diff --git a/docs/source/en/chat_templating.md b/docs/source/en/chat_templating.md index e0ffd9ad15..87f95e1ebd 100644 --- a/docs/source/en/chat_templating.md +++ b/docs/source/en/chat_templating.md @@ -390,7 +390,7 @@ If your model expects those, they won't be added automatically by `apply_chat_te text will be tokenized with `add_special_tokens=False`. This is to avoid potential conflicts between the template and the `add_special_tokens` logic. If your model expects special tokens, make sure to add them to the template! -``` +```python tokenizer.chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}" ``` diff --git a/docs/source/en/custom_models.md b/docs/source/en/custom_models.md index c64b2af5c2..3d43446a0c 100644 --- a/docs/source/en/custom_models.md +++ b/docs/source/en/custom_models.md @@ -310,7 +310,7 @@ Use `register_for_auto_class()` if you want the code files to be copied. If you you don't need to call it. In cases where there's more than one auto class, you can modify the `config.json` directly using the following structure: -``` +```json "auto_map": { "AutoConfig": "--", "AutoModel": "--", diff --git a/docs/source/en/custom_tools.md b/docs/source/en/custom_tools.md index 86183a8075..4221679c79 100644 --- a/docs/source/en/custom_tools.md +++ b/docs/source/en/custom_tools.md @@ -405,7 +405,7 @@ Assistant: Therefore it is important that the examples of the custom `chat` prompt template also make use of this format. You can overwrite the `chat` template at instantiation as follows. -``` +```python template = """ [...] """ agent = HfAgent(url_endpoint=your_endpoint, chat_prompt_template=template) diff --git a/docs/source/en/installation.md b/docs/source/en/installation.md index 818667feb1..a7b916fe48 100644 --- a/docs/source/en/installation.md +++ b/docs/source/en/installation.md @@ -72,7 +72,7 @@ pip install 'transformers[tf-cpu]' M1 / ARM Users You will need to install the following before installing TensorFLow 2.0 -``` +```bash brew install cmake brew install pkg-config ``` diff --git a/docs/source/en/model_doc/fastspeech2_conformer.md b/docs/source/en/model_doc/fastspeech2_conformer.md index 3995036eff..dbb87b5a41 100644 --- a/docs/source/en/model_doc/fastspeech2_conformer.md +++ b/docs/source/en/model_doc/fastspeech2_conformer.md @@ -41,7 +41,7 @@ You can run FastSpeech2Conformer locally with the 🤗 Transformers library. 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers), g2p-en: -``` +```bash pip install --upgrade pip pip install --upgrade transformers g2p-en ``` diff --git a/docs/source/en/model_doc/layoutlmv2.md b/docs/source/en/model_doc/layoutlmv2.md index 15286d4ddb..0769322e9a 100644 --- a/docs/source/en/model_doc/layoutlmv2.md +++ b/docs/source/en/model_doc/layoutlmv2.md @@ -50,7 +50,7 @@ this https URL.* LayoutLMv2 depends on `detectron2`, `torchvision` and `tesseract`. Run the following to install them: -``` +```bash python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' python -m pip install torchvision tesseract ``` diff --git a/docs/source/en/model_doc/lilt.md b/docs/source/en/model_doc/lilt.md index fb279573fb..2514a6ebd8 100644 --- a/docs/source/en/model_doc/lilt.md +++ b/docs/source/en/model_doc/lilt.md @@ -39,7 +39,7 @@ The original code can be found [here](https://github.com/jpwang/lilt). - To combine the Language-Independent Layout Transformer with a new RoBERTa checkpoint from the [hub](https://huggingface.co/models?search=roberta), refer to [this guide](https://github.com/jpWang/LiLT#or-generate-your-own-checkpoint-optional). The script will result in `config.json` and `pytorch_model.bin` files being stored locally. After doing this, one can do the following (assuming you're logged in with your HuggingFace account): -``` +```python from transformers import LiltModel model = LiltModel.from_pretrained("path_to_your_files") diff --git a/docs/source/en/model_doc/musicgen.md b/docs/source/en/model_doc/musicgen.md index bc2234ce3c..7c105e1f39 100644 --- a/docs/source/en/model_doc/musicgen.md +++ b/docs/source/en/model_doc/musicgen.md @@ -136,7 +136,7 @@ The same [`MusicgenProcessor`] can be used to pre-process an audio prompt that i following example, we load an audio file using the 🤗 Datasets library, which can be pip installed through the command below: -``` +```bash pip install --upgrade pip pip install datasets[audio] ``` diff --git a/docs/source/en/model_doc/pop2piano.md b/docs/source/en/model_doc/pop2piano.md index 8e52eda70c..8e7c1fbd34 100644 --- a/docs/source/en/model_doc/pop2piano.md +++ b/docs/source/en/model_doc/pop2piano.md @@ -54,7 +54,7 @@ The original code can be found [here](https://github.com/sweetcocoa/pop2piano). ## Usage tips * To use Pop2Piano, you will need to install the 🤗 Transformers library, as well as the following third party modules: -``` +```bash pip install pretty-midi==0.2.9 essentia==2.1b6.dev1034 librosa scipy ``` Please note that you may need to restart your runtime after installation. diff --git a/docs/source/en/perf_hardware.md b/docs/source/en/perf_hardware.md index 18c70e1b30..187bdd27b5 100644 --- a/docs/source/en/perf_hardware.md +++ b/docs/source/en/perf_hardware.md @@ -64,7 +64,7 @@ Next let's have a look at one of the most important aspects when having multiple If you use multiple GPUs the way cards are inter-connected can have a huge impact on the total training time. If the GPUs are on the same physical node, you can run: -``` +```bash nvidia-smi topo -m ``` diff --git a/docs/source/en/perf_train_cpu.md b/docs/source/en/perf_train_cpu.md index 3517cec3dc..19b76c169d 100644 --- a/docs/source/en/perf_train_cpu.md +++ b/docs/source/en/perf_train_cpu.md @@ -38,7 +38,7 @@ IPEX release is following PyTorch, to install via pip: | 1.12 | 1.12.300+cpu | Please run `pip list | grep torch` to get your `pytorch_version`, so