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Sometimes, older CUDA versions may refuse to build with newer compilers. For example, if you have `gcc-9` but CUDA wants `gcc-7`. Usually, installing the latest CUDA toolkit enables support for the newer compiler.
You could also install an older version of the compiler in addition to the one you're currently using (o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/debugging.md | https://huggingface.co/docs/transformers/en/debugging/#older-cuda-versions | #older-cuda-versions | .md | 60_5 |
If you're still having issues with installing DeepSpeed or if you're building DeepSpeed at run time, you can try to prebuild the DeepSpeed modules before installing them. To make a local build for DeepSpeed:
```bash
git clone https://github.com/microsoft/DeepSpeed/
cd DeepSpeed
rm -rf build
TORCH_CUDA_ARCH_LIST="8.6"... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/debugging.md | https://huggingface.co/docs/transformers/en/debugging/#prebuild | #prebuild | .md | 60_6 |
When training or inferencing with `DistributedDataParallel` and multiple GPU, if you run into issue of inter-communication between processes and/or nodes, you can use the following script to diagnose network issues.
```bash
wget https://raw.githubusercontent.com/huggingface/transformers/main/scripts/distributed/torch... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/debugging.md | https://huggingface.co/docs/transformers/en/debugging/#multi-gpu-network-issues-debug | #multi-gpu-network-issues-debug | .md | 60_7 |
<Tip>
This feature is currently available for PyTorch-only.
</Tip>
<Tip>
For multi-GPU training it requires DDP (`torch.distributed.launch`).
</Tip>
<Tip>
This feature can be used with any `nn.Module`-based model.
</Tip>
If you start getting `loss=NaN` or the model exhibits some other abnormal behavio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/debugging.md | https://huggingface.co/docs/transformers/en/debugging/#underflow-and-overflow-detection | #underflow-and-overflow-detection | .md | 60_8 |
The same debugging class can be used for per-batch tracing with the underflow/overflow detection feature turned off.
Let's say you want to watch the absolute min and max values for all the ingredients of each `forward` call of a given
batch, and only do that for batches 1 and 3. Then you instantiate this class as:
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/debugging.md | https://huggingface.co/docs/transformers/en/debugging/#specific-batch-absolute-min-and-max-value-tracing | #specific-batch-absolute-min-and-max-value-tracing | .md | 60_9 |
<!--Copyright 2022 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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/create_a_model.md | https://huggingface.co/docs/transformers/en/create_a_model/ | .md | 61_0 | |
An [`AutoClass`](model_doc/auto) automatically infers the model architecture and downloads pretrained configuration and weights. Generally, we recommend using an `AutoClass` to produce checkpoint-agnostic code. But users who want more control over specific model parameters can create a custom 🤗 Transformers model from... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/create_a_model.md | https://huggingface.co/docs/transformers/en/create_a_model/#create-a-custom-architecture | #create-a-custom-architecture | .md | 61_1 |
A [configuration](main_classes/configuration) refers to a model's specific attributes. Each model configuration has different attributes; for instance, all NLP models have the `hidden_size`, `num_attention_heads`, `num_hidden_layers` and `vocab_size` attributes in common. These attributes specify the number of attentio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/create_a_model.md | https://huggingface.co/docs/transformers/en/create_a_model/#configuration | #configuration | .md | 61_2 |
The next step is to create a [model](main_classes/models). The model - also loosely referred to as the architecture - defines what each layer is doing and what operations are happening. Attributes like `num_hidden_layers` from the configuration are used to define the architecture. Every model shares the base class [`Pr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/create_a_model.md | https://huggingface.co/docs/transformers/en/create_a_model/#model | #model | .md | 61_3 |
At this point, you have a base DistilBERT model which outputs the *hidden states*. The hidden states are passed as inputs to a model head to produce the final output. 🤗 Transformers provides a different model head for each task as long as a model supports the task (i.e., you can't use DistilBERT for a sequence-to-sequ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/create_a_model.md | https://huggingface.co/docs/transformers/en/create_a_model/#model-heads | #model-heads | .md | 61_4 |
The last base class you need before using a model for textual data is a [tokenizer](main_classes/tokenizer) to convert raw text to tensors. There are two types of tokenizers you can use with 🤗 Transformers:
- [`PreTrainedTokenizer`]: a Python implementation of a tokenizer.
