source stringclasses 470
values | url stringlengths 49 167 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#usage-tips | .md | model = LiltModel.from_pretrained("path_to_your_files")
model.push_to_hub("name_of_repo_on_the_hub")
```
- When preparing data for the model, make sure to use the token vocabulary that corresponds to the RoBERTa checkpoint you combined with the Layout Transformer.
- As [lilt-roberta-en-base](https://huggingface.co/SC... | 195_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#usage-tips | .md | The same is true for [lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-infoxlm-base): one can use [`LayoutXLMTokenizerFast`] for that model. | 195_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#resources | .md | A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LiLT.
- Demo notebooks for LiLT can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT).
**Documentation resources**
- [Text classification task guide](../tasks/sequence_classifi... | 195_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#resources | .md | - [Question answering task guide](../tasks/question_answering)
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource. | 195_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltconfig | .md | This is the configuration class to store the configuration of a [`LiltModel`]. It is used to instantiate a LiLT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the LiLT
[SCUT-DLVCLab/lilt-roberta-e... | 195_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltconfig | .md | Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the LiLT model. Defines the number of different tokens that can be re... | 195_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltconfig | .md | `inputs_ids` passed when calling [`LiltModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer. Should be a multiple of 24.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, ... | 195_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltconfig | .md | intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"g... | 195_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltconfig | .md | `"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabili... | 195_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltconfig | .md | The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`LiltModel`].
initializer_range (`float`, *optional*, ... | 195_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltconfig | .md | layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`... | 195_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltconfig | .md | [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
classifier_dropou... | 195_4_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltconfig | .md | The dropout ratio for the classification head.
channel_shrink_ratio (`int`, *optional*, defaults to 4):
The shrink ratio compared to the `hidden_size` for the channel dimension of the layout embeddings.
max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the 2D position embedding mi... | 195_4_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltconfig | .md | >>> # Initializing a LiLT SCUT-DLVCLab/lilt-roberta-en-base style configuration
>>> configuration = LiltConfig()
>>> # Randomly initializing a model from the SCUT-DLVCLab/lilt-roberta-en-base style configuration
>>> model = LiltModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.confi... | 195_4_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltmodel | .md | The bare LiLT Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
et... | 195_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltmodel | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`LiltConfig`]): Model configuration class with ... | 195_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltmodel | .md | model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 195_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltforsequenceclassification | .md | LiLT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or savin... | 195_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltforsequenceclassification | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`LiltConfig`]): Model configuration class with ... | 195_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltforsequenceclassification | .md | model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 195_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltfortokenclassification | .md | Lilt Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading o... | 195_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltfortokenclassification | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`LiltConfig`]): Model configuration class with ... | 195_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltfortokenclassification | .md | model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 195_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltforquestionanswering | .md | Lilt Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
l... | 195_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltforquestionanswering | .md | library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all ma... | 195_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltforquestionanswering | .md | and behavior.
Parameters:
config ([`LiltConfig`]): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods... | 195_8_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/ | .md | <!--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 agr... | 196_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 196_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#overview | .md | The M2M100 model was proposed in [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky,
Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy
Liptch... | 196_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#overview | .md | Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
The abstract from the paper is the following:
*Existing work in translation demonstrated the potential of massively multilingual machine translation by training a
single model able to translate between any pair of languages. However, much of th... | 196_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#overview | .md | only on data which was translated from or to English. While this is supported by large sources of training data, it
does not reflect translation needs worldwide. In this work, we create a true Many-to-Many multilingual translation
model that can translate directly between any pair of 100 languages. We build and open so... | 196_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#overview | .md | covers thousands of language directions with supervised data, created through large-scale mining. Then, we explore how
to effectively increase model capacity through a combination of dense scaling and language-specific sparse parameters
to create high quality models. Our focus on non-English-Centric models brings gains... | 196_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#overview | .md | translating between non-English directions while performing competitively to the best single systems of WMT. We
open-source our scripts so that others may reproduce the data, evaluation, and final M2M-100 model.*
This model was contributed by [valhalla](https://huggingface.co/valhalla). | 196_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage-tips-and-examples | .md | M2M100 is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation tasks. As the model is
multilingual it expects the sequences in a certain format: A special language id token is used as prefix in both the
source and target text. The source text format is `[lang_code] X [eos]`, where `lang_... | 196_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage-tips-and-examples | .md | id for source text and target language id for target text, with `X` being the source or target text.
The [`M2M100Tokenizer`] depends on `sentencepiece` so be sure to install it before running the
examples. To install `sentencepiece` run `pip install sentencepiece`.