you can get the `IPEX version_name`. -``` +```bash pip install intel_extension_for_pytorch== -f https://developer.intel.com/ipex-whl-stable-cpu ``` You can check the latest versions in [ipex-whl-stable-cpu](https://developer.intel.com/ipex-whl-stable-cpu) if needed. diff --git a/docs/source/en/perf_train_cpu_many.md b/docs/source/en/perf_train_cpu_many.md index 8b938921cb..9312d4b911 100644 --- a/docs/source/en/perf_train_cpu_many.md +++ b/docs/source/en/perf_train_cpu_many.md @@ -39,7 +39,7 @@ Wheel files are available for the following Python versions: | 1.12.0 | | √ | √ | √ | √ | Please run `pip list | grep torch` to get your `pytorch_version`. -``` +```bash pip install oneccl_bind_pt=={pytorch_version} -f https://developer.intel.com/ipex-whl-stable-cpu ``` where `{pytorch_version}` should be your PyTorch version, for instance 2.1.0. @@ -59,13 +59,13 @@ Use this standards-based MPI implementation to deliver flexible, efficient, scal oneccl_bindings_for_pytorch is installed along with the MPI tool set. Need to source the environment before using it. for Intel® oneCCL >= 1.12.0 -``` +```bash oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)") source $oneccl_bindings_for_pytorch_path/env/setvars.sh ``` for Intel® oneCCL whose version < 1.12.0 -``` +```bash torch_ccl_path=$(python -c "import torch; import torch_ccl; import os; print(os.path.abspath(os.path.dirname(torch_ccl.__file__)))") source $torch_ccl_path/env/setvars.sh ``` @@ -154,7 +154,7 @@ This example assumes that you have: The snippet below is an example of a Dockerfile that uses a base image that supports distributed CPU training and then extracts a Transformers release to the `/workspace` directory, so that the example scripts are included in the image: -``` +```dockerfile FROM intel/ai-workflows:torch-2.0.1-huggingface-multinode-py3.9 WORKDIR /workspace @@ -286,7 +286,7 @@ set the same CPU and memory amounts for both the resource limits and requests. After the PyTorchJob spec has been updated with values appropriate for your cluster and training job, it can be deployed to the cluster using: -``` +```bash kubectl create -f pytorchjob.yaml ``` @@ -304,7 +304,7 @@ transformers-pytorchjob-worker-3 1/1 Running ``` The logs for worker can be viewed using `kubectl logs -n kubeflow `. Add `-f` to stream the logs, for example: -``` +```bash kubectl logs -n kubeflow transformers-pytorchjob-worker-0 -f ``` diff --git a/docs/source/en/perf_train_gpu_many.md b/docs/source/en/perf_train_gpu_many.md index 92c2fe9bbf..30c7aedfa3 100644 --- a/docs/source/en/perf_train_gpu_many.md +++ b/docs/source/en/perf_train_gpu_many.md @@ -140,7 +140,7 @@ Here is the benchmarking code and outputs: **DP** -``` +```bash rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \ python examples/pytorch/language-modeling/run_clm.py \ --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \ @@ -151,7 +151,7 @@ python examples/pytorch/language-modeling/run_clm.py \ **DDP w/ NVlink** -``` +```bash rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \ torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \ --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \ @@ -162,7 +162,7 @@ torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \ **DDP w/o NVlink** -``` +```bash rm -r /tmp/test-clm; NCCL_P2P_DISABLE=1 CUDA_VISIBLE_DEVICES=0,1 \ torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \ --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \ diff --git a/docs/source/en/perf_train_gpu_one.md b/docs/source/en/perf_train_gpu_one.md index d8cbf55f6d..9a81a622cc 100644 --- a/docs/source/en/perf_train_gpu_one.md +++ b/docs/source/en/perf_train_gpu_one.md @@ -201,7 +201,7 @@ of 23 bits precision it has only 10 bits (same as fp16) and uses only 19 bits in you can use the normal fp32 training and/or inference code and by enabling tf32 support you can get up to 3x throughput improvement. All you need to do is to add the following to your code: -``` +```python import torch torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True diff --git a/docs/source/en/tasks/video_classification.md b/docs/source/en/tasks/video_classification.md index a140ba3730..38bdceba41 100644 --- a/docs/source/en/tasks/video_classification.md +++ b/docs/source/en/tasks/video_classification.md @@ -483,7 +483,7 @@ You can also manually replicate the results of the `pipeline` if you'd like. Now, pass your input to the model and return the `logits`: -``` +```py >>> logits = run_inference(trained_model, sample_test_video["video"]) ``` diff --git a/docs/source/fr/installation.md b/docs/source/fr/installation.md index bf2fa26a34..793a1eec82 100644 --- a/docs/source/fr/installation.md +++ b/docs/source/fr/installation.md @@ -74,7 +74,7 @@ Pour les architectures mac M1 / ARM Vous devez installer les outils suivants avant d'installer TensorFLow 2.0 -``` +```bash brew install cmake brew install pkg-config ``` diff --git a/docs/source/it/perf_hardware.md b/docs/source/it/perf_hardware.md index dd1187a01b..79e41c0b7e 100644 --- a/docs/source/it/perf_hardware.md +++ b/docs/source/it/perf_hardware.md @@ -63,7 +63,7 @@ Diamo quindi un'occhiata a uno degli aspetti più importanti quando si hanno pi Se utilizzi più GPU, il modo in cui le schede sono interconnesse può avere un enorme impatto sul tempo totale di allenamento. Se le GPU si trovano sullo stesso nodo fisico, puoi eseguire: -``` +```bash nvidia-smi topo -m ``` diff --git a/docs/source/ja/chat_templating.md b/docs/source/ja/chat_templating.md index c36b21013d..78d900b5be 100644 --- a/docs/source/ja/chat_templating.md +++ b/docs/source/ja/chat_templating.md @@ -215,7 +215,7 @@ LLM（Language Model）はさまざまな入力形式を処理できるほどス If you like this one, here it is in one-liner form, ready to copy into your code: -``` +```python tokenizer.chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}" ``` diff --git a/docs/source/ja/custom_tools.md b/docs/source/ja/custom_tools.md index 9a097100c5..6a9b1f58e5 100644 --- a/docs/source/ja/custom_tools.md +++ b/docs/source/ja/custom_tools.md @@ -385,7 +385,7 @@ Assistant: したがって、カスタム`chat`プロンプトテンプレートの例もこのフォーマットを使用することが重要です。