# Technical Documentation: huggingface/notebooks > โ„น๏ธ **Provenance:** In-Tree Documentation (19 Chapters) ยท [CodeWiki Reference](https://codewiki.google/github.com/huggingface/notebooks) ยท Recency: Active (< 180 days) ## File: README.md # notebooks Notebooks using the Hugging Face libraries ๐Ÿค— --- ## File: diffusers/README.md # Diffusers Notebooks Notebooks for the HuggingFace Diffusers library ## ๐Ÿ““ Examples | Notebook | Colab | Description | |------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------| | [Getting started with Diffusers](https://github.com/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) | First look at the library with a detailed model inference example. | | [Training a DDPM model](https://github.com/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) | An example of training a [DDPM model](https://arxiv.org/abs/2006.11239) from scratch on a dataset of butterfly images. | | [Stable Diffusion with Diffusers](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb) | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb) | How to use Stable Diffusion model for text-to-image generation using diffusers library. | --- ## File: examples/idefics/README.md # IDEFICS Demos/examples ## Inference - [Normal inference](inference.py) (needs ~20GB GPU memory) - [4bit quantized inference](inference_4bit.py) (needs ~7GB GPU memory) ## Finetuning The following demos use the Image captioning task: - [PEFT (LORA) finetuning (notebook)](finetune_image_captioning_peft.ipynb) (fits on Google colab) - [Normal finetuning](finetune_image_captioning.py) (needs ~40GB GPU memory) --- ## File: examples/idefics/idefics_zero3_finetuning/requirements.txt torch deepspeed transformers>4.33.3 pillow sentencepiece protobuf datasets accelerate mpi4py --- ## File: sagemaker/README.md # Hugging Face Transformers Amazon SageMaker Examples Example Jupyter notebooks that demonstrate how to build, train, and deploy [Hugging Face Transformers](https://github.com/huggingface/transformers) using [Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html) and the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/). ## ๐Ÿ› ๏ธ Setup The quickest setup to run example notebooks includes: - An [AWS account](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-account.html) - Proper [IAM User and Role](http://docs.aws.amazon.com/sagemaker/latest/dg/authentication-and-access-control.html) setup - An [Amazon SageMaker Notebook Instance](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html) - An [S3 bucket](http://docs.aws.amazon.com/sagemaker/latest/dg/gs-config-permissions.html) ## ๐Ÿ““ Examples | Notebook | Type | Description | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [01 Getting started with PyTorch](https://github.com/huggingface/notebooks/blob/main/sagemaker/01_getting_started_pytorch/sagemaker-notebook.ipynb) | Training | Getting started end-to-end example on how to fine-tune a pre-trained Hugging Face Transformer for Text-Classification using PyTorch | | [02 getting started with TensorFlow](https://github.com/huggingface/notebooks/blob/main/sagemaker/02_getting_started_tensorflow/sagemaker-notebook.ipynb) | Training | Getting started end-to-end example on how to fine-tune a pre-trained Hugging Face Transformer for Text-Classification using TensorFlow | | [03 Distributed Training: Data Parallelism](https://github.com/huggingface/notebooks/blob/main/sagemaker/03_distributed_training_data_parallelism/sagemaker-notebook.ipynb) | Training | End-to-end example on how to use distributed training with data-parallelism strategy for fine-tuning a pre-trained Hugging Face Transformer for Question-Answering using Amazon SageMaker Data Parallelism | | [04 Distributed Training: Model Parallelism](https://github.com/huggingface/notebooks/blob/main/sagemaker/04_distributed_training_model_parallelism/sagemaker-notebook.ipynb) | Training | End-to-end example on how to use distributed training with model-parallelism strategy