**Supervised Training**
```python
from transform... | 196_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage-tips-and-examples | .md | model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="fr")
src_text = "Life is like a box of chocolates."
tgt_text = "La vie est comme une boîte de chocolat."
model_inputs = tokenizer(src_text, text_t... | 196_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage-tips-and-examples | .md | loss = model(**model_inputs).loss # forward pass
```
**Generation**
M2M100 uses the `eos_token_id` as the `decoder_start_token_id` for generation with the target language id
being forced as the first generated token. To force the target language id as the first generated token, pass the
*forced_bos_token_id* param... | 196_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage-tips-and-examples | .md | Hindi to French and Chinese to English using the *facebook/m2m100_418M* checkpoint.
```python
>>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer | 196_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage-tips-and-examples | .md | >>> hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।"
>>> chinese_text = "生活就像一盒巧克力。"
>>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
>>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M") | 196_2_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage-tips-and-examples | .md | >>> # translate Hindi to French
>>> tokenizer.src_lang = "hi"
>>> encoded_hi = tokenizer(hi_text, return_tensors="pt")
>>> generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id("fr"))
>>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
"La vie est comme une boîte d... | 196_2_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage-tips-and-examples | .md | >>> # translate Chinese to English
>>> tokenizer.src_lang = "zh"
>>> encoded_zh = tokenizer(chinese_text, return_tensors="pt")
>>> generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en"))
>>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
"Life is like a box o... | 196_2_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#resources | .md | - [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization) | 196_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100config | .md | This is the configuration class to store the configuration of a [`M2M100Model`]. It is used to instantiate an
M2M100 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the M2M100
[facebook/m2m100_418M... | 196_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100config | .md | Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the M2M100 model. Defines the number of different tokens that can be ... | 196_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100config | .md | d_model (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Numbe... | 196_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100config | .md | decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, default... | 196_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100config | .md | activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully co... | 196_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100config | .md | attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for cla... | 196_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100config | .md | The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`floa... | 196_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100config | .md | The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
use_cache (`... | 196_4_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100config | .md | Whether or not the model should return the last key/values attentions (not used by all models).
Example:
```python
>>> from transformers import M2M100Config, M2M100Model | 196_4_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100config | .md | >>> # Initializing a M2M100 facebook/m2m100_418M style configuration
>>> configuration = M2M100Config()
>>> # Initializing a model (with random weights) from the facebook/m2m100_418M style configuration
>>> model = M2M100Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | 196_4_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100tokenizer | .md | Construct an M2M100 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vo... | 196_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100tokenizer | .md | contains the vocabulary.
src_lang (`str`, *optional*):
A string representing the source language.
tgt_lang (`str`, *optional*):
A string representing the target language.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator to... | 196_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100tokenizer | .md | The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The un... | 196_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100tokenizer | .md | token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
language_codes (`str`, *optional*, defaults to `"m2m100"`):
What language codes to use. Should be one of `"m2m100"` or `"wmt21"`.
sp_model_kwargs (`dict`, *optional*... | 196_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100tokenizer | .md | sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampli... | 196_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100tokenizer | .md | - `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling... | 196_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100tokenizer | .md | >>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
>>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="ro")
>>> src_text = " UN Chief Says There Is No Military Solution in Syria"
>>> tgt_text = "Şeful ONU declară că nu există o soluţie militar... | 196_5_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100tokenizer | .md | >>> outputs = model(**model_inputs) # should work
```
Methods: build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary | 196_5_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100model | .md | The bare M2M100 Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This... | 196_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100model | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`M2M100Config`]):
Model configuration class wit... | 196_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100model | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 196_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100forconditionalgeneration | .md | The M2M100 Model with a language modeling head. Can be used for summarization.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model... | 196_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100forconditionalgeneration | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`M2M100Config`]):
Model configuration class wit... | 196_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100forconditionalgeneration | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 196_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#using-flash-attention-2 | .md | Flash Attention 2 is a faster, optimized version of the attention scores computation which relies on `cuda` kernels. | 196_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#installation | .md | First, check whether your hardware is compatible with Flash Attention 2. The latest list of compatible hardware can be found in the [official documentation](https://github.com/Dao-AILab/flash-attention#installation-and-features).
Next, [install](https://github.com/Dao-AILab/flash-attention#installation-and-features) ... | 196_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage | .md | To load a model using Flash Attention 2, we can pass the argument `attn_implementation="flash_attention_2"` to [`.from_pretrained`](https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained). You can use either `torch.float16` or `torch.bfloat16` precision.