- [`PreTrainedTokenizerFast`]: a tokenizer ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/create_a_model.md | https://huggingface.co/docs/transformers/en/create_a_model/#tokenizer | #tokenizer | .md | 61_5 |
An image processor processes vision inputs. It inherits from the base [`~image_processing_utils.ImageProcessingMixin`] class.
To use, create an image processor associated with the model you're using. For example, create a default [`ViTImageProcessor`] if you are using [ViT](model_doc/vit) for image classification:
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/create_a_model.md | https://huggingface.co/docs/transformers/en/create_a_model/#image-processor | #image-processor | .md | 61_6 |
<div style="text-align: center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Backbone.png">
</div>
Computer vision models consist of a backbone, neck, and head. The backbone extracts features from an input image, the neck combines and enhances the extracted fea... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/create_a_model.md | https://huggingface.co/docs/transformers/en/create_a_model/#backbone | #backbone | .md | 61_7 |
A feature extractor processes audio inputs. It inherits from the base [`~feature_extraction_utils.FeatureExtractionMixin`] class, and may also inherit from the [`SequenceFeatureExtractor`] class for processing audio inputs.
To use, create a feature extractor associated with the model you're using. For example, create... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/create_a_model.md | https://huggingface.co/docs/transformers/en/create_a_model/#feature-extractor | #feature-extractor | .md | 61_8 |
For models that support multimodal tasks, 🤗 Transformers offers a processor class that conveniently wraps processing classes such as a feature extractor and a tokenizer into a single object. For example, let's use the [`Wav2Vec2Processor`] for an automatic speech recognition task (ASR). ASR transcribes audio to text, ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/create_a_model.md | https://huggingface.co/docs/transformers/en/create_a_model/#processor | #processor | .md | 61_9 |
<!--Copyright 2022 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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/ | .md | 62_0 | |
This guide demonstrates practical techniques that you can use to increase the efficiency of your model's training by
optimizing memory utilization, speeding up the training, or both. If you'd like to understand how GPU is utilized during
training, please refer to the [Model training anatomy](model_memory_anatomy) conce... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#methods-and-tools-for-efficient-training-on-a-single-gpu | #methods-and-tools-for-efficient-training-on-a-single-gpu | .md | 62_1 |
To achieve optimal performance, start by identifying the appropriate batch size. It is recommended to use batch sizes and
input/output neuron counts that are of size 2^N. Often it's a multiple of 8, but it can be
higher depending on the hardware being used and the model's dtype.
For reference, check out NVIDIA's reco... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#batch-size-choice | #batch-size-choice | .md | 62_2 |
The **gradient accumulation** method aims to calculate gradients in smaller increments instead of computing them for the
entire batch at once. This approach involves iteratively calculating gradients in smaller batches by performing forward
and backward passes through the model and accumulating the gradients during the... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#gradient-accumulation | #gradient-accumulation | .md | 62_3 |
Some large models may still face memory issues even when the batch size is set to 1 and gradient accumulation is used.
This is because there are other components that also require memory storage.
Saving all activations from the forward pass in order to compute the gradients during the backward pass can result in
sign... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#gradient-checkpointing | #gradient-checkpointing | .md | 62_4 |
**Mixed precision training** is a technique that aims to optimize the computational efficiency of training models by
utilizing lower-precision numerical formats for certain variables. Traditionally, most models use 32-bit floating point
precision (fp32 or float32) to represent and process variables. However, not all va... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#mixed-precision-training | #mixed-precision-training | .md | 62_5 |
The main advantage of mixed precision training comes from saving the activations in half precision (fp16).
Although the gradients are also computed in half precision they are converted back to full precision for the optimization
step so no memory is saved here.