to pre-trained Hugging Face Transformer using Amazon SageMaker Model Parallelism | | [05 How to use Spot Instances & Checkpointing](https://github.com/huggingface/notebooks/blob/main/sagemaker/05_spot_instances/sagemaker-notebook.ipynb) | Training | End-to-end example on how to use Spot Instances and Checkpointing to reduce training cost | | [06 Experiment Tracking with SageMaker Metrics](https://github.com/huggingface/notebooks/blob/main/sagemaker/06_sagemaker_metrics/sagemaker-notebook.ipynb) | Training | End-to-end example on how to use SageMaker metrics to track your experiments and training jobs | | [07 Distributed Training: Data Parallelism](https://github.com/huggingface/notebooks/blob/main/sagemaker/07_tensorflow_distributed_training_data_parallelism/sagemaker-notebook.ipynb) | Training | End-to-end example on how to use Amazon SageMaker Data Parallelism with TensorFlow | | [08 Distributed Training: Summarization with T5/BART](https://github.com/huggingface/notebooks/blob/main/sagemaker/08_distributed_summarization_bart_t5/sagemaker-notebook.ipynb) | Training | End-to-end example on how to fine-tune BART/T5 for Summarization using Amazon SageMaker Data Parallelism | | [09 Vision: Fine-tune ViT](https://github.com/huggingface/notebooks/blob/main/sagemaker/09_image_classification_vision_transformer/sagemaker-notebook.ipynb) | Training | End-to-end example on how to fine-tune Vision Transformer for Image-Classification | | [10 Deploy HF Transformer from Amazon S3](https://github.com/huggingface/notebooks/blob/main/sagemaker/10_deploy_model_from_s3/deploy_transformer_model_from_s3.ipynb) | Inference | End-to-end example on how to deploy a model from Amazon S3 | | [11 Deploy HF Transformer from Hugging Face Hub](https://github.com/huggingface/notebooks/blob/main/sagemaker/11_deploy_model_from_hf_hub/deploy_transformer_model_from_hf_hub.ipynb) | Inference | End-to-end example on how to deploy a model from the Hugging Face Hub | | [12 Batch Processing with Amazon SageMaker Batch Transform](https://github.com/huggingface/notebooks/blob/main/sagemaker/12_batch_transform_inference/sagemaker-notebook.ipynb) | Inference | End-to-end example on how to do batch processing with Amazon SageMaker Batch Transform | | [13 Autoscaling SageMaker Endpoints](https://github.com/huggingface/notebooks/blob/main/sagemaker/13_deploy_and_autoscaling_transformers/sagemaker-notebook.ipynb) | Inference | End-to-end example on how to use autoscaling for a HF Endpoint | | [14 Fine-tune and push to Hub](https://github.com/huggingface/notebooks/blob/main/sagemaker/14_train_and_push_to_hub/sagemaker-notebook.ipynb) | Training | End-to-end example on how to use the Hugging Face Hub as MLOps backend for saving checkpoints during training | | [15 Training Compiler](https://github.com/huggingface/notebooks/blob/main/sagemaker/15_training_compiler/sagemaker-notebook.ipynb) | Training | End-to-end example on how to use Amazon SageMaker Training Compiler to speed up training time | | [16 Asynchronous Inference](https://github.com/huggingface/notebooks/blob/main/sagemaker/16_async_inference_hf_hub/sagemaker-notebook.ipynb) | Inference | End-to-end example on how to use Amazon SageMaker Asynchronous Inference endpoints with Hugging Face Transformers | | [17 Custom inference.py script](https://github.com/huggingface/notebooks/blob/main/sagemaker/17_custom_inference_script/sagemaker-notebook.ipynb) | Inference | End-to-end example on how to create a custom inference.py for Sentence Transformers and sentence embeddings | | [18 AWS Inferentia](https://github.com/huggingface/notebooks/blob/main/sagemaker/18_inferentia_inference/sagemaker-notebook.ipynb) | Inference | End-to-end example on how to AWS Inferentia to speed up inference time | | [19 Serverless Inference](https://github.com/huggingface/notebooks/blob/main/sagemaker/19_serverless_inference/sagemaker-notebook.ipynb) | Inference | Serverless Inference example to save cost | | [20 Automatic Speech