transformers/pr_33962/en/model_doc/gemma.md

# Gemma

[Gemma](https://huggingface.co/papers/2403.08295) is a family of lightweight language models with pretrained and instruction-tuned variants, available in 2B and 7B parameters. The architecture is based on a transformer decoder-only design. It features Multi-Query Attention, rotary positional embeddings (RoPE), GeGLU activation functions, and RMSNorm layer normalization.

The instruction-tuned variant was fine-tuned with supervised learning on instruction-following data, followed by reinforcement learning from human feedback (RLHF) to align the model outputs with human preferences.

You can find all the original Gemma checkpoints under the [Gemma](https://huggingface.co/collections/google/gemma-release-65d5efbccdbb8c4202ec078b) release.

> [!TIP]
> Click on the Gemma models in the right sidebar for more examples of how to apply Gemma to different language tasks.

The example below demonstrates how to generate text with [Pipeline](/docs/transformers/pr_33962/en/main_classes/pipelines#transformers.Pipeline) or the [AutoModel](/docs/transformers/pr_33962/en/model_doc/auto#transformers.AutoModel) class, and from the command line.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(
    task="text-generation",
    model="google/gemma-2b",
    dtype=torch.bfloat16,
    device_map="auto",
)

pipeline("LLMs generate text through a process known as", max_new_tokens=50)
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-2b",
    dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="sdpa"
)

input_text = "LLMs generate text through a process known as"
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)

outputs = model.generate(**input_ids, max_new_tokens=50, cache_implementation="static")
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

</hfoption>
<hfoption id="transformers CLI">

```bash
echo -e "LLMs generate text through a process known as" | transformers run --task text-generation --model google/gemma-2b --device 0
```

</hfoption>
</hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.

The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.

```py
#!pip install bitsandbytes
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4"
)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-7b",
    quantization_config=quantization_config,
    device_map="auto",
    attn_implementation="sdpa"
)

input_text = "LLMs generate text through a process known as."
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(
    **input_ids,
    max_new_tokens=50,
    cache_implementation="static"
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/blob/beb9b5b02246b9b7ee81ddf938f93f44cfeaad19/src/transformers/utils/attention_visualizer.py#L139) to better understand what tokens the model can and cannot attend to.

```py
from transformers.utils.attention_visualizer import AttentionMaskVisualizer

visualizer = AttentionMaskVisualizer("google/gemma-2b")
visualizer("LLMs generate text through a process known as")
```

<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/gemma-attn-mask.png"/>
</div>

## Notes

- The original Gemma models support standard kv-caching used in many transformer-based language models. You can use use the default [DynamicCache](/docs/transformers/pr_33962/en/internal/generation_utils#transformers.DynamicCache) instance or a tuple of tensors for past key values during generation. This makes it compatible with typical autoregressive generation workflows.

   ```py
   import torch
   from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache

   tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
   model = AutoModelForCausalLM.from_pretrained(
       "google/gemma-2b",
       dtype=torch.bfloat16,
       device_map="auto",
       attn_implementation="sdpa"
   )
   input_text = "LLMs generate text through a process known as"
   input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
   past_key_values = DynamicCache(config=model.config)
   outputs = model.generate(**input_ids, max_new_tokens=50, past_key_values=past_key_values)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   ```