While mixed precision training results in faster computati... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#fp16 | #fp16 | .md | 62_6 |
If you have access to an Ampere or newer hardware you can use bf16 for mixed precision training and evaluation. While
bf16 has a worse precision than fp16, it has a much bigger dynamic range. In fp16 the biggest number you can have
is `65504` and any number above that will result in an overflow. A bf16 number can be as... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#bf16 | #bf16 | .md | 62_7 |
The Ampere hardware uses a magical data type called tf32. It has the same numerical range as fp32 (8-bits), but instead
of 23 bits precision it has only 10 bits (same as fp16) and uses only 19 bits in total. It's "magical" in the sense that
you can use the normal fp32 training and/or inference code and by enabling tf32... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#tf32 | #tf32 | .md | 62_8 |
You can speedup the training throughput by using Flash Attention 2 integration in transformers. Check out the appropriate section in the [single GPU section](./perf_infer_gpu_one#Flash-Attention-2) to learn more about how to load a model with Flash Attention 2 modules. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#flash-attention-2 | #flash-attention-2 | .md | 62_9 |
The most common optimizer used to train transformer models is Adam or AdamW (Adam with weight decay). Adam achieves
good convergence by storing the rolling average of the previous gradients; however, it adds an additional memory
footprint of the order of the number of model parameters. To remedy this, you can use an al... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#optimizer-choice | #optimizer-choice | .md | 62_10 |
Adafactor doesn't store rolling averages for each element in weight matrices. Instead, it keeps aggregated information
(sums of rolling averages row- and column-wise), significantly reducing its footprint. However, compared to Adam,
Adafactor may have slower convergence in certain cases.
You can switch to Adafactor b... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#adafactor | #adafactor | .md | 62_11 |
Instead of aggregating optimizer states like Adafactor, 8-bit Adam keeps the full state and quantizes it. Quantization
means that it stores the state with lower precision and dequantizes it only for the optimization. This is similar to the
idea behind mixed precision training.
To use `adamw_bnb_8bit`, you simply need... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#8-bit-adam | #8-bit-adam | .md | 62_12 |
pytorch-nightly introduced `torch.optim._multi_tensor` which should significantly speed up the optimizers for situations
with lots of small feature tensors. It should eventually become the default, but if you want to experiment with it sooner, take a look at this GitHub [issue](https://github.com/huggingface/transforme... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#multitensor | #multitensor | .md | 62_13 |
One of the important requirements to reach great training speed is the ability to feed the GPU at the maximum speed it
can handle. By default, everything happens in the main process, and it might not be able to read the data from disk fast
enough, and thus create a bottleneck, leading to GPU under-utilization. Configur... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#data-preloading | #data-preloading | .md | 62_14 |
DeepSpeed is an open-source deep learning optimization library that is integrated with 🤗 Transformers and 🤗 Accelerate.
It provides a wide range of features and optimizations designed to improve the efficiency and scalability of large-scale
deep learning training.
If your model fits onto a single GPU and you have e... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#deepspeed-zero | #deepspeed-zero | .md | 62_15 |
PyTorch 2.0 introduced a new compile function that doesn't require any modification to existing PyTorch code but can
optimize your code by adding a single line of code: `model = torch.compile(model)`.
If using [`Trainer`], you only need `to` pass the `torch_compile` option in the [`TrainingArguments`]:
```python
tr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#using-torchcompile | #using-torchcompile | .md | 62_16 |
[Parameter-Efficient Fine Tuning (PEFT)](https://huggingface.co/blog/peft) methods freeze the pretrained model parameters during fine-tuning and add a small number of trainable parameters (the adapters) on top of it.
As a result the [memory associated to the optimizer states and gradients](https://huggingface.co/docs... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#using--peft | #using--peft | .md | 62_17 |
With [🤗 Accelerate](https://huggingface.co/docs/accelerate/index) you can use the above methods while gaining full
control over the training loop and can essentially write the loop in pure PyTorch with some minor modifications.
Suppose you have combined the methods in the [`TrainingArguments`] like so:
```py
train... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#using--accelerate | #using--accelerate | .md | 62_18 |
PyTorch's [pip and conda builds](https://pytorch.org/get-started/locally/#start-locally) come prebuilt with the cuda toolkit
which is enough to run PyTorch, but it is insufficient if you need to build cuda extensions.
At times, additional efforts may be required to pre-build some components. For instance, if you're u... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#efficient-software-prebuilds | #efficient-software-prebuilds | .md | 62_19 |
Some recent papers reported a 4-5x training speedup and a faster inference by integrating
Mixture of Experts (MoE) into the Transformer models.