Recognition](https://github.com/huggingface/notebooks/blob/main/sagemaker/20_automatic_speech_recognition_inference/sagemaker-notebook.ipynb) | Inference | Example how to do speech recognition with wav2vec2 | | [21 Image Segmentation](https://github.com/huggingface/notebooks/blob/main/sagemaker/21_image_segmantation/sagemaker-notebook.ipynb) | Inference | Example how to do image segmentation with segformer | | [22 Accelerate AWS SageMaker Integration examples](https://github.com/huggingface/notebooks/blob/main/sagemaker/22_accelerate_sagemaker_examples/README.md) | Training | End-to-end examples on how to use AWS SageMaker integration of Accelerate | | [23 Stable Diffusion](https://github.com/huggingface/notebooks/blob/main/sagemaker/23_stable_diffusion_inference/sagemaker-notebook.ipynb) | Inference | Example how to generate images with stable diffusion | | [24 Train BLOOM with PEFT](https://github.com/huggingface/notebooks/blob/main/sagemaker/24_train_bloom_peft_lora/sagemaker-notebook.ipynb) | Training | Example how to train BLOOM on a single GPU using PEFT & LoRA | | [25 PyTorch FSDP model parallelism](https://github.com/huggingface/notebooks/blob/main/sagemaker/25_pytorch_fsdp_model_parallelism/sagemaker-notebook.ipynb) | Training | Example how to train LLMs on multi-node multi GPU with PyTorch FSDP | | [26 Document AI Donut](https://github.com/huggingface/notebooks/blob/main/sagemaker/26_document_ai_donut/sagemaker-notebook.ipynb) | Training | In this tutorial, you will learn how to fine-tune and deploy [Donut-base](https://huggingface.co/naver-clova-ix/donut-base) for document-understand/document-parsing using Hugging Face Transformers and Amazon SageMaker. | | [27 Deploy Large Language Models](https://github.com/huggingface/notebooks/blob/main/sagemaker/27_deploy_large_language_models/sagemaker-notebook.ipynb) | Inference | Learn how to deploy LLMs with the Hugging Face LLM DLC | | [28 Train LLMs with QLora](https://github.com/huggingface/notebooks/blob/main/notebooks/sagemaker/28_train_llms_with_qlora/sagemaker-notebook.ipynb) | Training | Example on how to fine-tune LLMs using Q-Lora | | [29 Deploy LLMs with Inferentia2](https://github.com/huggingface/notebooks/blob/main/notebooks/sagemaker/29_deploy_llms_on_inferentia2/sagemaker-notebook.ipynb) | Inference | Learn how to deploy LLMs using AWS Inferentia2 | | [30 Evaluate LLMs with ligtheval](https://github.com/huggingface/notebooks/blob/main/notebooks/sagemaker/30_evaluate_llms_with_lighteval/sagemaker-notebook.ipynb) | Inference | Learn how to evaluate LLMs using Hugging Face LightEval | | [31 Deploy Embedding Models with TEI](https://github.com/huggingface/notebooks/blob/main/notebooks/sagemaker/31_deploy_embedding_models/sagemaker-notebook.ipynb) | Inference | Learn how to deploy Embedding models for RAG applications with Hugging Face TEI | | [32 Train and deploy Embedding Models](https://github.com/huggingface/notebooks/blob/main/notebooks/sagemaker/32_train_deploy_embedding_models/sagemaker-notebook.ipynb) | Train & Inference | Learn how to train and deploy embedding models with Sentence Transformers and TEI | --- ## File: sagemaker/14_train_and_push_to_hub/README.md # SageMaker push to hf.co/models example --- ## File: sagemaker/22_accelerate_sagemaker_examples/README.md # accelerate-aws-sagemaker Examples showcasing AWS SageMaker integration of ๐Ÿค— Accelerate. Just give the `accelerate config` and do `accelerate launch` ๐Ÿš€. It's as simple as that! 1. Set up the accelerate config by running `accelerate config --config_file accelerate_config.yaml` and answer the SageMaker questions. 