## GemmaConfig[[transformers.GemmaConfig]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class transformers.GemmaConfig</name><anchor>transformers.GemmaConfig</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/configuration_gemma.py#L25</source><parameters>[{"name": "vocab_size", "val": " = 256000"}, {"name": "hidden_size", "val": " = 3072"}, {"name": "intermediate_size", "val": " = 24576"}, {"name": "num_hidden_layers", "val": " = 28"}, {"name": "num_attention_heads", "val": " = 16"}, {"name": "num_key_value_heads", "val": " = 16"}, {"name": "head_dim", "val": " = 256"}, {"name": "hidden_act", "val": " = 'gelu_pytorch_tanh'"}, {"name": "max_position_embeddings", "val": " = 8192"}, {"name": "initializer_range", "val": " = 0.02"}, {"name": "rms_norm_eps", "val": " = 1e-06"}, {"name": "use_cache", "val": " = True"}, {"name": "pad_token_id", "val": " = 0"}, {"name": "eos_token_id", "val": " = 1"}, {"name": "bos_token_id", "val": " = 2"}, {"name": "tie_word_embeddings", "val": " = True"}, {"name": "rope_theta", "val": " = 10000.0"}, {"name": "attention_bias", "val": " = False"}, {"name": "attention_dropout", "val": " = 0.0"}, {"name": "layer_types", "val": " = None"}, {"name": "use_bidirectional_attention", "val": " = None"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_size** (`int`, *optional*, defaults to 256000) --
  Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the
  `inputs_ids` passed when calling [GemmaModel](/docs/transformers/pr_33962/en/model_doc/gemma#transformers.GemmaModel)
- **hidden_size** (`int`, *optional*, defaults to 3072) --
  Dimension of the hidden representations.
- **intermediate_size** (`int`, *optional*, defaults to 24576) --
  Dimension of the MLP representations.
- **num_hidden_layers** (`int`, *optional*, defaults to 28) --
  Number of hidden layers in the Transformer decoder.
- **num_attention_heads** (`int`, *optional*, defaults to 16) --
  Number of attention heads for each attention layer in the Transformer decoder.
- **num_key_value_heads** (`int`, *optional*, defaults to 16) --
  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 checkpoint to a GQA checkpoint, each group key and value head should be constructed
  by meanpooling all the original heads within that group. For more details, check out [this
  paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
  `num_attention_heads`.
- **head_dim** (`int`, *optional*, defaults to 256) --
  The attention head dimension.
- **hidden_act** (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`) --
  The legacy activation function. It is overwritten by the `hidden_activation`.
- **max_position_embeddings** (`int`, *optional*, defaults to 8192) --
  The maximum sequence length that this model might ever be used with.
- **initializer_range** (`float`, *optional*, defaults to 0.02) --
  The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- **rms_norm_eps** (`float`, *optional*, defaults to 1e-06) --
  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 attentions (not used by all models). Only
  relevant if `config.is_decoder=True`.
- **pad_token_id** (`int`, *optional*, defaults to 0) --
  Padding token id.
- **eos_token_id** (`int`, *optional*, defaults to 1) --
  End of stream token id.
- **bos_token_id** (`int`, *optional*, defaults to 2) --
  Beginning of stream token id.
- **tie_word_embeddings** (`bool`, *optional*, defaults to `True`) --
  Whether to tie weight embeddings
- **rope_theta** (`float`, *optional*, defaults to 10000.0) --
  The base period of the RoPE embeddings.
- **attention_bias** (`bool`, defaults to `False`, *optional*, defaults to `False`) --
  Whether to use a bias in the query, key, value and output projection layers during self-attention.
- **attention_dropout** (`float`, *optional*, defaults to 0.0) --
  The dropout ratio for the attention probabilities.
- **layer_types** (`list`, *optional*) --
  Attention pattern for each layer.
- **use_bidirectional_attention** (`bool`, *optional*) --
  If True, the model will attend to all text tokens instead of using a causal mask.</paramsdesc><paramgroups>0</paramgroups></docstring>

This is the configuration class to store the configuration of a [GemmaModel](/docs/transformers/pr_33962/en/model_doc/gemma#transformers.GemmaModel). It is used to instantiate an Gemma
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 Gemma-7B.
e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)
Configuration objects inherit from [PreTrainedConfig](/docs/transformers/pr_33962/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/pr_33962/en/main_classes/configuration#transformers.PreTrainedConfig) for more information.