```py... | 196_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage | .md | >>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M", torch_dtype=torch.float16, attn_implementation="flash_attention_2").to("cuda").eval()
>>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M") | 196_10_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage | .md | >>> # translate Hindi to French
>>> hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।"
>>> tokenizer.src_lang = "hi"
>>> encoded_hi = tokenizer(hi_text, return_tensors="pt").to("cuda")
>>> generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id("fr"))
>>> tokenizer.batch_decode(generated_toke... | 196_10_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#expected-speedups | .md | Below is an expected speedup diagram that compares pure inference time between the native implementation and the Flash Attention 2.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/visheratin/documentation-images/resolve/main/nllb-speedup.webp">
</div> | 196_11_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#using-scaled-dot-product-attention-sdpa | .md | PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.s... | 196_12_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#using-scaled-dot-product-attention-sdpa | .md | page for more information.
SDPA is used by default for `torch>=2.1.1` when an implementation is available, but you may also set
`attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.
```python
from transformers import M2M100ForConditionalGeneration
model = M2M100ForConditionalGen... | 196_12_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#using-scaled-dot-product-attention-sdpa | .md | ...
```
For the best speedups, we recommend loading the model in half-precision (e.g. `torch.float16` or `torch.bfloat16`). | 196_12_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/ | .md | <!--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... | 197_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 197_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#overview | .md | Bamba-9B is a decoder-only language model based on the [Mamba-2](https://github.com/state-spaces/mamba) architecture and is designed to handle a wide range of text generation tasks. It is trained from scratch using a two-stage training approach. In the first stage, the model is trained on 2 trillion tokens from the Dol... | 197_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#overview | .md | training on 200 billion tokens, leveraging a carefully curated blend of high-quality data to further refine its performance and enhance output quality. | 197_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#overview | .md | Checkout all Bamba-9B model checkpoints [here](https://github.com/foundation-model-stack/bamba). | 197_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | | Model | Params | # Layers | Hidden Dim. | Attention Heads | GQA | KV Heads | Context Length | Tied Embeddings |
|-------------------|--------------|----------|-------------|-----------------|-----|----------|----------------|------------------|
| Bamba | 9B (9.78B) | 32 | 4096 | 32 ... | 197_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | This is the configuration class to store the configuration of a [`BambaModel`]. It is used to instantiate a
BambaModel model according to the specified arguments, defining the model architecture. Instantiating a configuration
with defaults taken from [ibm-fms/Bamba-9.8b-2.2T-hf](https://huggingface.co/ibm-fms/Bamba-9.8... | 197_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | The checkpoints are jointly trained by IBM, Princeton, and UIUC.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 128000):
Vocabulary size of ... | 197_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | `inputs_ids` passed when calling [`BambaModel`]
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
model has a output word embedding layer.
hidden_size (`int`, *optional*, defaults to 4096):
Dimension... | 197_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*,... | 197_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head check... | 197_2_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
... | 197_2_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attenti... | 197_2_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | relevant if `config.is_decoder=True`.
num_logits_to_keep (`int` or `None`, *optional*, defaults to 1):
Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an
integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the
logits of ... | 197_2_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint
significantly.
pad_token_id (`int`, *optional*, defaults to 0):
The id of the padding token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, def... | 197_2_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | max_position_embeddings (`int`, *optional*, defaults to 262144):
Max cached sequence length for the model
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
attn_layer_indices (`list`, *optional*):
Specifies the layer indices that will have full attention. Must ... | 197_2_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | mamba_n_heads (`int`, *optional*, defaults to 128):
The number of mamba heads used in the v2 implementation.
mamba_d_head (`int`, *optional*, defaults to `"auto"`):
Head embeddding dimension size
mamba_n_groups (`int`, *optional*, defaults to 1):
The number of the mamba groups used in the v2 implementation.
mamba_d_sta... | 197_2_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | mamba_d_conv (`int`, *optional*, defaults to 4):
The size of the mamba convolution kernel
mamba_expand (`int`, *optional*, defaults to 2):
Expanding factor (relative to hidden_size) used to determine the mamba intermediate size
mamba_chunk_size (`int`, *optional*, defaults to 256):
The chunks in which to break the sequ... | 197_2_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | .md | Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block.
mamba_proj_bias (`bool`, *optional*, defaults to `False`):
Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block
<!--- | 197_2_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#usage-tips | .md | Tips:
- The architecture is based on Mamba-2 models. | 197_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambamodel | .md | The bare Bamba Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This ... | 197_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambamodel | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`BambaConfig`]):
Model configuration class with... | 197_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambamodel | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`BambaDecoderLayer`]
Args:
config: BambaConfig
Methods: forward
--> | 197_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaforcausallm | .md | ```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ibm-fms/Bamba-9B")
tokenizer = AutoTokenizer.from_pretrained("ibm-fms/Bamba-9B") | 197_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaforcausallm | .md | message = ["Mamba is a snake with following properties "]
inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
response = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
```
No docstring available for BambaForCausalLM
Methods... | 197_5_1 |
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