Since it has been discovered that more parameters lead to better performance, this technique allows to increase the
number of parameters by an order of magnitude without incr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#mixture-of-experts | #mixture-of-experts | .md | 62_20 |
PyTorch's [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html) (SDPA) can also call FlashAttention and memory-efficient attention kernels under the hood. SDPA support is currently being added natively in Transformers and is... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_one.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_one/#using-pytorch-native-attention-and-flash-attention | #using-pytorch-native-attention-and-flash-attention | .md | 62_21 |
<!---
Copyright 2022 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 a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/ | .md | 63_0 | |
Install 🤗 Transformers for whichever deep learning library you're working with, setup your cache, and optionally configure 🤗 Transformers to run offline.
🤗 Transformers is tested on Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, and Flax. Follow the installation instructions below for the deep learning library you ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/#installation | #installation | .md | 63_1 |
You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, take a look at this [guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). A virtual environment makes it easier to man... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/#install-with-pip | #install-with-pip | .md | 63_2 |
Install 🤗 Transformers from source with the following command:
```bash
pip install git+https://github.com/huggingface/transformers
```
This command installs the bleeding edge `main` version rather than the latest `stable` version. The `main` version is useful for staying up-to-date with the latest developments. Fo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/#install-from-source | #install-from-source | .md | 63_3 |
You will need an editable install if you'd like to:
* Use the `main` version of the source code.
* Contribute to 🤗 Transformers and need to test changes in the code.
Clone the repository and install 🤗 Transformers with the following commands:
```bash
git clone https://github.com/huggingface/transformers.git
cd ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/#editable-install | #editable-install | .md | 63_4 |
Install from the conda channel `conda-forge`:
```bash
conda install conda-forge::transformers
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/#install-with-conda | #install-with-conda | .md | 63_5 |
Pretrained models are downloaded and locally cached at: `~/.cache/huggingface/hub`. This is the default directory given by the shell environment variable `TRANSFORMERS_CACHE`. On Windows, the default directory is given by `C:\Users\username\.cache\huggingface\hub`. You can change the shell environment variables shown b... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/#cache-setup | #cache-setup | .md | 63_6 |
Run 🤗 Transformers in a firewalled or offline environment with locally cached files by setting the environment variable `HF_HUB_OFFLINE=1`.
<Tip>
Add [🤗 Datasets](https://huggingface.co/docs/datasets/) to your offline training workflow with the environment variable `HF_DATASETS_OFFLINE=1`.
</Tip>
```bash
HF_D... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/#offline-mode | #offline-mode | .md | 63_7 |
Another option for using 🤗 Transformers offline is to download the files ahead of time, and then point to their local path when you need to use them offline. There are three ways to do this:
* Download a file through the user interface on the [Model Hub](https://huggingface.co/models) by clicking on the ↓ icon.
![... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/#fetch-models-and-tokenizers-to-use-offline | #fetch-models-and-tokenizers-to-use-offline | .md | 63_8 |
See below for some of the more common installation issues and how to resolve them. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/#troubleshooting | #troubleshooting | .md | 63_9 |
Ensure you are using Python 3.9 or later. Run the command below to check your Python version.
```
python --version
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/#unsupported-python-version | #unsupported-python-version | .md | 63_10 |
Install all required dependencies by running the following command. Ensure you’re in the project directory before executing the command.
```
pip install -r requirements.txt
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/#missing-dependencies | #missing-dependencies | .md | 63_11 |
If you encounter issues on Windows, you may need to activate Developer Mode. Navigate to Windows Settings > For Developers > Developer Mode.
Alternatively, create and activate a virtual environment as shown below.
```
python -m venv env
.\env\Scripts\activate
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/installation.md | https://huggingface.co/docs/transformers/en/installation/#windows-specific | #windows-specific | .md | 63_12 |
<!--Copyright 2024 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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/agents_advanced.md | https://huggingface.co/docs/transformers/en/agents_advanced/ | .md | 64_0 | |
[[open-in-colab]] | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/agents_advanced.md | https://huggingface.co/docs/transformers/en/agents_advanced/#agents-supercharged---multi-agents-external-tools-and-more | #agents-supercharged---multi-agents-external-tools-and-more | .md | 64_1 |
> [!TIP]
> If you're new to `transformers.agents`, make sure to first read the main [agents documentation](./agents).