以下のように、インスタンス化時に`chat`テンプレートを上書きできます。 -``` +```python template = """ [...] """ agent = HfAgent(url_endpoint=your_endpoint, chat_prompt_template=template) diff --git a/docs/source/ja/main_classes/deepspeed.md b/docs/source/ja/main_classes/deepspeed.md index d5206e3647..b2ba2bead9 100644 --- a/docs/source/ja/main_classes/deepspeed.md +++ b/docs/source/ja/main_classes/deepspeed.md @@ -2202,7 +2202,7 @@ print(f"rank{rank}:\n in={text_in}\n out={text_out}") それを`t0.py`として保存して実行しましょう。 -``` +```bash $ deepspeed --num_gpus 2 t0.py rank0: in=Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy @@ -2226,13 +2226,13 @@ DeepSpeed 統合を含む PR を送信する場合は、CircleCI PR CI セット DeepSpeed テストを実行するには、少なくとも以下を実行してください。 -``` +```bash RUN_SLOW=1 pytest tests/deepspeed/test_deepspeed.py ``` モデリングまたは pytorch サンプル コードのいずれかを変更した場合は、Model Zoo テストも実行します。以下はすべての DeepSpeed テストを実行します。 -``` +```bash RUN_SLOW=1 pytest tests/deepspeed ``` diff --git a/docs/source/ja/perf_hardware.md b/docs/source/ja/perf_hardware.md index a0db527a94..2ebc0eef9b 100644 --- a/docs/source/ja/perf_hardware.md +++ b/docs/source/ja/perf_hardware.md @@ -64,7 +64,7 @@ GPUが重要な負荷の下でどのような温度を目指すべきかを正 複数のGPUを使用する場合、カードの相互接続方法はトータルのトレーニング時間に大きな影響を与える可能性があります。GPUが同じ物理ノードにある場合、次のように実行できます： -``` +```bash nvidia-smi topo -m ``` diff --git a/docs/source/ja/perf_torch_compile.md b/docs/source/ja/perf_torch_compile.md index 2927138aee..6eb69ec8eb 100644 --- a/docs/source/ja/perf_torch_compile.md +++ b/docs/source/ja/perf_torch_compile.md @@ -42,7 +42,7 @@ model = AutoModelForImageClassification.from_pretrained(MODEL_ID).to("cuda") ### Image Classification with ViT -``` +```python from PIL import Image import requests import numpy as np diff --git a/docs/source/ja/perf_train_cpu.md b/docs/source/ja/perf_train_cpu.md index b6876f03a0..b22d7b96aa 100644 --- a/docs/source/ja/perf_train_cpu.md +++ b/docs/source/ja/perf_train_cpu.md @@ -36,7 +36,7 @@ IPEXのリリースはPyTorchに従っており、pipを使用してインスト | 1.11 | 1.11.200+cpu | | 1.10 | 1.10.100+cpu | -``` +```bash pip install intel_extension_for_pytorch== -f https://developer.intel.com/ipex-whl-stable-cpu ``` diff --git a/docs/source/ja/perf_train_cpu_many.md b/docs/source/ja/perf_train_cpu_many.md index 5cbdade4e5..a15cb5d490 100644 --- a/docs/source/ja/perf_train_cpu_many.md +++ b/docs/source/ja/perf_train_cpu_many.md @@ -38,7 +38,7 @@ Wheelファイルは、以下のPythonバージョン用に利用可能です: | 1.11.0 | | √ | √ | √ | √ | | 1.10.0 | √ | √ | √ | √ | | -``` +```bash pip install oneccl_bind_pt=={pytorch_version} -f https://developer.intel.com/ipex-whl-stable-cpu ``` @@ -70,13 +70,13 @@ oneccl_bindings_for_pytorchはMPIツールセットと一緒にインストー for Intel® oneCCL >= 1.12.0 -``` +```bash oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)") source $oneccl_bindings_for_pytorch_path/env/setvars.sh ``` for Intel® oneCCL whose version < 1.12.0 -``` +```bash torch_ccl_path=$(python -c "import torch; import torch_ccl; import os; print(os.path.abspath(os.path.dirname(torch_ccl.__file__)))") source $torch_ccl_path/env/setvars.sh ``` diff --git a/docs/source/ja/perf_train_gpu_many.md b/docs/source/ja/perf_train_gpu_many.md index 71d6c28058..44186bba79 100644 --- a/docs/source/ja/perf_train_gpu_many.md +++ b/docs/source/ja/perf_train_gpu_many.md @@ -131,7 +131,7 @@ DPとDDPの他にも違いがありますが、この議論には関係ありま `NCCL_P2P_DISABLE=1`を使用して、対応するベンチマークでNVLink機能を無効にしました。 -``` +```bash # DP rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \ diff --git a/docs/source/ja/perf_train_gpu_one.md b/docs/source/ja/perf_train_gpu_one.md index b06709cd00..215c0914d1 100644 --- a/docs/source/ja/perf_train_gpu_one.md +++ b/docs/source/ja/perf_train_gpu_one.md @@ -151,7 +151,7 @@ training_args = TrainingArguments(bf16=True, **default_args) アンペアハードウェアは、tf32という特別なデータ型を使用します。これは、fp32と同じ数値範囲（8ビット）を持っていますが、23ビットの精度ではなく、10ビットの精度（fp16と同じ）を持ち、合計で19ビットしか使用しません。これは通常のfp32トレーニングおよび推論コードを使用し、tf32サポートを有効にすることで、最大3倍のスループットの向上が得られる点で「魔法のよう」です。行う必要があるのは、次のコードを追加するだけです： -``` +```python import torch torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True diff --git a/docs/source/ja/tasks/video_classification.md b/docs/source/ja/tasks/video_classification.md index ae49875b71..e0c3836194 100644 --- a/docs/source/ja/tasks/video_classification.md +++ b/docs/source/ja/tasks/video_classification.md @@ -490,7 +490,7 @@ def compute_metrics(eval_pred): 次に、入力をモデルに渡し、`logits `を返します。 -``` +```py >>> logits = run_inference(trained_model, sample_test_video["video"]) ``` diff --git a/docs/source/ko/custom_tools.md b/docs/source/ko/custom_tools.md index 87017a68b5..6e07ccf86c 100644 --- a/docs/source/ko/custom_tools.md +++ b/docs/source/ko/custom_tools.md @@ -373,7 +373,7 @@ Assistant: 따라서 사용자 정의 `chat` 프롬프트 템플릿의 예제에서도 이 형식을 사용하는 것이 중요합니다. 다음과 같이 인스턴스화 할 때 `chat` 템플릿을 덮어쓸 수 있습니다. -``` +```python template = """ [...] """ agent = HfAgent(url_endpoint=your_endpoint, chat_prompt_template=template) diff --git a/docs/source/ko/perf_hardware.md b/docs/source/ko/perf_hardware.md index bb35e6fae2..dedb9a60ed 100644 --- a/docs/source/ko/perf_hardware.md +++ b/docs/source/ko/perf_hardware.md @@ -64,7 +64,7 @@ GPU가 과열될 때 정확한 적정 온도를 알기 어려우나, 아마도 + 다중 GPU를 사용하는 경우 GPU 간의 연결 방식은 전체 훈련 시간에 큰 영향을 미칠 수 있습니다. 