2. Below is a sample config which is using aws `profile` to launch training job using ๐Ÿค— SageMaker estimator. It also has the `iam_role_name` which has the needed SageMaker permissions specified. In this config it is replaced `xxxxx` as user needs to specify it based on their corresponding AWS setup. ```yaml base_job_name: accelerate-sagemaker-1 compute_environment: AMAZON_SAGEMAKER distributed_type: DATA_PARALLEL ec2_instance_type: ml.p3.16xlarge iam_role_name: xxxxx image_uri: null mixed_precision: fp16 num_machines: 1 profile: xxxxx py_version: py38 pytorch_version: 1.10.2 region: us-east-1 transformers_version: 4.17.0 use_cpu: false ``` 3. One can specify a custom docker image instead of Official ๐Ÿค— DLCs through the accelerate config questionnaire. When this isn't provided, the latest Official ๐Ÿค— DLC will be used. 4. Support for input channels pointing to S3 data locations via TSV file, e.g., below are the contents of sagemaker_inputs.tsv whose location is given as part of accelerate config setup. ```tsv channel_name data_location train s3://sagemaker-sample/samples/datasets/imdb/train test s3://sagemaker-sample/samples/datasets/imdb/test ``` 5. Support for SageMaker metrics logging via TSV file, e.g., below are the contents of the sagemaker_metrics_definition.tsv whose location is given as part of accelerate config setup. ```tsv metric_name metric_regex accuracy 'accuracy': ([0-9.]+) f1 'f1': ([0-9.]+) ``` 6. Example of accelerate config with above features setup [XXXXX values are AWS account specific]: ```yaml base_job_name: accelerate-sagemaker-1 compute_environment: AMAZON_SAGEMAKER distributed_type: DATA_PARALLEL ec2_instance_type: ml.p3.16xlarge iam_role_name: XXXXX image_uri: 763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.8.1-transformers4.10.2-gpu-py36-cu111-ubuntu18.04 mixed_precision: fp16 num_machines: 1 profile: XXXXX py_version: py38 pytorch_version: 1.10.2 region: us-east-1 sagemaker_inputs_file: sagemaker_inputs.tsv sagemaker_metrics_file: sagemaker_metrics_definition.tsv transformers_version: 4.17.0 use_cpu: false ``` 7. Put `requirements.txt` with all the needed libraries for running the training script. 8. Running `text-classification` example using s3 datasets (from the root directory): ```bash cd src/text-classification bash launch.sh ``` The contents of launch.sh ```bash accelerate launch --config_file accelerate_config.yaml train_using_s3_data.py \ --mixed_precision "fp16" ``` Output logs: ```bash ... [1,mpirank:0,algo-1]:algo-1:79:1300 [0] NCCL INFO Launch mode Parallel [1,mpirank:0,algo-1]:INFO:root:Reducer buckets have been rebuilt in this iteration. [1,mpirank:3,algo-1]:INFO:root:Reducer buckets have been rebuilt in this iteration. [1,mpirank:1,algo-1]:INFO:root:Reducer buckets have been rebuilt in this iteration. [1,mpirank:2,algo-1]:INFO:root:Reducer buckets have been rebuilt in this iteration. [1,mpirank:6,algo-1]:INFO:root:Reducer buckets have been rebuilt in this iteration. [1,mpirank:5,algo-1]:INFO:root:Reducer buckets have been rebuilt in this iteration. [1,mpirank:7,algo-1]:INFO:root:Reducer buckets have been rebuilt in this iteration. [1,mpirank:4,algo-1]:INFO:root:Reducer buckets have been rebuilt in this iteration. [1,mpirank:0,algo-1]:epoch 0: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079} [1,mpirank:0,algo-1]:epoch 1: {'accuracy': 0.7205882352941176, 'f1': 0.8256880733944955} [1,mpirank:0,algo-1]:epoch 2: {'accuracy': 0.75, 'f1': 0.838095238095238} 2022-09-21 13:21:05,187 sagemaker-training-toolkit INFO Waiting for the process to finish and give a return code. 2022-09-21 13:21:05,188 sagemaker-training-toolkit INFO Done waiting for a return code. Received 0 from exiting process. 2022-09-21 13:21:05,188 sagemaker-training-toolkit INFO Reporting training SUCCESS ``` 9. Running `seq2seq` example: ```bash cd src/seq2seq bash launch.sh ``` The contents of launch.sh ```bash accelerate launch --config_file accelerate_config.yaml run_seq2seq_no_trainer.py \ --dataset_name "smangrul/MuDoConv" \ --max_source_length 128 \ --source_prefix "chatbot: " \ --max_target_length 64 \ --val_max_target_length 64 \ --val_min_target_length 20 \ --n_val_batch_generations 5 \ --n_train 10000 \ --n_val 1000 \ --pad_to_max_length True\ --num_beams 10 \ --model_name_or_path "facebook/blenderbot-400M-distill" \ --per_device_train_batch_size 