In this page we're going to highlight several advanced uses of `transformers.agents`. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/agents_advanced.md | https://huggingface.co/docs/transformers/en/agents_advanced/#what-is-an-agent | #what-is-an-agent | .md | 64_2 |
Multi-agent has been introduced in Microsoft's framework [Autogen](https://huggingface.co/papers/2308.08155).
It simply means having several agents working together to solve your task instead of only one.
It empirically yields better performance on most benchmarks. The reason for this better performance is conceptually... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/agents_advanced.md | https://huggingface.co/docs/transformers/en/agents_advanced/#multi-agents | #multi-agents | .md | 64_3 |
Let's take again the tool example from main documentation, for which we had implemented a `tool` decorator.
If you need to add variation, like custom attributes for your tool, you can build your tool following the fine-grained method: building a class that inherits from the [`Tool`] superclass.
The custom tool need... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/agents_advanced.md | https://huggingface.co/docs/transformers/en/agents_advanced/#directly-define-a-tool-by-subclassing-tool-and-share-it-to-the-hub | #directly-define-a-tool-by-subclassing-tool-and-share-it-to-the-hub | .md | 64_4 |
You can directly import a Space from the Hub as a tool using the [`Tool.from_space`] method!
You only need to provide the id of the Space on the Hub, its name, and a description that will help you agent understand what the tool does. Under the hood, this will use [`gradio-client`](https://pypi.org/project/gradio-clie... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/agents_advanced.md | https://huggingface.co/docs/transformers/en/agents_advanced/#import-a-space-as-a-tool- | #import-a-space-as-a-tool- | .md | 64_5 |
[gradio-tools](https://github.com/freddyaboulton/gradio-tools) is a powerful library that allows using Hugging
Face Spaces as tools. It supports many existing Spaces as well as custom Spaces.
Transformers supports `gradio_tools` with the [`Tool.from_gradio`] method. For example, let's use the [`StableDiffusionPromptG... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/agents_advanced.md | https://huggingface.co/docs/transformers/en/agents_advanced/#use-gradio-tools | #use-gradio-tools | .md | 64_6 |
We love Langchain and think it has a very compelling suite of tools.
To import a tool from LangChain, use the `from_langchain()` method.
Here is how you can use it to recreate the intro's search result using a LangChain web search tool.
This tool will need `pip install google-search-results` to work properly.
```pyth... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/agents_advanced.md | https://huggingface.co/docs/transformers/en/agents_advanced/#use-langchain-tools | #use-langchain-tools | .md | 64_7 |
You can leverage `gradio.Chatbot` to display your agent's thoughts using `stream_to_gradio`, here is an example:
```py
import gradio as gr
from transformers import (
load_tool,
ReactCodeAgent,
HfApiEngine,
stream_to_gradio,
)
# Import tool from Hub
image_generation_tool = load_tool("m-ric/text-to-image")
llm_engine... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/agents_advanced.md | https://huggingface.co/docs/transformers/en/agents_advanced/#display-your-agent-run-in-a-cool-gradio-interface | #display-your-agent-run-in-a-cool-gradio-interface | .md | 64_8 |
<!---
Copyright 2020 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 a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/pr_checks.md | https://huggingface.co/docs/transformers/en/pr_checks/ | .md | 65_0 | |
When you open a pull request on 🤗 Transformers, a fair number of checks will be run to make sure the patch you are adding is not breaking anything existing. Those checks are of four types:
- regular tests
- documentation build
- code and documentation style
- general repository consistency
In this document, we will ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/pr_checks.md | https://huggingface.co/docs/transformers/en/pr_checks/#checks-on-a-pull-request | #checks-on-a-pull-request | .md | 65_1 |
All the jobs that begin with `ci/circleci: run_tests_` run parts of the Transformers testing suite. Each of those jobs focuses on a part of the library in a certain environment: for instance `ci/circleci: run_tests_pipelines_tf` runs the pipelines test in an environment where TensorFlow only is installed.