<ExampleCodeBlock anchor="transformers.GemmaConfig.example">

```python
>>> from transformers import GemmaModel, GemmaConfig
>>> # Initializing a Gemma gemma-7b style configuration
>>> configuration = GemmaConfig()
>>> # Initializing a model from the gemma-7b style configuration
>>> model = GemmaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```

</ExampleCodeBlock>

</div>

## GemmaTokenizer[[transformers.GemmaTokenizer]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class transformers.GemmaTokenizer</name><anchor>transformers.GemmaTokenizer</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/tokenization_gemma.py#L44</source><parameters>[{"name": "vocab_file", "val": ""}, {"name": "unk_token", "val": " = '<unk>'"}, {"name": "bos_token", "val": " = '<bos>'"}, {"name": "eos_token", "val": " = '<eos>'"}, {"name": "pad_token", "val": " = '<pad>'"}, {"name": "sp_model_kwargs", "val": ": typing.Optional[dict[str, typing.Any]] = None"}, {"name": "add_bos_token", "val": " = True"}, {"name": "add_eos_token", "val": " = False"}, {"name": "clean_up_tokenization_spaces", "val": " = False"}, {"name": "use_default_system_prompt", "val": " = False"}, {"name": "spaces_between_special_tokens", "val": " = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_file** (`str`) --
  Path to the vocabulary file.
- **unk_token** (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`) --
  The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
  token instead.
- **bos_token** (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<bos>"`) --
  The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
- **eos_token** (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<eos>"`) --
  The end of sequence token.
- **pad_token** (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<pad>"`) --
  A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
  attention mechanisms or loss computation.
- **sp_model_kwargs** (`dict[str, Any]`, `Optional`, *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`: Sampling parameters for unigram. Invalid for BPE-Dropout.

    - `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, and dropout probability of merge operations for
    BPE-dropout.

- **add_bos_token** (`bool`, *optional*, defaults to `True`) --
  Whether or not to add an `bos_token` at the start of sequences.
- **add_eos_token** (`bool`, *optional*, defaults to `False`) --
  Whether or not to add an `eos_token` at the end of sequences.
- **clean_up_tokenization_spaces** (`bool`, *optional*, defaults to `False`) --
  Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
  extra spaces.
- **use_default_system_prompt** (`bool`, *optional*, defaults to `False`) --
  Whether or not the default system prompt for Gemma should be used.
- **spaces_between_special_tokens** (`bool`, *optional*, defaults to `False`) --
  Whether or not to add spaces between special tokens.</paramsdesc><paramgroups>0</paramgroups></docstring>

Construct a Gemma tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
no padding token in the original model.





<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>convert_tokens_to_string</name><anchor>transformers.GemmaTokenizer.convert_tokens_to_string</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/tokenization_gemma.py#L183</source><parameters>[{"name": "tokens", "val": ""}]</parameters></docstring>
Converts a sequence of tokens (string) in a single string.

</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>create_token_type_ids_from_sequences</name><anchor>transformers.GemmaTokenizer.create_token_type_ids_from_sequences</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/tokenization_gemma.py#L272</source><parameters>[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}]</parameters><paramsdesc>- **token_ids_0** (`list[int]`) --
  List of ids.
- **token_ids_1** (`list[int]`, *optional*) --
  Optional second list of IDs for sequence pairs.</paramsdesc><paramgroups>0</paramgroups><rettype>`list[int]`</rettype><retdesc>List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).</retdesc></docstring>

Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
<ExampleCodeBlock anchor="transformers.GemmaTokenizer.create_token_type_ids_from_sequences.example">

sequence pair mask has the following format:

