> For the complete documentation index, see [llms.txt](https://boinc-ai.gitbook.io/transformers/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boinc-ai.gitbook.io/transformers/api/main-classes/auto-classes/natural-language-processing/flaxautomodelforcausallm.md). # FlaxAutoModelForCausalLM #### FlaxAutoModelForCausalLM #### class transformers.FlaxAutoModelForCausalLM [\](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/auto/modeling_flax_auto.py#L288) ( \*args\*\*kwargs ) This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created with the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.FlaxAutoModelForVision2Seq.from_pretrained) class method or the [from\_config()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.FlaxAutoModelForVision2Seq.from_config) class method. This class cannot be instantiated directly using `__init__()` (throws an error). **from\_config** [\](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/auto/auto_factory.py#L417) ( \*\*kwargs ) Parameters * **config** ([PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig)) — The model class to instantiate is selected based on the configuration class: * [BartConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bart#transformers.BartConfig) configuration class: [FlaxBartForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bart#transformers.FlaxBartForCausalLM) (BART model) * [BertConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bert#transformers.BertConfig) configuration class: [FlaxBertForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bert#transformers.FlaxBertForCausalLM) (BERT model) * [BigBirdConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/big_bird#transformers.BigBirdConfig) configuration class: [FlaxBigBirdForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM) (BigBird model) * [BloomConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bloom#transformers.BloomConfig) configuration class: [FlaxBloomForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bloom#transformers.FlaxBloomForCausalLM) (BLOOM model) * [ElectraConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/electra#transformers.ElectraConfig) configuration class: [FlaxElectraForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/electra#transformers.FlaxElectraForCausalLM) (ELECTRA model) * [GPT2Config](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/gpt2#transformers.GPT2Config) configuration class: [FlaxGPT2LMHeadModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel) (OpenAI GPT-2 model) * [GPTJConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/gptj#transformers.GPTJConfig) configuration class: [FlaxGPTJForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM) (GPT-J model) * [GPTNeoConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/gpt_neo#transformers.GPTNeoConfig) configuration class: [FlaxGPTNeoForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM) (GPT Neo model) * [OPTConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/opt#transformers.OPTConfig) configuration class: [FlaxOPTForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/opt#transformers.FlaxOPTForCausalLM) (OPT model) * [RobertaConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/roberta#transformers.RobertaConfig) configuration class: [FlaxRobertaForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM) (RoBERTa model) * [RobertaPreLayerNormConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig) configuration class: [FlaxRobertaPreLayerNormForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForCausalLM) (RoBERTa-PreLayerNorm model) * [XGLMConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/xglm#transformers.XGLMConfig) configuration class: [FlaxXGLMForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM) (XGLM model) * [XLMRobertaConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig) configuration class: [FlaxXLMRobertaForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForCausalLM) (XLM-RoBERTa model) Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration. Note: Loading a model from its configuration file does **not** load the model weights. It only affects the model’s configuration. Use [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.FlaxAutoModelForVision2Seq.from_pretrained) to load the model weights. Examples: Copied ``` >>> from transformers import AutoConfig, FlaxAutoModelForCausalLM >>> # Download configuration from huggingface.co and cache. >>> config = AutoConfig.from_pretrained("bert-base-cased") >>> model = FlaxAutoModelForCausalLM.from_config(config) ``` **from\_pretrained** [\](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/auto/auto_factory.py#L448) ( \*model\_args\*\*kwargs ) Parameters * **pretrained\_model\_name\_or\_path** (`str` or `os.PathLike`) — Can be either: * A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. * A path to a *directory* containing model weights saved using [save\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained), e.g., `./my_model_directory/`. * A path or url to a *PyTorch state\_dict save file* (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards. * **model\_args** (additional positional arguments, *optional*) — Will be passed along to the underlying model `__init__()` method. * **config** ([PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig), *optional*) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when: * The model is a model provided by the library (loaded with the *model id* string of a pretrained model). * The model was saved using [save\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained) and is reloaded by supplying the save directory. * The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named *config.json* is found in the directory. * **cache\_dir** (`str` or `os.PathLike`, *optional*) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. * **from\_pt** (`bool`, *optional*, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument). * **force\_download** (`bool`, *optional*, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. * **resume\_download** (`bool`, *optional*, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. * **proxies** (`Dict[str, str]`, *optional*) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. * **output\_loading\_info(`bool`,** *optional*, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. * **local\_files\_only(`bool`,** *optional*, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model). * **revision** (`str`, *optional*, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. * **trust\_remote\_code** (`bool`, *optional*, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. * **code\_revision** (`str`, *optional*, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. * **kwargs** (additional keyword arguments, *optional*) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded: * If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done) * If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function ([from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained)). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function. Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model. The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`: * **bart** — [FlaxBartForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bart#transformers.FlaxBartForCausalLM) (BART model) * **bert** — [FlaxBertForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bert#transformers.FlaxBertForCausalLM) (BERT model) * **big\_bird** — [FlaxBigBirdForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM) (BigBird model) * **bloom** — [FlaxBloomForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bloom#transformers.FlaxBloomForCausalLM) (BLOOM model) * **electra** — [FlaxElectraForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/electra#transformers.FlaxElectraForCausalLM) (ELECTRA model) * **gpt-sw3** — [FlaxGPT2LMHeadModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel) (GPT-Sw3 model) * **gpt2** — [FlaxGPT2LMHeadModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel) (OpenAI GPT-2 model) * **gpt\_neo** — [FlaxGPTNeoForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM) (GPT Neo model) * **gptj** — [FlaxGPTJForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM) (GPT-J model) * **opt** — [FlaxOPTForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/opt#transformers.FlaxOPTForCausalLM) (OPT model) * **roberta** — [FlaxRobertaForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM) (RoBERTa model) * **roberta-prelayernorm** — [FlaxRobertaPreLayerNormForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForCausalLM) (RoBERTa-PreLayerNorm model) * **xglm** — [FlaxXGLMForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM) (XGLM model) * **xlm-roberta** — [FlaxXLMRobertaForCausalLM](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForCausalLM) (XLM-RoBERTa model) Examples: Copied ``` >>> from transformers import AutoConfig, FlaxAutoModelForCausalLM >>> # Download model and configuration from huggingface.co and cache. >>> model = FlaxAutoModelForCausalLM.from_pretrained("bert-base-cased") >>> # Update configuration during loading >>> model = FlaxAutoModelForCausalLM.from_pretrained("bert-base-cased", output_attentions=True) >>> model.config.output_attentions True >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower) >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json") >>> model = FlaxAutoModelForCausalLM.from_pretrained( ... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config ... ) ```