repo_id stringlengths 15 86 | file_path stringlengths 28 180 | content stringlengths 1 1.75M | __index_level_0__ int64 0 0 |
|---|---|---|---|
hf_public_repos/peft/examples | hf_public_repos/peft/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb | from transformers import AutoModelForCausalLM
from peft import get_peft_config, get_peft_model, PrefixTuningConfig, TaskType, PeftType
import torch
from datasets import load_dataset
import os
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
from transformers import default_data_collator, g... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/multi_adapter_examples/PEFT_Multi_LoRA_Inference.ipynb | import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"from huggingface_hub import notebook_login
import torch
notebook_login()from peft import PeftModel
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
model_name = "decapoda-research/llama-7b-hf"
tokenizer = LlamaTokenizer.from_pretrained(mode... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb | # Select CUDA device index
import os
import torch
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from datasets import load_dataset
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model_name = "google/flan-t5-large"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name, load_in_8bit=True)
tokenizer = Auto... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/int8_training/Finetune_opt_bnb_peft.ipynb | import os
import torch
import torch.nn as nn
import bitsandbytes as bnb
from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("facebook/opt-6.7b", load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b")from peft import prepare_... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb | from huggingface_hub import notebook_login
notebook_login()# Select CUDA device index
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
model_name_or_path = "openai/whisper-large-v2"
language = "Marathi"
language_abbr = "mr"
task = "transcribe"
dataset_name = "mozilla-foundation/common_voice_11_0"from datasets impor... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/int8_training/fine_tune_blip2_int8.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/int8_training/peft_adalora_whisper_large_training.py | import argparse
import gc
import json
import logging
import math
import os
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
from random import randint
from typing import Any, Dict, List, Union
# datasets imports
import datasets
# metric imports
import evaluate
import numpy as n... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/int8_training/run_adalora_whisper_int8.sh | accelerate launch --config_file config.yaml peft_adalora_whisper_large_training.py \
--model_name_or_path "openai/whisper-large-v2" \
--language "Marathi" \
--language_abbr "mr" \
--task "transcribe" \
--dataset_name "mozilla-foundation/common_voice_11_0" \
--push_to_hub \
--preprocessing_nu... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/token_classification/requirements.txt | transformers
accelerate
evaluate
tqdm
datasets
Pillow
torchvision | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/token_classification/peft_lora_token_cls.ipynb | # ! rm -r unilm
# ! pip install unilm# ! wget https://guillaumejaume.github.io/FUNSD/dataset.zip
# ! unzip dataset.zip && mv dataset data && rm -rf dataset.zip __MACOSXfrom PIL import Image, ImageDraw, ImageFont
import os
base_path = "/home/sourab/temp/data/dataset"
image = Image.open(os.path.join(base_path, "trainin... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/feature_extraction/requirements.txt | git+https://github.com/huggingface/peft
git+https://github.com/huggingface/accelerate
git+https://github.com/huggingface/transformers
datasets
evaluate
hnswlib
pandas
tqdm
huggingface_hub
wandb | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/feature_extraction/peft_lora_embedding_semantic_search.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/feature_extraction/peft_lora_embedding_semantic_similarity_inference.ipynb | import argparse
import json
import logging
import math
import os
import random
from pathlib import Path
from tqdm import tqdm
import datasets
from datasets import load_dataset, DatasetDict
import evaluate
import torch
from torch import nn
from torch.utils.data import DataLoader
import transformers
from transformers ... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb | from huggingface_hub import notebook_login
notebook_login()from datasets import load_dataset
ds = load_dataset("scene_parse_150", split="train[:150]")ds = ds.train_test_split(test_size=0.1)
train_ds = ds["train"]
test_ds = ds["test"]import json
from huggingface_hub import cached_download, hf_hub_url
repo_id = "huggi... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/semantic_segmentation/README.md | # Fine-tuning for semantic segmentation using LoRA and 🤗 PEFT
[](https://colab.research.google.com/github/huggingface/peft/blob/main/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb)
We provide a notebook (`semantic_segmen... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/fp4_finetuning/finetune_fp4_opt_bnb_peft.py | import os