```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |
```

</ExampleCodeBlock>

if token_ids_1 is None, only returns the first portion of the mask (0s).








</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>get_special_tokens_mask</name><anchor>transformers.GemmaTokenizer.get_special_tokens_mask</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/tokenization_gemma.py#L235</source><parameters>[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}, {"name": "already_has_special_tokens", "val": ": bool = False"}]</parameters><paramsdesc>- **token_ids_0** (`list[int]`) --
  List of IDs.
- **token_ids_1** (`list[int]`, *optional*) --
  Optional second list of IDs for sequence pairs.
- **already_has_special_tokens** (`bool`, *optional*, defaults to `False`) --
  Whether or not the token list is already formatted with special tokens for the model.</paramsdesc><paramgroups>0</paramgroups><rettype>`list[int]`</rettype><retdesc>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</retdesc></docstring>

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.








</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>get_vocab</name><anchor>transformers.GemmaTokenizer.get_vocab</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/tokenization_gemma.py#L152</source><parameters>[]</parameters></docstring>
Returns vocab as a dict

</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>save_vocabulary</name><anchor>transformers.GemmaTokenizer.save_vocabulary</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/tokenization_gemma.py#L197</source><parameters>[{"name": "save_directory", "val": ""}, {"name": "filename_prefix", "val": ": typing.Optional[str] = None"}]</parameters><paramsdesc>- **save_directory** (`str`) --
  The directory in which to save the vocabulary.</paramsdesc><paramgroups>0</paramgroups><rettype>`Tuple(str)`</rettype><retdesc>Paths to the files saved.</retdesc></docstring>

Save the vocabulary and special tokens file to a directory.








</div>
<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>tokenize</name><anchor>transformers.GemmaTokenizer.tokenize</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/tokenization_gemma.py#L158</source><parameters>[{"name": "text", "val": ": TextInput"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **text** -- TextInput</paramsdesc><paramgroups>0</paramgroups></docstring>



Simply calls PreTrainedTokenizer's method


</div></div>

## GemmaTokenizerFast[[transformers.GemmaTokenizerFast]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class transformers.GemmaTokenizerFast</name><anchor>transformers.GemmaTokenizerFast</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/tokenization_gemma_fast.py#L34</source><parameters>[{"name": "vocab_file", "val": " = None"}, {"name": "tokenizer_file", "val": " = None"}, {"name": "clean_up_tokenization_spaces", "val": " = False"}, {"name": "unk_token", "val": " = '<unk>'"}, {"name": "bos_token", "val": " = '<bos>'"}, {"name": "eos_token", "val": " = '<eos>'"}, {"name": "pad_token", "val": " = '<pad>'"}, {"name": "add_bos_token", "val": " = True"}, {"name": "add_eos_token", "val": " = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_file** (`str`, *optional*) --
  [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
  contains the vocabulary necessary to instantiate a tokenizer.
- **tokenizer_file** (`str`, *optional*) --
  [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
  contains everything needed to load the tokenizer.
- **clean_up_tokenization_spaces** (`bool`, *optional*, defaults to `False`) --
  Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
  extra spaces.
- **unk_token** (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`) --
  The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
  token instead.
- **bos_token** (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<bos>"`) --
  The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
- **eos_token** (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<eos>"`) --
  The end of sequence token.
- **pad_token** (`str`, *optional*, defaults to `"<pad>"`) --
  The padding token
- **add_bos_token** (`bool`, *optional*, defaults to `True`) --
  Whether or not to add an `bos_token` at the start of sequences.
- **add_eos_token** (`bool`, *optional*, defaults to `False`) --
  Whether or not to add an `eos_token` at the end of sequences.</paramsdesc><paramgroups>0</paramgroups></docstring>

Construct a Gemma tokenizer fast. Based on byte-level Byte-Pair-Encoding.

This uses notably ByteFallback and no prefix space. Normalization is applied to replace  `" "` with `"▁"`

<ExampleCodeBlock anchor="transformers.GemmaTokenizerFast.example">