Note that t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/pr_checks.md | https://huggingface.co/docs/transformers/en/pr_checks/#tests | #tests | .md | 65_2 |
The `build_pr_documentation` job builds and generates a preview of the documentation to make sure everything looks okay once your PR is merged. A bot will add a link to preview the documentation in your PR. Any changes you make to the PR are automatically updated in the preview. If the documentation fails to build, cli... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/pr_checks.md | https://huggingface.co/docs/transformers/en/pr_checks/#documentation-build | #documentation-build | .md | 65_3 |
Code formatting is applied to all the source files, the examples and the tests using `black` and `ruff`. We also have a custom tool taking care of the formatting of docstrings and `rst` files (`utils/style_doc.py`), as well as the order of the lazy imports performed in the Transformers `__init__.py` files (`utils/custo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/pr_checks.md | https://huggingface.co/docs/transformers/en/pr_checks/#code-and-documentation-style | #code-and-documentation-style | .md | 65_4 |
This regroups all the tests to make sure your PR leaves the repository in a good state, and is performed by the `ci/circleci: check_repository_consistency` check. You can locally run that check by executing the following:
```bash
make repo-consistency
```
This checks that:
- All objects added to the init are docu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/pr_checks.md | https://huggingface.co/docs/transformers/en/pr_checks/#repository-consistency | #repository-consistency | .md | 65_5 |
Since the Transformers library is very opinionated with respect to model code, and each model should fully be implemented in a single file without relying on other models, we have added a mechanism that checks whether a copy of the code of a layer of a given model stays consistent with the original. This way, when ther... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/pr_checks.md | https://huggingface.co/docs/transformers/en/pr_checks/#check-copies | #check-copies | .md | 65_6 |
<!--Copyright 2024 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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_infer_gpu_multi.md | https://huggingface.co/docs/transformers/en/perf_infer_gpu_multi/ | .md | 66_0 | |
Built-in Tensor Parallelism (TP) is now available with certain models using PyTorch. Tensor parallelism shards a model onto multiple GPUs, enabling larger model sizes, and parallelizes computations such as matrix multiplication.
To enable tensor parallel, pass the argument `tp_plan="auto"` to [`~AutoModelForCausalLM.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_infer_gpu_multi.md | https://huggingface.co/docs/transformers/en/perf_infer_gpu_multi/#multi-gpu-inference | #multi-gpu-inference | .md | 66_1 |
You can benefit from considerable speedups for inference, especially for inputs with large batch size or long sequences.
For a single forward pass on [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel) with a sequence length of 512 and various batch sizes, the expected speedup is... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_infer_gpu_multi.md | https://huggingface.co/docs/transformers/en/perf_infer_gpu_multi/#expected-speedups | #expected-speedups | .md | 66_2 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tflite.md | https://huggingface.co/docs/transformers/en/tflite/ | .md | 67_0 | |
[TensorFlow Lite](https://www.tensorflow.org/lite/guide) is a lightweight framework for deploying machine learning models
on resource-constrained devices, such as mobile phones, embedded systems, and Internet of Things (IoT) devices.
TFLite is designed to optimize and run models efficiently on these devices with limite... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tflite.md | https://huggingface.co/docs/transformers/en/tflite/#export-to-tflite | #export-to-tflite | .md | 67_1 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/ | .md | 68_0 | |
This guide aims to provide a benchmark on the inference speed-ups introduced with [`torch.compile()`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html)for [computer vision models in 🤗 Transformers](https://huggingface.co/models?pipeline_tag=image-classification&library=transformers&sort=trending)... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#optimize-inference-using-torchcompile | #optimize-inference-using-torchcompile | .md | 68_1 |
Depending on the model and the GPU, `torch.compile()` yields up to 30% speed-up during inference. To use `torch.compile()`, simply install any version of `torch` above 2.0.
Compiling a model takes time, so it's useful if you are compiling the model only once instead of every time you infer.