import torch
import torch.nn as nn
import transformers
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import LoraConfig, get_peft_model
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# -*- coding: utf-8 -*-
"""Finetune-opt-bnb-peft.i... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/conditional_generation/requirements.txt | transformers
accelerate
evaluate
deepspeed
tqdm
datasets | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/conditional_generation/peft_prompt_tuning_seq2seq_with_generate.ipynb | import os
import torch
from transformers import (
AutoTokenizer,
default_data_collator,
AutoModelForSeq2SeqLM,
Seq2SeqTrainingArguments,
Seq2SeqTrainer,
GenerationConfig,
)
from peft import get_peft_model, PromptTuningInit, PromptTuningConfig, TaskType
from datasets import load_dataset
os.envi... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/conditional_generation/accelerate_ds_zero3_cpu_offload_config.yaml | compute_environment: LOCAL_MACHINE
deepspeed_config:
gradient_accumulation_steps: 1
gradient_clipping: 1.0
offload_optimizer_device: none
offload_param_device: none
zero3_init_flag: true
zero3_save_16bit_model: true
zero_stage: 3
distributed_type: DEEPSPEED
downcast_bf16: 'no'
dynamo_backend: 'NO'
fsdp_co... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py | import gc
import os
import sys
import threading
import numpy as np
import psutil
import torch
from accelerate import Accelerator
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/conditional_generation/peft_adalora_seq2seq.py | import os
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup
from peft import AdaLoraConfig, PeftConfig, PeftModel, TaskType, get_peft_model
... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/conditional_generation/peft_prompt_tuning_seq2seq.ipynb | import os
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup
from peft import get_peft_model, PromptTuningConfig, TaskType, PromptTuningInit
from torch.utils.data import DataLoader
from tqdm import tqdm
from datasets import load_dataset
o... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_big_model_inference.ipynb | from transformers import AutoModelForSeq2SeqLM
from peft import PeftModel, PeftConfig
import torch
from datasets import load_dataset
import os
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
from transformers import default_data_collator, get_linear_schedule_with_warmup
from tqdm import t... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py | import os
import torch
from accelerate import Accelerator
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup
from peft import LoraConfig, TaskType, get_pef... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/conditional_generation/peft_ia3_seq2seq.ipynb | from transformers import AutoModelForSeq2SeqLM
import peft
from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, IA3Config, TaskType
import torch
from datasets import load_dataset
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from transformers import AutoTokenizer
from torch.utils.dat... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/conditional_generation/peft_lora_seq2seq.ipynb | from transformers import AutoModelForSeq2SeqLM
from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, LoraConfig, TaskType
import torch
from datasets import load_dataset
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from transformers import AutoTokenizer
from torch.utils.data import Da... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/conditional_generation/peft_prefix_tuning_seq2seq.ipynb | from transformers import AutoModelForSeq2SeqLM
from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, PrefixTuningConfig, TaskType
import torch
from datasets import load_dataset
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
from transformers imp... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/sequence_classification/requirements.txt | transformers
accelerate
evaluate
tqdm
datasets | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/sequence_classification/P_Tuning.ipynb | import argparse
import os
import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader
from peft import (
get_peft_config,
get_peft_model,
get_peft_model_state_dict,
set_peft_model_state_dict,
PeftType,
PrefixTuningConfig,
PromptEncoderConfig,
)
import evaluate
from d... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/sequence_classification/Prompt_Tuning.ipynb | import argparse
import os
import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader
from peft import (
get_peft_config,
get_peft_model,
get_peft_model_state_dict,
set_peft_model_state_dict,
PeftType,
PrefixTuningConfig,
PromptEncoderConfig,
PromptTuningConfig,
)... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/sequence_classification/LoRA.ipynb | import argparse
import os
import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader
from peft import (
get_peft_config,
get_peft_model,
get_peft_model_state_dict,
set_peft_model_state_dict,
LoraConfig,
PeftType,
PrefixTuningConfig,
PromptEncoderConfig,
)
import... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/sequence_classification/prefix_tuning.ipynb | import argparse
import os
import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader
from peft import (
get_peft_config,
get_peft_model,
get_peft_model_state_dict,
set_peft_model_state_dict,
PeftType,
PrefixTuningConfig,
PromptEncoderConfig,
)
import evaluate
from d... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/sequence_classification/IA3.ipynb | import argparse
import os
import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader
import peft
import evaluate
from datasets import load_dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from tqdm import tqdmbatch_si... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/sequence_classification/peft_no_lora_accelerate.py | import argparse
import evaluate
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_li... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/lora_dreambooth/requirements.txt | transformers
accelerate
evaluate
tqdm
datasets
diffusers
Pillow
torchvision
huggingface_hub
safetensors
wandb | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/lora_dreambooth/train_dreambooth.py | import argparse
import gc
import hashlib
import itertools
import logging
import math
import os
import threading
import warnings
from pathlib import Path
from typing import Optional
import datasets
import diffusers
import numpy as np
import psutil
import torch
import torch.nn.functional as F
import torch.utils.checkpoi... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/lora_dreambooth/convert_peft_sd_lora_to_kohya_ss.py | import argparse
import os
from typing import Dict
import torch
from diffusers import UNet2DConditionModel
from safetensors.torch import save_file
from transformers import CLIPTextModel
from peft import PeftModel, get_peft_model_state_dict
# Default kohya_ss LoRA replacement modules
# https://github.com/kohya-ss/sd-... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/lora_dreambooth/lora_dreambooth_inference.ipynb | import argparse
import gc
import hashlib
import itertools
import logging
import math
import os
import threading
import warnings
from pathlib import Path
from typing import Optional
import psutil
import json
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.utils.data import Dataset
... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/lora_dreambooth/colab_notebook.ipynb | %cd "peft-lora-sd-dreambooth"
!pip install -r requirements.txt | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/lora_dreambooth/convert_kohya_ss_sd_lora_to_peft.py | import argparse
import os
import re
from typing import Callable, List, Optional, Union
import safetensors
import torch
import torch.nn as nn
from diffusers import UNet2DConditionModel
from transformers import CLIPTextModel
from peft import LoraConfig, get_peft_model, get_peft_model_state_dict, set_peft_model_state_di... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/image_classification/README.md | # Fine-tuning for image classification using LoRA and 🤗 PEFT
[](https://colab.research.google.com/github/huggingface/peft/blob/main/examples/image_classification/image_classification_peft_lora.ipynb)
We provide a notebook (`image_classificati... | 0 |
hf_public_repos/peft/examples | hf_public_repos/peft/examples/image_classification/image_classification_peft_lora.ipynb | from huggingface_hub import notebook_login
notebook_login()import transformers
import accelerate
import peftprint(f"Transformers version: {transformers.__version__}")
print(f"Accelerate version: {accelerate.__version__}")
print(f"PEFT version: {peft.__version__}")model_checkpoint = "google/vit-base-patch16-224-in21k" ... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/test_decoder_models.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/test_adaption_prompt.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/test_encoder_decoder_models.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/test_hub_features.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/test_common_gpu.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/test_gpu_examples.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/test_auto.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/test_config.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/testing_utils.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/test_custom_models.py | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/test_feature_extraction_models.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/testing_common.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft | hf_public_repos/peft/tests/test_stablediffusion.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft | hf_public_repos/peft/docs/README.md | <!---
Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or ... | 0 |
hf_public_repos/peft | hf_public_repos/peft/docs/Makefile | # Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
SOURCEDIR = source
BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" ... | 0 |
hf_public_repos/peft/docs | hf_public_repos/peft/docs/source/install.mdx | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | 0 |