```python
>>> from transformers import GemmaTokenizerFast

>>> tokenizer = GemmaTokenizerFast.from_pretrained("hf-internal-testing/dummy-gemma")
>>> tokenizer.encode("Hello this is a test")
[2, 4521, 736, 603, 476, 2121]
```

</ExampleCodeBlock>

If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
[post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.


This tokenizer inherits from [PreTrainedTokenizerFast](/docs/transformers/pr_33962/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast) which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.





<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>update_post_processor</name><anchor>transformers.GemmaTokenizerFast.update_post_processor</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/tokenization_gemma_fast.py#L118</source><parameters>[]</parameters></docstring>

Updates the underlying post processor with the current `bos_token` and `eos_token`.


</div></div>

## GemmaModel[[transformers.GemmaModel]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class transformers.GemmaModel</name><anchor>transformers.GemmaModel</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/modeling_gemma.py#L333</source><parameters>[{"name": "config", "val": ": GemmaConfig"}]</parameters><paramsdesc>- **config** ([GemmaConfig](/docs/transformers/pr_33962/en/model_doc/gemma#transformers.GemmaConfig)) --
  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
  [from_pretrained()](/docs/transformers/pr_33962/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

The bare Gemma Model outputting raw hidden-states without any specific head on top.

This model inherits from [PreTrainedModel](/docs/transformers/pr_33962/en/main_classes/model#transformers.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 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.





<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>forward</name><anchor>transformers.GemmaModel.forward</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/modeling_gemma.py#L350</source><parameters>[{"name": "input_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "past_key_values", "val": ": typing.Optional[transformers.cache_utils.Cache] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "use_cache", "val": ": typing.Optional[bool] = None"}, {"name": "cache_position", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/pr_33962/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_33962/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_33962/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **past_key_values** (`~cache_utils.Cache`, *optional*) --
  Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
  returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Only [Cache](/docs/transformers/pr_33962/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, [DynamicCache](/docs/transformers/pr_33962/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default.

  The model will output the same cache format that is fed as input.

  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
  have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids`
  of shape `(batch_size, sequence_length)`.
- **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- **cache_position** (`torch.LongTensor` of shape `(sequence_length)`, *optional*) --
  Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
  this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
  the complete sequence length.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.BaseModelOutputWithPast](/docs/transformers/pr_33962/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.BaseModelOutputWithPast](/docs/transformers/pr_33962/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([GemmaConfig](/docs/transformers/pr_33962/en/model_doc/gemma#transformers.GemmaConfig)) and inputs.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/pr_33962/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
  `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
  input) to speed up sequential decoding.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [GemmaModel](/docs/transformers/pr_33962/en/model_doc/gemma#transformers.GemmaModel) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>








</div></div>

## GemmaForCausalLM[[transformers.GemmaForCausalLM]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class transformers.GemmaForCausalLM</name><anchor>transformers.GemmaForCausalLM</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/modeling_gemma.py#L425</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([GemmaForCausalLM](/docs/transformers/pr_33962/en/model_doc/gemma#transformers.GemmaForCausalLM)) --
  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
  [from_pretrained()](/docs/transformers/pr_33962/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

The Gemma Model for causal language modeling.

This model inherits from [PreTrainedModel](/docs/transformers/pr_33962/en/main_classes/model#transformers.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 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.





<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>forward</name><anchor>transformers.GemmaForCausalLM.forward</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/modeling_gemma.py#L439</source><parameters>[{"name": "input_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "past_key_values", "val": ": typing.Optional[transformers.cache_utils.Cache] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "labels", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "use_cache", "val": ": typing.Optional[bool] = None"}, {"name": "cache_position", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "logits_to_keep", "val": ": typing.Union[int, torch.Tensor] = 0"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/pr_33962/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_33962/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_33962/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **past_key_values** (`~cache_utils.Cache`, *optional*) --
  Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
  returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Only [Cache](/docs/transformers/pr_33962/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, [DynamicCache](/docs/transformers/pr_33962/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default.

  The model will output the same cache format that is fed as input.

  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
  have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids`
  of shape `(batch_size, sequence_length)`.
- **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
  config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
  (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- **cache_position** (`torch.LongTensor` of shape `(sequence_length)`, *optional*) --
  Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
  this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
  the complete sequence length.
- **logits_to_keep** (`Union[int, torch.Tensor]`, defaults to `0`) --
  If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
  `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
  token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
  If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
  This is useful when using packed tensor format (single dimension for batch and sequence length).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.CausalLMOutputWithPast](/docs/transformers/pr_33962/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.CausalLMOutputWithPast](/docs/transformers/pr_33962/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([GemmaConfig](/docs/transformers/pr_33962/en/model_doc/gemma#transformers.GemmaConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss (for next-token prediction).
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/pr_33962/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
  `past_key_values` input) to speed up sequential decoding.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [GemmaForCausalLM](/docs/transformers/pr_33962/en/model_doc/gemma#transformers.GemmaForCausalLM) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







<ExampleCodeBlock anchor="transformers.GemmaForCausalLM.forward.example">

Example:

```python
>>> from transformers import AutoTokenizer, GemmaForCausalLM

>>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b")
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")

>>> prompt = "What is your favorite condiment?"
>>> inputs = tokenizer(prompt, return_tensors="pt")

>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"What is your favorite condiment?"
```

</ExampleCodeBlock>

</div></div>

## GemmaForSequenceClassification[[transformers.GemmaForSequenceClassification]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class transformers.GemmaForSequenceClassification</name><anchor>transformers.GemmaForSequenceClassification</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/modeling_gemma.py#L500</source><parameters>[{"name": "config", "val": ""}]</parameters></docstring>



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>forward</name><anchor>transformers.GemmaForSequenceClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/modeling_layers.py#L111</source><parameters>[{"name": "input_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "past_key_values", "val": ": typing.Optional[transformers.cache_utils.Cache] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "labels", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "use_cache", "val": ": typing.Optional[bool] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/pr_33962/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_33962/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_33962/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **past_key_values** (`~cache_utils.Cache`, *optional*) --
  Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
  returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Only [Cache](/docs/transformers/pr_33962/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, [DynamicCache](/docs/transformers/pr_33962/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default.

  The model will output the same cache format that is fed as input.

  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
  have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids`
  of shape `(batch_size, sequence_length)`.
- **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
  config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
  (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).</paramsdesc><paramgroups>0</paramgroups><rettype>`transformers.modeling_outputs.SequenceClassifierOutputWithPast` or `tuple(torch.FloatTensor)`</rettype><retdesc>A `transformers.modeling_outputs.SequenceClassifierOutputWithPast` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`None`) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Classification (or regression if config.num_labels==1) loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/pr_33962/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
  `past_key_values` input) to speed up sequential decoding.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The `GenericForSequenceClassification` forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>








</div></div>

## GemmaForTokenClassification[[transformers.GemmaForTokenClassification]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class transformers.GemmaForTokenClassification</name><anchor>transformers.GemmaForTokenClassification</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/gemma/modeling_gemma.py#L504</source><parameters>[{"name": "config", "val": ""}]</parameters></docstring>



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>forward</name><anchor>transformers.GemmaForTokenClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/modeling_layers.py#L254</source><parameters>[{"name": "input_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "past_key_values", "val": ": typing.Optional[transformers.cache_utils.Cache] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "labels", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "use_cache", "val": ": typing.Optional[bool] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/pr_33962/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_33962/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_33962/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **past_key_values** (`~cache_utils.Cache`, *optional*) --
  Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
  returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Only [Cache](/docs/transformers/pr_33962/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, [DynamicCache](/docs/transformers/pr_33962/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default.

  The model will output the same cache format that is fed as input.

  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
  have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids`
  of shape `(batch_size, sequence_length)`.
- **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
  config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
  (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.TokenClassifierOutput](/docs/transformers/pr_33962/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.TokenClassifierOutput](/docs/transformers/pr_33962/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`None`) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided)  -- Classification loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`) -- Classification scores (before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The `GenericForTokenClassification` forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>








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