To compile any computer vi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#benefits-of-torchcompile | #benefits-of-torchcompile | .md | 68_2 |
Below you can find the benchmarking code for each task. We warm up the GPU before inference and take the mean time of 300 inferences, using the same image each time. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#benchmarking-code | #benchmarking-code | .md | 68_3 |
```python
import torch
from PIL import Image
import requests
import numpy as np
from transformers import AutoImageProcessor, AutoModelForImageClassification
from accelerate.test_utils.testing import get_backend
device, _, _ = get_backend() # automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#image-classification-with-vit | #image-classification-with-vit | .md | 68_4 |
```python
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from accelerate.test_utils.testing import get_backend
device, _, _ = get_backend() # automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#object-detection-with-detr | #object-detection-with-detr | .md | 68_5 |
```python
from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
from accelerate.test_utils.testing import get_backend
device, _, _ = get_backend() # automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
processor = SegformerImageProcessor.from_pretrained("nvidia/segf... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#image-segmentation-with-segformer | #image-segmentation-with-segformer | .md | 68_6 |
| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|
| Image Classification/ViT | 9.325 | 7.584 |
| Image Segmentation/Segformer | 11.759 | 10.500 |
| Object Detection/OwlViT | 24.978 | 18.420 |
| Image Classification/BeiT | 11.282 | 8.448 |
| Object Detection/DETR | 34.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#a100-batch-size-1 | #a100-batch-size-1 | .md | 68_7 |
| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|
| Image Classification/ViT | 14.832 | 14.499 |
| Image Segmentation/Segformer | 18.838 | 16.476 |
| Image Classification/BeiT | 13.205 | 13.048 |
| Object Detection/DETR | 48.657 | 32.418|
| Image Classification/ConvNe... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#a100-batch-size-4 | #a100-batch-size-4 | .md | 68_8 |
| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|
| Image Classification/ViT | 40.944 | 40.010 |
| Image Segmentation/Segformer | 37.005 | 31.144 |
| Image Classification/BeiT | 41.854 | 41.048 |
| Object Detection/DETR | 164.382 | 161.902 |
| Image Classification/Con... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#a100-batch-size-16 | #a100-batch-size-16 | .md | 68_9 |
| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|
| Image Classification/ViT | 10.495 | 6.00 |
| Image Segmentation/Segformer | 13.321 | 5.862 |
| Object Detection/OwlViT | 25.769 | 22.395 |
| Image Classification/BeiT | 11.347 | 7.234 |
| Object Detection/DETR | 33.9... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#v100-batch-size-1 | #v100-batch-size-1 | .md | 68_10 |
| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|
| Image Classification/ViT | 15.181 | 14.501 |
| Image Segmentation/Segformer | 16.787 | 16.188 |
| Image Classification/BeiT | 15.171 | 14.753 |
| Object Detection/DETR | 88.529 | 64.195 |
| Image Classification/ConvN... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#v100-batch-size-4 | #v100-batch-size-4 | .md | 68_11 |
| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|
| Image Classification/ViT | 52.209 | 51.633 |
| Image Segmentation/Segformer | 61.013 | 55.499 |
| Image Classification/BeiT | 53.938 | 53.581 |
| Object Detection/DETR | OOM | OOM |
| Image Classification/ConvNeXT |... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#v100-batch-size-16 | #v100-batch-size-16 | .md | 68_12 |
| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|
| Image Classification/ViT | 16.520 | 15.786 |
| Image Segmentation/Segformer | 16.116 | 14.205 |
| Object Detection/OwlViT | 53.634 | 51.105 |
| Image Classification/BeiT | 16.464 | 15.710 |
| Object Detection/DETR | ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#t4-batch-size-1 | #t4-batch-size-1 | .md | 68_13 |
| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|
| Image Classification/ViT | 43.653 | 43.626 |
| Image Segmentation/Segformer | 45.327 | 42.445 |
| Image Classification/BeiT | 52.007 | 51.354 |
| Object Detection/DETR | 277.850 | 268.003 |
| Image Classification/Con... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#t4-batch-size-4 | #t4-batch-size-4 | .md | 68_14 |
| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|
| Image Classification/ViT | 163.914 | 160.907 |
| Image Segmentation/Segformer | 192.412 | 163.620 |
| Image Classification/BeiT | 188.978 | 187.976 |
| Object Detection/DETR | OOM | OOM |