만약 GPU가 동일한 물리적 노드에 있을 경우, 다음과 같이 확인할 수 있습니다: -``` +```bash nvidia-smi topo -m ``` diff --git a/docs/source/ko/perf_train_cpu.md b/docs/source/ko/perf_train_cpu.md index 573e7abc9d..f0398aaa26 100644 --- a/docs/source/ko/perf_train_cpu.md +++ b/docs/source/ko/perf_train_cpu.md @@ -36,7 +36,7 @@ IPEX 릴리스는 PyTorch를 따라갑니다. pip를 통해 설치하려면: | 1.11 | 1.11.200+cpu | | 1.10 | 1.10.100+cpu | -``` +```bash pip install intel_extension_for_pytorch== -f https://developer.intel.com/ipex-whl-stable-cpu ``` diff --git a/docs/source/ko/perf_train_cpu_many.md b/docs/source/ko/perf_train_cpu_many.md index 47545e8453..9ff4cfbfa6 100644 --- a/docs/source/ko/perf_train_cpu_many.md +++ b/docs/source/ko/perf_train_cpu_many.md @@ -37,7 +37,7 @@ rendered properly in your Markdown viewer. | 1.11.0 | | √ | √ | √ | √ | | 1.10.0 | √ | √ | √ | √ | | -``` +```bash pip install oneccl_bind_pt=={pytorch_version} -f https://developer.intel.com/ipex-whl-stable-cpu ``` `{pytorch_version}`은 1.13.0과 같이 PyTorch 버전을 나타냅니다. @@ -57,13 +57,13 @@ PyTorch 1.12.1은 oneccl_bindings_for_pytorch 1.12.10 버전과 함께 사용해 oneccl_bindings_for_pytorch는 MPI 도구 세트와 함께 설치됩니다. 사용하기 전에 환경을 소스로 지정해야 합니다. Intel® oneCCL 버전 1.12.0 이상인 경우 -``` +```bash oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)") source $oneccl_bindings_for_pytorch_path/env/setvars.sh ``` Intel® oneCCL 버전이 1.12.0 미만인 경우 -``` +```bash torch_ccl_path=$(python -c "import torch; import torch_ccl; import os; print(os.path.abspath(os.path.dirname(torch_ccl.__file__)))") source $torch_ccl_path/env/setvars.sh ``` diff --git a/docs/source/ko/perf_train_gpu_many.md b/docs/source/ko/perf_train_gpu_many.md index 706832a8a1..1fc6ce8e1c 100644 --- a/docs/source/ko/perf_train_gpu_many.md +++ b/docs/source/ko/perf_train_gpu_many.md @@ -133,7 +133,7 @@ DP와 DDP 사이에는 다른 차이점이 있지만, 이 토론과는 관련이 해당 벤치마크에서 `NCCL_P2P_DISABLE=1`을 사용하여 NVLink 기능을 비활성화했습니다. -``` +```bash # DP rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \ diff --git a/docs/source/ko/tasks/video_classification.md b/docs/source/ko/tasks/video_classification.md index eb04352d84..01dbb0757b 100644 --- a/docs/source/ko/tasks/video_classification.md +++ b/docs/source/ko/tasks/video_classification.md @@ -485,7 +485,7 @@ def compute_metrics(eval_pred): 모델에 입력값을 넣고 `logits`을 반환받으세요: -``` +```py >>> logits = run_inference(trained_model, sample_test_video["video"]) ``` diff --git a/docs/source/zh/installation.md b/docs/source/zh/installation.md index 56ff01957e..0ce10ba529 100644 --- a/docs/source/zh/installation.md +++ b/docs/source/zh/installation.md @@ -72,7 +72,7 @@ pip install 'transformers[tf-cpu]' M1 / ARM用户 在安装 TensorFlow 2.0 前，你需要安装以下库： -``` +```bash brew install cmake brew install pkg-config ``` diff --git a/docs/source/zh/main_classes/deepspeed.md b/docs/source/zh/main_classes/deepspeed.md index f91f6c347c..85c5d017ef 100644 --- a/docs/source/zh/main_classes/deepspeed.md +++ b/docs/source/zh/main_classes/deepspeed.md @@ -2048,7 +2048,7 @@ print(f"rank{rank}:\n in={text_in}\n out={text_out}") ``` 让我们保存它为 `t0.py`并运行： -``` +```bash $ deepspeed --num_gpus 2 t0.py rank0: in=Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy @@ -2074,13 +2074,13 @@ rank1: 要运行DeepSpeed测试，请至少运行以下命令： -``` +```bash RUN_SLOW=1 pytest tests/deepspeed/test_deepspeed.py ``` 如果你更改了任何模型或PyTorch示例代码，请同时运行多模型测试。以下将运行所有DeepSpeed测试： -``` +```bash RUN_SLOW=1 pytest tests/deepspeed ``` diff --git a/docs/source/zh/perf_hardware.md b/docs/source/zh/perf_hardware.md index ce7ab36151..e193e09cd8 100644 --- a/docs/source/zh/perf_hardware.md +++ b/docs/source/zh/perf_hardware.md @@ -64,7 +64,7 @@ rendered properly in your Markdown viewer. 如果您使用多个GPU，则卡之间的互连方式可能会对总训练时间产生巨大影响。如果GPU位于同一物理节点上，您可以运行以下代码： -``` +```bash nvidia-smi topo -m ``` diff --git a/examples/legacy/seq2seq/README.md b/examples/legacy/seq2seq/README.md index 6a2e302a60..e6e3e20dcf 100644 --- a/examples/legacy/seq2seq/README.md +++ b/examples/legacy/seq2seq/README.md @@ -228,7 +228,7 @@ Contributions that implement this command for other distributed hardware setups When using `run_eval.py`, the following features can be useful: * if you running the script multiple times and want to make it easier to track what arguments produced that output, use `--dump-args`. Along with the results it will also dump any custom params that were passed to the script. For example if you used: `--num_beams 8 --early_stopping true`, the output will be: - ``` + ```json {'bleu': 26.887, 'n_obs': 10, 'runtime': 1, 'seconds_per_sample': 0.1, 'num_beams': 8, 'early_stopping': True} ``` @@ -236,13 +236,13 @@ When using `run_eval.py`, the following features can be useful: If using `--dump-args --info`, the output will be: - ``` + ```json {'bleu': 26.887, 'n_obs': 10, 'runtime': 1, 'seconds_per_sample': 0.1, 'num_beams': 8, 'early_stopping': True, 'info': '2020-09-13 18:44:43'} ``` If using `--dump-args --info "pair:en-ru chkpt=best`, the output will be: - ``` + ```json {'bleu': 26.887, 'n_obs': 10, 'runtime': 1, 'seconds_per_sample': 0.1, 'num_beams': 8, 'early_stopping': True, 'info': 'pair=en-ru chkpt=best'} ``` diff --git a/examples/pytorch/README.md b/examples/pytorch/README.md index a9e18a1e22..be3c9c52a0 100644 --- a/examples/pytorch/README.md +++ b/examples/pytorch/README.md @@ -53,7 +53,7 @@ Coming soon! Most examples are equipped with a mechanism to truncate the number of dataset samples to the desired length. This is useful for debugging purposes, for example to quickly check that all stages of the programs can complete, before running the same setup on the full dataset which may take hours to complete. For example here is how to truncate all three splits to just 50 samples each: -``` +```bash examples/pytorch/token-classification/run_ner.py \ --max_train_samples 50 \ --max_eval_samples 50 \ @@ -62,7 +62,7 @@ examples/pytorch/token-classification/run_ner.py \ ``` Most example scripts should have the first two command line arguments and some have the third one. You can quickly check if a given example supports any of these by passing a `-h` option, e.g.: -``` +```bash examples/pytorch/token-classification/run_ner.py -h ``` diff --git a/examples/pytorch/speech-recognition/README.md b/examples/pytorch/speech-recognition/README.md index 33039e67c6..8dbfcafe34 100644 --- a/examples/pytorch/speech-recognition/README.md +++ b/examples/pytorch/speech-recognition/README.md @@ -277,7 +277,7 @@ language or concept the adapter layers shall be trained. The adapter weights wil accordingly be called `adapter.