hf_public_repos/peft/docs | hf_public_repos/peft/docs/source/index.mdx | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | 0 |
hf_public_repos/peft/docs | hf_public_repos/peft/docs/source/_toctree.yml | - title: Get started
sections:
- local: index
title: 🤗 PEFT
- local: quicktour
title: Quicktour
- local: install
title: Installation
- title: Task guides
sections:
- local: task_guides/image_classification_lora
title: Image classification using LoRA
- local: task_guides/seq2seq-prefix-tu... | 0 |
hf_public_repos/peft/docs | hf_public_repos/peft/docs/source/quicktour.mdx | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | 0 |
hf_public_repos/peft/docs | hf_public_repos/peft/docs/source/_config.py | # docstyle-ignore
INSTALL_CONTENT = """
# PEFT installation
! pip install peft accelerate transformers
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/peft.git
"""
| 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/accelerate/deepspeed-zero3-offload.mdx | # DeepSpeed
[DeepSpeed](https://www.deepspeed.ai/) is a library designed for speed and scale for distributed training of large models with billions of parameters. At its core is the Zero Redundancy Optimizer (ZeRO) that shards optimizer states (ZeRO-1), gradients (ZeRO-2), and parameters (ZeRO-3) across data parallel ... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/accelerate/fsdp.mdx | # Fully Sharded Data Parallel
[Fully sharded data parallel](https://pytorch.org/docs/stable/fsdp.html) (FSDP) is developed for distributed training of large pretrained models up to 1T parameters. FSDP achieves this by sharding the model parameters, gradients, and optimizer states across data parallel processes and it ... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/task_guides/seq2seq-prefix-tuning.mdx | # Prefix tuning for conditional generation
[[open-in-colab]]
Prefix tuning is an additive method where only a sequence of continuous task-specific vectors is attached to the beginning of the input, or *prefix*. Only the prefix parameters are optimized and added to the hidden states in every layer of the model. The to... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/task_guides/semantic-similarity-lora.md | # LoRA for semantic similarity tasks
Low-Rank Adaptation (LoRA) is a reparametrization method that aims to reduce the number of trainable parameters with low-rank representations. The weight matrix is broken down into low-rank matrices that are trained and updated. All the pretrained model parameters remain frozen. Af... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/task_guides/int8-asr.mdx | # int8 training for automatic speech recognition
Quantization reduces the precision of floating point data types, decreasing the memory required to store model weights. However, quantization degrades inference performance because you lose information when you reduce the precision. 8-bit or `int8` quantization uses onl... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/task_guides/dreambooth_lora.mdx | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/task_guides/clm-prompt-tuning.mdx | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/task_guides/semantic_segmentation_lora.mdx | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/task_guides/image_classification_lora.mdx | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/task_guides/token-classification-lora.mdx | # LoRA for token classification
Low-Rank Adaptation (LoRA) is a reparametrization method that aims to reduce the number of trainable parameters with low-rank representations. The weight matrix is broken down into low-rank matrices that are trained and updated. All the pretrained model parameters remain frozen. After t... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/task_guides/ptuning-seq-classification.mdx | # P-tuning for sequence classification
It is challenging to finetune large language models for downstream tasks because they have so many parameters. To work around this, you can use *prompts* to steer the model toward a particular downstream task without fully finetuning a model. Typically, these prompts are handcraf... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/conceptual_guides/ia3.mdx | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/conceptual_guides/lora.mdx | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/conceptual_guides/prompting.mdx | # Prompting
Training large pretrained language models is very time-consuming and compute-intensive. As they continue to grow in size, there is increasing interest in more efficient training methods such as *prompting*. Prompting primes a frozen pretrained model for a specific downstream task by including a text prompt... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/package_reference/peft_model.mdx | # Models
[`PeftModel`] is the base model class for specifying the base Transformer model and configuration to apply a PEFT method to. The base `PeftModel` contains methods for loading and saving models from the Hub, and supports the [`PromptEncoder`] for prompt learning.