16 \ --per_device_eval_batch_size 16 \ --learning_rate 1e-6 \ --weight_decay 0.0 \ --num_train_epochs 1 \ --gradient_accumulation_steps 1 \ --num_warmup_steps 100 \ --output_dir "/opt/ml/model" \ --seed 25 \ --logging_steps 100 \ --report_name "blenderbot_400M_finetuning" ``` Output logs: ``` [1,mpirank:0,algo-1]:09/21/2022 13:37:39 - INFO - __main__ - Distributed environment: MULTI_GPU Backend: smddp [1,mpirank:0,algo-1]:Num processes: 8 [1,mpirank:0,algo-1]:Process index: 0 [1,mpirank:0,algo-1]:Local process index: 0 [1,mpirank:0,algo-1]:Device: cuda:0 [1,mpirank:0,algo-1]: ... [1,mpirank:0,algo-1]:09/21/2022 13:38:31 - INFO - __main__ - ***** Running training ***** [1,mpirank:0,algo-1]:09/21/2022 13:38:31 - INFO - __main__ - Num examples = 10000 [1,mpirank:0,algo-1]:09/21/2022 13:38:31 - INFO - __main__ - Num Epochs = 1 [1,mpirank:0,algo-1]:09/21/2022 13:38:31 - INFO - __main__ - Instantaneous batch size per device = 16 [1,mpirank:0,algo-1]:09/21/2022 13:38:31 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 128 [1,mpirank:0,algo-1]:09/21/2022 13:38:31 - INFO - __main__ - Gradient Accumulation steps = 1 [1,mpirank:0,algo-1]:09/21/2022 13:38:31 - INFO - __main__ - Total optimization steps = 79 ... [1,mpirank:0,algo-1]:#015100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 79/79 [00:19<00:00, 4.79it/s] [1,mpirank:0,algo-1]:09/21/2022 13:38:50 - INFO - __main__ - Epoch 0 training took 19.50162172317505 seconds [1,mpirank:0,algo-1]:starting evaluation [1,mpirank:0,algo-1]:09/21/2022 13:38:55 - INFO - __main__ - printing few sample generations and corresponding labels from eval set [1,mpirank:0,algo-1]:09/21/2022 13:38:55 - INFO - __main__ - prompt | generated | label [1,mpirank:0,algo-1]:09/21/2022 13:38:55 - INFO - __main__ - chatbot:your persona: i work as an electrician. i always sleep 8 hours a day. Which level are you at? | I'm at the top of the ladder. I work for an electrical company. | I received on-the-job training when i first started [1,mpirank:0,algo-1]:09/21/2022 13:38:55 - INFO - __main__ - chatbot:your persona: i work as an electrician. i always sleep 8 hours a day. Which level are you at? I received on-the-job training when i first started Thats great! How long have you been doing this work? | I've been working as an Electrician for about 5 years now. It's a great job. | For a good number of years now. ... [1,mpirank:0,algo-1]:09/21/2022 13:39:02 - INFO - __main__ - chatbot:your persona: i'm a painter and love to create art. i'm a talented singer and have won several competitions. I love the Doors! They have such a unique sound. Do you have a favorite Doors song? | My favorite song of theirs is "When I Was Your Man". What's yours? | It's a tie between People are strange and Love me two times. What's your favorite? [1,mpirank:0,algo-1]:09/21/2022 13:39:02 - INFO - __main__ - chatbot:your persona: i'm a painter and love to create art. i'm a talented singer and have won several competitions. I think my favorite is Love Street. It has such a haunting melody. Have you heard that one? | No, I haven't. I'll have to check it out. What genre is it? | Yeah, it's a pretty great song, Jim Morrison was just an amazing songwriter, I aspire to make songs as good as his one day. ... [1,mpirank:0,algo-1]:09/21/2022 13:39:17 - INFO - __main__ - {'bleu': 1.7067114414104911} [1,mpirank:0,algo-1]:evaluation completed [1,mpirank:0,algo-1]:09/21/2022 13:39:17 - INFO - __main__ - Epoch 0 evaluation took 24.294514417648315 seconds [1,mpirank:0,algo-1]:Configuration saved in /opt/ml/model/config.json [1,mpirank:0,algo-1]:Model weights saved in /opt/ml/model/pytorch_model.bin [1,mpirank:0,algo-1]:tokenizer config file saved in /opt/ml/model/tokenizer_config.json [1,mpirank:0,algo-1]:Special tokens file saved in /opt/ml/model/special_tokens_map.json [1,mpirank:0,algo-1]:#015100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 79/79 [00:47<00:00, 1.65it/s] 2022-09-21 13:39:27,753 sagemaker-training-toolkit INFO Waiting for the process to finish and give a return code. 2022-09-21 13:39:27,753 sagemaker-training-toolkit INFO Done waiting for a return code. Received 0 from exiting process. 