{/bin/activate Next you should install JAX's TPU version on TPU by running the following command: -``` +```bash $ pip install requests ``` and then: -``` +```bash $ pip install "jax[tpu]>=0.2.16" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html ``` @@ -468,7 +468,7 @@ library from source to profit from the most current additions during the communi Simply run the following steps: -``` +```bash $ cd ~/ $ git clone https://github.com/huggingface/datasets.git $ cd datasets @@ -568,7 +568,7 @@ class ModelPyTorch: Instantiating an object `model_pytorch` of the class `ModelPyTorch` would actually allocate memory for the model weights and attach them to the attributes `self.key_proj`, `self.value_proj`, `self.query_proj`, and `self.logits.proj`. We could access the weights via: -``` +```python key_projection_matrix = model_pytorch.key_proj.weight.data ``` @@ -1224,25 +1224,25 @@ Sometimes you might be using different libraries or a very specific application A common use case is how to load files you have in your model repository in the Hub from the Streamlit demo. The `huggingface_hub` library is here to help you! -``` +```bash pip install huggingface_hub ``` Here is an example downloading (and caching!) a specific file directly from the Hub -``` +```python from huggingface_hub import hf_hub_download filepath = hf_hub_download("flax-community/roberta-base-als", "flax_model.msgpack"); ``` In many cases you will want to download the full repository. Here is an example downloading all the files from a repo. You can even specify specific revisions! -``` +```python from huggingface_hub import snapshot_download local_path = snapshot_download("flax-community/roberta-base-als"); ``` Note that if you're using 🤗 Transformers library, you can quickly load the model and tokenizer as follows -``` +```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("REPO_ID") diff --git a/examples/research_projects/jax-projects/dataset-streaming/README.md b/examples/research_projects/jax-projects/dataset-streaming/README.md index 35fc02acd2..bbb5803744 100644 --- a/examples/research_projects/jax-projects/dataset-streaming/README.md +++ b/examples/research_projects/jax-projects/dataset-streaming/README.md @@ -42,20 +42,20 @@ Here we call the model `"english-roberta-base-dummy"`, but you can change the mo You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that you are logged in) or via the command line: -``` +```bash huggingface-cli repo create english-roberta-base-dummy ``` Next we clone the model repository to add the tokenizer and model files. -``` +```bash git clone https://huggingface.co//english-roberta-base-dummy ``` To ensure that all tensorboard traces will be uploaded correctly, we need to track them. You can run the following command inside your model repo to do so. -``` +```bash cd english-roberta-base-dummy git lfs track "*tfevents*" ``` diff --git a/examples/research_projects/jax-projects/hybrid_clip/README.md b/examples/research_projects/jax-projects/hybrid_clip/README.md index 282d5c813b..76df92e463 100644 --- a/examples/research_projects/jax-projects/hybrid_clip/README.md +++ b/examples/research_projects/jax-projects/hybrid_clip/README.md @@ -43,17 +43,17 @@ Here we call the model `"clip-roberta-base"`, but you can change the model name You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that you are logged in) or via the command line: -``` +```bash huggingface-cli repo create clip-roberta-base ``` Next we clone the model repository to add the tokenizer and model files. -``` +```bash git clone https://huggingface.co//clip-roberta-base ``` To ensure that all tensorboard traces will be uploaded correctly, we need to track them. You can run the following command inside your model repo to do so. -``` +```bash cd clip-roberta-base git lfs track "*tfevents*" ``` diff --git a/examples/research_projects/jax-projects/wav2vec2/README.md b/examples/research_projects/jax-projects/wav2vec2/README.md index 200e7ad933..5f8e14f47c 100644 --- a/examples/research_projects/jax-projects/wav2vec2/README.md +++ b/examples/research_projects/jax-projects/wav2vec2/README.md @@ -18,20 +18,20 @@ Here we call the model `"wav2vec2-base-robust"`, but you can change the model na You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that you are logged in) or via the command line: -``` +```bash huggingface-cli repo create wav2vec2-base-robust ``` Next we clone the model repository to add the tokenizer and model files. -``` +```bash git clone https://huggingface.co//wav2vec2-base-robust ``` To ensure that all tensorboard traces will be uploaded correctly, we need to track them. You can run the following command inside your model repo to do so. -``` +```bash cd wav2vec2-base-robust git lfs track "*tfevents*" ``` diff --git a/examples/research_projects/mm-imdb/README.md b/examples/research_projects/mm-imdb/README.md index 7cfc2a7487..73e77aeb96 100644 --- a/examples/research_projects/mm-imdb/README.md +++ b/examples/research_projects/mm-imdb/README.md @@ -6,7 +6,7 @@ Based on the script [`run_mmimdb.py`](https://github.com/huggingface/transformer ### Training on MM-IMDb -``` +```bash python run_mmimdb.py \ --data_dir /path/to/mmimdb/dataset/ \ --model_type bert \ diff --git a/examples/research_projects/movement-pruning/README.md b/examples/research_projects/movement-pruning/README.md index 76c6601874..c2f74d6dcd 100644 --- a/examples/research_projects/movement-pruning/README.md +++ b/examples/research_projects/movement-pruning/README.md @@ -173,7 +173,7 @@ In particular, hardware manufacturers are announcing devices that will speedup i If you find this resource useful, please consider citing the following paper: -``` +```bibtex @article{sanh2020movement, title={Movement Pruning: Adaptive Sparsity by Fine-Tuning}, author={Victor Sanh and Thomas Wolf and Alexander M. Rush}, diff --git a/examples/research_projects/quantization-qdqbert/README.md b/examples/research_projects/quantization-qdqbert/README.md index fe69819cc5..4d459c4c71 100644 --- a/examples/research_projects/quantization-qdqbert/README.md +++ b/examples/research_projects/quantization-qdqbert/README.md @@ -30,17 +30,17 @@ Required: ## Setup the environment with Dockerfile Under the directory of `transformers/`, build the docker image: -``` +```bash docker build . -f examples/research_projects/quantization-qdqbert/Dockerfile -t bert_quantization:latest ``` Run the docker: -``` +```bash docker run --gpus all --privileged --rm -it --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 bert_quantization:latest ``` In the container: -``` +```bash cd transformers/examples/research_projects/quantization-qdqbert/ ``` @@ -48,7 +48,7 @@ cd transformers/examples/research_projects/quantization-qdqbert/ Calibrate the pretrained model and finetune with quantization awared: -``` +```bash python3 run_quant_qa.py \ --model_name_or_path bert-base-uncased \ --dataset_name squad \ @@ -60,7 +60,7 @@ python3 run_quant_qa.py \ --percentile 99.99 ``` -``` +```bash python3 run_quant_qa.py \ --model_name_or_path calib/bert-base-uncased \ --dataset_name squad \ @@ -80,7 +80,7 @@ python3 run_quant_qa.py \ To export the QAT model finetuned above: -``` +```bash python3 run_quant_qa.py \ --model_name_or_path finetuned_int8/bert-base-uncased \ --output_dir ./ \ @@ -97,19 +97,19 @@ Recalibrating will affect the accuracy of the model, but the change should be mi ### Benchmark the INT8 QAT ONNX model inference with TensorRT using dummy input -``` +```bash trtexec --onnx=model.onnx --explicitBatch --workspace=16384 --int8 --shapes=input_ids:64x128,attention_mask:64x128,token_type_ids:64x128 --verbose ``` ### Benchmark the INT8 QAT ONNX model inference with [ONNX Runtime-TRT](https://onnxruntime.ai/docs/execution-providers/TensorRT-ExecutionProvider.html) using dummy input -``` +```bash python3 ort-infer-benchmark.py ``` ### Evaluate the INT8 QAT ONNX model inference with TensorRT -``` +```bash python3 evaluate-hf-trt-qa.py \ --onnx_model_path=./model.onnx \ --output_dir ./ \ @@ -126,7 +126,7 @@ python3 evaluate-hf-trt-qa.py \ Finetune a fp32 precision model with [transformers/examples/pytorch/question-answering/](../../pytorch/question-answering/): -``` +```bash python3 ../../pytorch/question-answering/run_qa.py \ --model_name_or_path bert-base-uncased \ --dataset_name squad \ @@ -145,7 +145,7 @@ python3 ../../pytorch/question-answering/run_qa.py \ ### PTQ by calibrating and evaluating the finetuned FP32 model above: -``` +```bash python3 run_quant_qa.py \ --model_name_or_path ./finetuned_fp32/bert-base-uncased \ --dataset_name squad \ @@ -161,7 +161,7 @@ python3 run_quant_qa.py \ ### Export the INT8 PTQ model to ONNX -``` +```bash python3 run_quant_qa.py \ --model_name_or_path ./calib/bert-base-uncased \ --output_dir ./ \ @@ -175,7 +175,7 @@ python3 run_quant_qa.py \ ### Evaluate the INT8 PTQ ONNX model inference with TensorRT -``` +```bash python3 evaluate-hf-trt-qa.py \ --onnx_model_path=./model.onnx \ --output_dir ./ \ diff --git a/examples/research_projects/rag/README.md b/examples/research_projects/rag/README.md index eae1d863fd..7fbaea84b9 100644 --- a/examples/research_projects/rag/README.md +++ b/examples/research_projects/rag/README.md @@ -45,7 +45,7 @@ We publish two `base` models which can serve as a starting point for finetuning The `base` models initialize the question encoder with [`facebook/dpr-question_encoder-single-nq-base`](https://huggingface.co/facebook/dpr-question_encoder-single-nq-base) and the generator with [`facebook/bart-large`](https://huggingface.co/facebook/bart-large). If you would like to initialize finetuning with a base model using different question encoder and generator architectures, you can build it with a consolidation script, e.g.: -``` +```bash python examples/research_projects/rag/consolidate_rag_checkpoint.py \ --model_type rag_sequence \ --generator_name_or_path facebook/bart-large-cnn \ diff --git a/examples/research_projects/robust-speech-event/README.md b/examples/research_projects/robust-speech-event/README.md index 7e63cfde57..5c7bf42a00 100644 --- a/examples/research_projects/robust-speech-event/README.md +++ b/examples/research_projects/robust-speech-event/README.md @@ -216,7 +216,7 @@ library from source to profit from the most current additions during the communi Simply run the following steps: -``` +```bash $ cd ~/ $ git clone https://github.com/huggingface/datasets.git $ cd datasets diff --git a/examples/research_projects/vqgan-clip/README.md b/examples/research_projects/vqgan-clip/README.md index aef9509354..a74bf9209b 100644 --- a/examples/research_projects/vqgan-clip/README.md +++ b/examples/research_projects/vqgan-clip/README.md @@ -21,7 +21,7 @@ To install locally: In the root of the repo run: -``` +```bash conda create -n vqganclip python=3.8 conda activate vqganclip git-lfs install @@ -30,7 +30,7 @@ pip install -r requirements.txt ``` ### Generate new images -``` +```python from VQGAN_CLIP import VQGAN_CLIP vqgan_clip = VQGAN_CLIP() vqgan_clip.generate("a picture of a smiling woman") @@ -41,7 +41,7 @@ To get a test image, run `git clone https://huggingface.co/datasets/erwann/vqgan-clip-pic test_images` To edit: -``` +```python from VQGAN_CLIP import VQGAN_CLIP vqgan_clip = VQGAN_CLIP() diff --git a/examples/research_projects/wav2vec2/FINE_TUNE_XLSR_WAV2VEC2.md b/examples/research_projects/wav2vec2/FINE_TUNE_XLSR_WAV2VEC2.md index d8a4e11087..52553532fe 100644 --- a/examples/research_projects/wav2vec2/FINE_TUNE_XLSR_WAV2VEC2.md +++ b/examples/research_projects/wav2vec2/FINE_TUNE_XLSR_WAV2VEC2.md @@ -138,20 +138,20 @@ For bigger datasets, we recommend to train Wav2Vec2 locally instead of in a goog First, you need to clone the `transformers` repo with: -``` +```bash $ git clone https://github.com/huggingface/transformers.git ``` Second, head over to the `examples/research_projects/wav2vec2` directory, where the `run_common_voice.py` script is located. -``` +```bash $ cd transformers/examples/research_projects/wav2vec2 ``` Third, install the required packages. The packages are listed in the `requirements.txt` file and can be installed with -``` +```bash $ pip install -r requirements.txt ``` @@ -259,7 +259,7 @@ Then and add the following files that fully define a XLSR-Wav2Vec2 checkpoint in - `pytorch_model.bin` Having added the above files, you should run the following to push files to your model repository. -``` +```bash git add . && git commit -m "Add model files" && git push ``` diff --git a/examples/research_projects/wav2vec2/README.md b/examples/research_projects/wav2vec2/README.md index 1dcd8dcc28..cc667d6567 100644 --- a/examples/research_projects/wav2vec2/README.md +++ b/examples/research_projects/wav2vec2/README.md @@ -134,7 +134,7 @@ which helps with capping GPU memory usage. To learn how to deploy Deepspeed Integration please refer to [this guide](https://huggingface.co/transformers/main/main_classes/deepspeed.html#deepspeed-trainer-integration). But to get started quickly all you need is to install: -``` +```bash pip install deepspeed ``` and then use the default configuration files in this directory: @@ -148,7 +148,7 @@ Here are examples of how you can use DeepSpeed: ZeRO-2: -``` +```bash PYTHONPATH=../../../src deepspeed --num_gpus 2 \ run_asr.py \ --output_dir=output_dir --num_train_epochs=2 --per_device_train_batch_size=2 \ @@ -162,7 +162,7 @@ run_asr.py \ ``` For ZeRO-2 with more than 1 gpu you need to use (which is already in the example configuration file): -``` +```json "zero_optimization": { ... "find_unused_parameters": true, @@ -172,7 +172,7 @@ For ZeRO-2 with more than 1 gpu you need to use (which is already in the example ZeRO-3: -``` +```bash PYTHONPATH=../../../src deepspeed --num_gpus 2 \ run_asr.py \ --output_dir=output_dir --num_train_epochs=2 --per_device_train_batch_size=2 \ @@ -192,7 +192,7 @@ It is recommended to pre-train Wav2Vec2 with Trainer + Deepspeed (please refer t Here is an example of how you can use DeepSpeed ZeRO-2 to pretrain a small Wav2Vec2 model: -``` +```bash PYTHONPATH=../../../src deepspeed --num_gpus 4 run_pretrain.py \ --output_dir="./wav2vec2-base-libri-100h" \ --num_train_epochs="3" \ @@ -238,7 +238,7 @@ Output directory will contain 0000.txt and 0001.txt. Each file will have format #### Run command -``` +```bash python alignment.py \ --model_name="arijitx/wav2vec2-xls-r-300m-bengali" \ --wav_dir="./wavs" diff --git a/examples/research_projects/zero-shot-distillation/README.md b/examples/research_projects/zero-shot-distillation/README.md index cbc33071f0..14b6a8ea07 100644 --- a/examples/research_projects/zero-shot-distillation/README.md +++ b/examples/research_projects/zero-shot-distillation/README.md @@ -21,7 +21,7 @@ classification performance to the original zero-shot model A teacher NLI model can be distilled to a more efficient student model by running [`distill_classifier.py`](https://github.com/huggingface/transformers/blob/main/examples/research_projects/zero-shot-distillation/distill_classifier.py): -``` +```bash python distill_classifier.py \ --data_file \ --class_names_file \ diff --git a/examples/tensorflow/language-modeling/README.md b/examples/tensorflow/language-modeling/README.md index b96217c1f5..e91639adb0 100644 --- a/examples/tensorflow/language-modeling/README.md +++ b/examples/tensorflow/language-modeling/README.md @@ -41,7 +41,7 @@ can also be used by passing the name of the TPU resource with the `--tpu` argume This script trains a masked language model. ### Example command -``` +```bash python run_mlm.py \ --model_name_or_path distilbert-base-cased \ --output_dir output \ @@ -50,7 +50,7 @@ python run_mlm.py \ ``` When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation. -``` +```bash python run_mlm.py \ --model_name_or_path distilbert-base-cased \ --output_dir output \ @@ -62,7 +62,7 @@ python run_mlm.py \ This script trains a causal language model. ### Example command -``` +```bash python run_clm.py \ --model_name_or_path distilgpt2 \ --output_dir output \ @@ -72,7 +72,7 @@ python run_clm.py \ When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation. -``` +```bash python run_clm.py \ --model_name_or_path distilgpt2 \ --output_dir output \ diff --git a/examples/tensorflow/question-answering/README.md b/examples/tensorflow/question-answering/README.md index b7c0443b1b..b347ffad81 100644 --- a/examples/tensorflow/question-answering/README.md +++ b/examples/tensorflow/question-answering/README.md @@ -45,7 +45,7 @@ README, but for more information you can see the 'Input Datasets' section of [this document](https://www.tensorflow.org/guide/tpu). ### Example command -``` +```bash python run_qa.py \ --model_name_or_path distilbert-base-cased \ --output_dir output \ diff --git a/examples/tensorflow/text-classification/README.md b/examples/tensorflow/text-classification/README.md index 898cfa7014..39ce915303 100644 --- a/examples/tensorflow/text-classification/README.md +++ b/examples/tensorflow/text-classification/README.md @@ -36,7 +36,7 @@ may not always be what you want, especially if you have