=vit) by 🤗 Transformers. Check out the respective
examples to learn more. If you run into problems, please open an issue.___

The same principle applies to our [segmentation models](https://huggingface.co/models?pipeline_tag=image-segmentation&sort=downloads) as well. 

### Semantic Segmentation

|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | IA3 |
| --------- | ---- | ---- | ---- | ----  | ----  |
| SegFormer           | ✅  |   |   |   |   | 


## Caveats:

1. Below is an example of using PyTorch FSDP for training. However, it doesn't lead to 
any GPU memory savings. Please refer issue [[FSDP] FSDP with CPU offload consumes 1.65X more GPU memory when training models with most of the params frozen](https://github.com/pytorch/pytorch/issues/91165). 

  ```python
  from peft.utils.other import fsdp_auto_wrap_policy

  ...

  if os.environ.get("ACCELERATE_USE_FSDP", None) is not None:
      accelerator.state.fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(model)

  model = accelerator.prepare(model)
  ```

  Example of parameter efficient tuning with [`mt0-xxl`](https://huggingface.co/bigscience/mt0-xxl) base model using 🤗 Accelerate is provided in `~examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py`. 
  a. First, run `accelerate config --config_file fsdp_config.yaml` and answer the questionnaire. 
  Below are the contents of the config file.
  ```yaml
  command_file: null
  commands: null
  compute_environment: LOCAL_MACHINE
  deepspeed_config: {}
  distributed_type: FSDP
  downcast_bf16: 'no'
  dynamo_backend: 'NO'
  fsdp_config:
    fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
    fsdp_backward_prefetch_policy: BACKWARD_PRE
    fsdp_offload_params: true
    fsdp_sharding_strategy: 1
    fsdp_state_dict_type: FULL_STATE_DICT
    fsdp_transformer_layer_cls_to_wrap: T5Block
  gpu_ids: null
  machine_rank: 0
  main_process_ip: null
  main_process_port: null
  main_training_function: main
  megatron_lm_config: {}
  mixed_precision: 'no'
  num_machines: 1
  num_processes: 2
  rdzv_backend: static
  same_network: true
  tpu_name: null
  tpu_zone: null
  use_cpu: false
  ```
  b. run the below command to launch the example script
  ```bash
  accelerate launch --config_file fsdp_config.yaml examples/peft_lora_seq2seq_accelerate_fsdp.py
  ```

2. When using ZeRO3 with zero3_init_flag=True, if you find the gpu memory increase with training steps. we might need to update deepspeed after [deepspeed commit 42858a9891422abc](https://github.com/microsoft/DeepSpeed/commit/42858a9891422abcecaa12c1bd432d28d33eb0d4) . The related issue is [[BUG] Peft Training with Zero.Init() and Zero3 will increase GPU memory every forward step ](https://github.com/microsoft/DeepSpeed/issues/3002)

## 🤗 PEFT as a utility library

### Injecting adapters directly into the model

Inject trainable adapters on any `torch` model using `inject_adapter_in_model` method. Note the method will make no further change to the model.

```python
import torch 
from peft import inject_adapter_in_model, LoraConfig

class DummyModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.embedding = torch.nn.Embedding(10, 10)
        self.linear = torch.nn.Linear(10, 10)
        self.lm_head = torch.nn.Linear(10, 10)
    
    def forward(self, input_ids):
        x = self.embedding(input_ids)
        x = self.linear(x)
        x = self.lm_head(x)
        return x

lora_config = LoraConfig(
    lora_alpha=16,
    lora_dropout=0.1,
    r=64,
    bias="none",
    target_modules=["linear"],
)

model = DummyModel()
model = inject_adapter_in_model(lora_config, model)

dummy_inputs = torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]])
dummy_outputs = model(dummy_inputs)
```

Learn more about the [low level API in the docs](https://huggingface.co/docs/peft/developer_guides/low_level_api).

### Mixing different adapter types

Ususally, it is not possible to combine different adapter types in the same model, e.g. combining LoRA with AdaLoRA, LoHa, or LoKr. Using a mixed model, this can, however, be achieved:

```python
from peft import PeftMixedModel

model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-OPTForCausalLM").eval()
peft_model = PeftMixedModel.from_pretrained(model, <path-to-adapter-0>, "adapter0")
peft_model.load_adapter(<path-to-adapter-1>, "adapter1")
peft_model.set_adapter(["adapter0", "adapter1"])
result = peft_model(**inputs)
```

The main intent is to load already trained adapters and use this only for inference. However, it is also possible to create a PEFT model for training by passing `mixed=True` to `get_peft_model`:

```python
from peft import get_peft_model, LoraConfig, LoKrConfig

base_model = ...
config0 = LoraConfig(...)
config1 = LoKrConfig(...)
peft_model = get_peft_model(base_model, config0, "adapter0", mixed=True)
peft_model.add_adapter(config1, "adapter1")
peft_model.set_adapter(["adapter0", "adapter1"])
for batch in dataloader:
    ...
```

## Contributing

If you would like to contribute to PEFT, please check out our [contributing guide](https://huggingface.co/docs/peft/developer_guides/contributing).

## Citing 🤗 PEFT

If you use 🤗 PEFT in your publication, please cite it by using the following BibTeX entry.

```bibtex
@Misc{peft,
  title =        {PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods},
  author =       {Sourab Mangrulkar and Sylvain Gugger and Lysandre Debut and Younes Belkada and Sayak Paul and Benjamin Bossan},
  howpublished = {\url{https://github.com/huggingface/peft}},
  year =         {2022}
}
```