2022-09-21 13:39:27,754 sagemaker-training-toolkit INFO Reporting training SUCCESS ``` --- ## File: sagemaker/22_accelerate_sagemaker_examples/src/seq2seq/accelerate_config.yaml base_job_name: accelerate-sagemaker-1 compute_environment: AMAZON_SAGEMAKER distributed_type: DATA_PARALLEL ec2_instance_type: ml.p3.16xlarge iam_role_name: xxxxx image_uri: null mixed_precision: fp16 num_machines: 1 profile: xxxxx py_version: py38 pytorch_version: 1.10.2 region: us-east-1 transformers_version: 4.17.0 use_cpu: false --- ## File: sagemaker/22_accelerate_sagemaker_examples/src/seq2seq/requirements.txt accelerate # used to be installed in Amazon SageMaker environment evaluate datasets==2.3.2 nltk deepspeed sentencepiece != 0.1.92 protobuf sacrebleu >= 1.4.12 nltk py7zr torch >= 1.3 transformers==4.22.1 --- ## File: sagemaker/22_accelerate_sagemaker_examples/src/text-classification/accelerate_config.yaml base_job_name: accelerate-sagemaker-1 compute_environment: AMAZON_SAGEMAKER distributed_type: DATA_PARALLEL ec2_instance_type: ml.p3.16xlarge iam_role_name: xxxxx image_uri: null mixed_precision: fp16 num_machines: 1 profile: xxxxx py_version: py38 pytorch_version: 1.10.2 region: us-east-1 sagemaker_inputs_file: sagemaker_inputs.tsv sagemaker_metrics_file: sagemaker_metrics_definition.tsv transformers_version: 4.17.0 use_cpu: false --- ## File: sagemaker/22_accelerate_sagemaker_examples/src/text-classification/requirements.txt accelerate # used to be installed in Amazon SageMaker environment evaluate datasets==2.3.2 transformers --- ## File: sagemaker/23_stable_diffusion_inference/code/requirements.txt diffusers==0.6.0 transformers==4.23.1 --- ## File: sagemaker/24_train_bloom_peft_lora/scripts/requirements.txt peft==0.3.0 transformers==4.27.1 accelerate==0.17.1 bitsandbytes==0.37.1 --- ## File: sagemaker/25_pytorch_fsdp_model_parallelism/scripts/requirements.txt transformers==4.28.1 --- ## File: sagemaker/28_train_llms_with_qlora/scripts/requirements.txt git+https://github.com/huggingface/transformers.git@2ab75add4b30c2fc44a8bf575156d448d9ed87a7 # transformers==4.31.0 peft==0.4.0 accelerate==0.21.0 bitsandbytes==0.40.2 safetensors>=0.3.1 tokenizers>=0.13.3 --- ## File: sagemaker/32_train_deploy_embedding_models/scripts/requirements.txt transformers==4.41.2 accelerate==0.31.0 sentence-transformers==3.0.0 datasets==2.18.0 torch==2.1.2 --- ## File: transformers_doc/README.md # ๐Ÿค— Transformers doc notebooks These notebooks are automatically generated from the [๐Ÿค— Transformers documentation](https://huggingface.co/transformers/) so you should not make any direct modification here. If there is a typo to fix or a sentence to add, open a pull request in the [๐Ÿค— Transformers repo](https://github.com/huggingface/transformers) and fix the corresponding file in the `docs/source/` folder. If there is something that seems weirdly converted from the original doc file, open an issue in this repo and we will try to fix the conversion script. **Note:** If you are looking for a particular notebook and can't find it, it may have moved inside the `en` subfolder. --- ## File: transformers_doc/en/tensorflow/README.md > [!WARNING] > TensorFlow is [deprecated](https://github.com/huggingface/transformers/pull/38758) for Transformers and no longer actively maintained. It will be removed in v5. We recommend pinning or installing a version of Transformers that supports TensorFlow to continue using these notebooks. --- ## File: .github/pull_request_template.md # What does this PR do? Fixes # (issue) ## Who can review? Feel free to tag members/contributors who may be interested in your PR. --- METRICS --- - Files Extracted: 19 - Estimated Token Budget: ~7137 tokens - Recency Window: Active (< 180 days) - Canonical Reference: https://codewiki.google/github.com/huggingface/notebooks