| Image Classification/ConvN... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#t4-batch-size-16 | #t4-batch-size-16 | .md | 68_15 |
We also benchmarked on PyTorch nightly (2.1.0dev, find the wheel [here](https://download.pytorch.org/whl/nightly/cu118)) and observed improvement in latency both for uncompiled and compiled models. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#pytorch-nightly | #pytorch-nightly | .md | 68_16 |
| **Task/Model** | **Batch Size** | **torch 2.0 - no compile** | **torch 2.0 -<br> compile** |
|:---:|:---:|:---:|:---:|
| Image Classification/BeiT | Unbatched | 12.462 | 6.954 |
| Image Classification/BeiT | 4 | 14.109 | 12.851 |
| Image Classification/BeiT | 16 | 42.179 | 42.147 |
| Object Detection/DETR | Unbatched... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#a100 | #a100 | .md | 68_17 |
| **Task/Model** | **Batch Size** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|:---:|
| Image Classification/BeiT | Unbatched | 14.408 | 14.052 |
| Image Classification/BeiT | 4 | 47.381 | 46.604 |
| Image Classification/BeiT | 16 | 42.179 | 42.147 |
| Object Detection/DETR | Unb... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#t4 | #t4 | .md | 68_18 |
| **Task/Model** | **Batch Size** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|:---:|
| Image Classification/BeiT | Unbatched | 13.477 | 7.926 |
| Image Classification/BeiT | 4 | 15.103 | 14.378 |
| Image Classification/BeiT | 16 | 52.517 | 51.691 |
| Object Detection/DETR | Unba... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#v100 | #v100 | .md | 68_19 |
We benchmarked `reduce-overhead` compilation mode for A100 and T4 in Nightly. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#reduce-overhead | #reduce-overhead | .md | 68_20 |
| **Task/Model** | **Batch Size** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|:---:|
| Image Classification/ConvNeXT | Unbatched | 11.758 | 7.335 |
| Image Classification/ConvNeXT | 4 | 23.171 | 21.490 |
| Image Classification/ResNet | Unbatched | 7.435 | 3.801 |
| Image Classifi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#a100 | #a100 | .md | 68_21 |
| **Task/Model** | **Batch Size** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
|:---:|:---:|:---:|:---:|
| Image Classification/ConvNeXT | Unbatched | 32.137 | 31.84 |
| Image Classification/ConvNeXT | 4 | 120.944 | 110.209 |
| Image Classification/ResNet | Unbatched | 9.761 | 7.698 |
| Image Classi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_torch_compile.md | https://huggingface.co/docs/transformers/en/perf_torch_compile/#t4 | #t4 | .md | 68_22 |
<!--Copyright 2022 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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_special.md | https://huggingface.co/docs/transformers/en/perf_train_special/ | .md | 69_0 | |
Previously, training models on a Mac was limited to the CPU only. With the release of PyTorch v1.12, you can take advantage of training models with Apple's silicon GPUs for significantly faster performance and training. This is powered in PyTorch by integrating Apple's Metal Performance Shaders (MPS) as a backend. The ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_special.md | https://huggingface.co/docs/transformers/en/perf_train_special/#pytorch-training-on-apple-silicon | #pytorch-training-on-apple-silicon | .md | 69_1 |
<!--Copyright 2024 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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tiktoken.md | https://huggingface.co/docs/transformers/en/tiktoken/ | .md | 70_0 | |
Support for tiktoken model files is seamlessly integrated in 🤗 transformers when loading models
`from_pretrained` with a `tokenizer.model` tiktoken file on the Hub, which is automatically converted into our
[fast tokenizer](https://huggingface.co/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrained... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tiktoken.md | https://huggingface.co/docs/transformers/en/tiktoken/#tiktoken-and-interaction-with-transformers | #tiktoken-and-interaction-with-transformers | .md | 70_1 |
- gpt2
- llama3 | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tiktoken.md | https://huggingface.co/docs/transformers/en/tiktoken/#known-models-that-were-released-with-a-tiktokenmodel | #known-models-that-were-released-with-a-tiktokenmodel | .md | 70_2 |
In order to load `tiktoken` files in `transformers`, ensure that the `tokenizer.model` file is a tiktoken file and it
will automatically be loaded when loading `from_pretrained`. Here is how one would load a tokenizer and a model, which
can be loaded from the exact same file:
```py
from transformers import AutoTokeni... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tiktoken.md | https://huggingface.co/docs/transformers/en/tiktoken/#example-usage | #example-usage | .md | 70_3 |
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