more than two fields! Here is a snippet of a valid input JSON file, though note that your texts can be much longer than these, and are not constrained (despite the field name) to being single grammatical sentences: -``` +```json {"sentence1": "COVID-19 vaccine updates: How is the rollout proceeding?", "label": "news"} {"sentence1": "Manchester United celebrates Europa League success", "label": "sports"} ``` @@ -69,7 +69,7 @@ README, but for more information you can see the 'Input Datasets' section of [this document](https://www.tensorflow.org/guide/tpu). ### Example command -``` +```bash python run_text_classification.py \ --model_name_or_path distilbert-base-cased \ --train_file training_data.json \ @@ -101,7 +101,7 @@ README, but for more information you can see the 'Input Datasets' section of [this document](https://www.tensorflow.org/guide/tpu). ### Example command -``` +```bash python run_glue.py \ --model_name_or_path distilbert-base-cased \ --task_name mnli \ diff --git a/scripts/tatoeba/README.md b/scripts/tatoeba/README.md index 94bb167d51..b142039b24 100644 --- a/scripts/tatoeba/README.md +++ b/scripts/tatoeba/README.md @@ -23,7 +23,7 @@ pip install pandas GitPython wget ``` Get required metadata -``` +```bash curl https://cdn-datasets.huggingface.co/language_codes/language-codes-3b2.csv > language-codes-3b2.csv curl https://cdn-datasets.huggingface.co/language_codes/iso-639-3.csv > iso-639-3.csv ``` diff --git a/templates/adding_a_new_example_script/README.md b/templates/adding_a_new_example_script/README.md index cbab2f3c3a..87aa385aec 100644 --- a/templates/adding_a_new_example_script/README.md +++ b/templates/adding_a_new_example_script/README.md @@ -18,13 +18,13 @@ limitations under the License. This folder provide a template for adding a new example script implementing a training or inference task with the models in the 🤗 Transformers library. To use it, you will need to install cookiecutter: -``` +```bash pip install cookiecutter ``` or refer to the installation page of the [cookiecutter documentation](https://cookiecutter.readthedocs.io/). You can then run the following command inside the `examples` folder of the transformers repo: -``` +```bash cookiecutter ../templates/adding_a_new_example_script/ ``` and answer the questions asked, which will generate a new folder where you will find a pre-filled template for your diff --git a/templates/adding_a_new_model/ADD_NEW_MODEL_PROPOSAL_TEMPLATE.md b/templates/adding_a_new_model/ADD_NEW_MODEL_PROPOSAL_TEMPLATE.md index 2018068375..dc7143465d 100644 --- a/templates/adding_a_new_model/ADD_NEW_MODEL_PROPOSAL_TEMPLATE.md +++ b/templates/adding_a_new_model/ADD_NEW_MODEL_PROPOSAL_TEMPLATE.md @@ -582,27 +582,27 @@ You should do the following: 1. Create a branch with a descriptive name from your main branch -``` +```bash git checkout -b add_[lowercase name of model] ``` 2. Commit the automatically generated code: -``` +```bash git add . git commit ``` 3. Fetch and rebase to current main -``` +```bash git fetch upstream git rebase upstream/main ``` 4. Push the changes to your account using: -``` +```bash git push -u origin a-descriptive-name-for-my-changes ``` diff --git a/templates/adding_a_new_model/README.md b/templates/adding_a_new_model/README.md index 024a664283..9f3b9161ff 100644 --- a/templates/adding_a_new_model/README.md +++ b/templates/adding_a_new_model/README.md @@ -103,7 +103,7 @@ tests/test_modeling_tf_.py You can run the tests to ensure that they all pass: -``` +```bash python -m pytest ./tests/test_**.py ``` @@ -236,7 +236,7 @@ depending on your choices). You will also see a doc file and tests for your new models. First you should run -``` +```bash make style make fix-copies ``` @@ -247,7 +247,7 @@ and then you can start tweaking your model. You should: Once you're done, you can run the tests to ensure that they all pass: -``` +```bash python -m pytest ./tests/test_**.py ``` diff --git a/templates/adding_a_new_model/open_model_proposals/ADD_BIG_BIRD.md b/templates/adding_a_new_model/open_model_proposals/ADD_BIG_BIRD.md index be10dadc0b..02c9fa32a2 100644 --- a/templates/adding_a_new_model/open_model_proposals/ADD_BIG_BIRD.md +++ b/templates/adding_a_new_model/open_model_proposals/ADD_BIG_BIRD.md @@ -593,27 +593,27 @@ You should do the following: 1. Create a branch with a descriptive name from your main branch -``` +```bash git checkout -b add_big_bird ``` 2. Commit the automatically generated code: -``` +```bash git add . git commit ``` 3. Fetch and rebase to current main -``` +```bash git fetch upstream git rebase upstream/main ``` 4. Push the changes to your account using: -``` +```bash git push -u origin a-descriptive-name-for-my-changes ``` diff --git a/tests/quantization/bnb/README.md b/tests/quantization/bnb/README.md index 3c1d3a0791..8155548c84 100644 --- a/tests/quantization/bnb/README.md +++ b/tests/quantization/bnb/README.md @@ -22,7 +22,7 @@ pip install accelerate>=0.12.0 pip install transformers>=4.23.0 ``` if `transformers>=4.23.0` is not released yet, then use: -``` +```bash pip install git+https://github.com/huggingface/transformers.git ``` @@ -72,15 +72,15 @@ Run your script by pre-pending `CUDA_LAUNCH_BLOCKING=1` and you should observe a ### `CUDA illegal memory error: an illegal memory access at line...`: Check the CUDA verisons with: -``` +```bash nvcc --version ``` and confirm it is the same version as the one detected by `bitsandbytes`. If not, run: -``` +```bash ls -l $CONDA_PREFIX/lib/libcudart.so ``` or -``` +```bash ls -l $LD_LIBRARY_PATH ``` Check if `libcudart.so` has a correct symlink that is set. Sometimes `nvcc` detects the correct CUDA version but `bitsandbytes` doesn't. You have to make sure that the symlink that is set for the file `libcudart.so` is redirected to the correct CUDA file.