## PeftModel
[[autodoc]] PeftModel
- all
... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/package_reference/tuners.mdx | # Tuners
Each tuner (or PEFT method) has a configuration and model.
## LoRA
For finetuning a model with LoRA.
[[autodoc]] LoraConfig
[[autodoc]] LoraModel
[[autodoc]] tuners.lora.LoraLayer
[[autodoc]] tuners.lora.Linear
## P-tuning
[[autodoc]] tuners.p_tuning.PromptEncoderConfig
[[autodoc]] tuners.p_tuning.Pr... | 0 |
hf_public_repos/peft/docs/source | hf_public_repos/peft/docs/source/package_reference/config.mdx | # Configuration
The configuration classes stores the configuration of a [`PeftModel`], PEFT adapter models, and the configurations of [`PrefixTuning`], [`PromptTuning`], and [`PromptEncoder`]. They contain methods for saving and loading model configurations from the Hub, specifying the PEFT method to use, type of task... | 0 |
hf_public_repos/peft | hf_public_repos/peft/scripts/stale.py | # Copyright 2023 The HuggingFace Team, the AllenNLP library authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 0 |
hf_public_repos/peft | hf_public_repos/peft/scripts/log_reports.py | import json, os
from pathlib import Path
from datetime import date
from tabulate import tabulate
failed = []
passed = []
group_info = []
total_num_failed = 0
empty_file = False or len(list(Path().glob("*.log"))) == 0
for log in Path().glob("*.log"):
section_num_failed = 0
with open(log, "r") as f:
nb... | 0 |
hf_public_repos/peft/docker | hf_public_repos/peft/docker/peft-cpu/Dockerfile | # Builds GPU docker image of PyTorch
# Uses multi-staged approach to reduce size
# Stage 1
# Use base conda image to reduce time
FROM continuumio/miniconda3:latest AS compile-image
# Specify py version
ENV PYTHON_VERSION=3.8
# Install apt libs - copied from https://github.com/huggingface/accelerate/blob/main/docker/acc... | 0 |
hf_public_repos/peft/docker | hf_public_repos/peft/docker/peft-gpu/Dockerfile | # Builds GPU docker image of PyTorch
# Uses multi-staged approach to reduce size
# Stage 1
# Use base conda image to reduce time
FROM continuumio/miniconda3:latest AS compile-image
# Specify py version
ENV PYTHON_VERSION=3.8
# Install apt libs - copied from https://github.com/huggingface/accelerate/blob/main/docker/acc... | 0 |
hf_public_repos/peft/src | hf_public_repos/peft/src/peft/__init__.py | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not ... | 0 |
hf_public_repos/peft/src | hf_public_repos/peft/src/peft/auto.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/src | hf_public_repos/peft/src/peft/mapping.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/src | hf_public_repos/peft/src/peft/import_utils.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/src | hf_public_repos/peft/src/peft/peft_model.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/tuners/__init__.py | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not u... | 0 |
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/tuners/adalora.py | import re
import warnings
from dataclasses import dataclass, field
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.pytorch_utils import Conv1D
from ..import_utils import is_bnb_4bit_available, is_bnb_available
from ..utils import (
TRANSFORMERS_MODE... | 0 |
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/tuners/p_tuning.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/tuners/prompt_tuning.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/tuners/prefix_tuning.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/tuners/adaption_prompt.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/tuners/lora.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/tuners/ia3.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/utils/save_and_load.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/utils/__init__.py | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all
# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not u... | 0 |
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/utils/config.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
hf_public_repos/peft/src/peft | hf_public_repos/peft/src/peft/utils/other.py | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 0 |
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