#
Cohere

##
Overview

The Cohere Command-R model was proposed in the blogpost Command-R: Retrieval Augmented Generation at Production Scale by the Cohere Team.

The abstract from the paper is the following:

Command-R is a scalable generative model targeting RAG and Tool Use to enable production-scale AI for enterprise. Today, we are introducing Command-R, a new LLM aimed at large-scale production workloads. Command-R targets the emerging “scalable” category of models that balance high efficiency with strong accuracy, enabling companies to move beyond proof of concept, and into production.

*Command-R is a generative model optimized for long context tasks such as retrieval augmented generation (RAG) and using external APIs and tools. It is designed to work in concert with our industry-leading Embed and Rerank models to provide best-in-class integration for RAG applications and excel at enterprise use cases. As a model built for companies to implement at scale, Command-R boasts:

- Strong accuracy on RAG and Tool Use
- Low latency, and high throughput
- Longer 128k context and lower pricing
- Strong capabilities across 10 key languages
- Model weights available on HuggingFace for research and evaluation

Checkout model checkpoints here. This model was contributed by Saurabh Dash and Ahmet Üstün. The code of the implementation in Hugging Face is based on GPT-NeoX here.

##
Usage tips

The checkpoints uploaded on the Hub use `torch_dtype = 'float16'`, which will be used by the `AutoModel` API to cast the checkpoints from `torch.float32` to `torch.float16`.

The `dtype` of the online weights is mostly irrelevant unless you are using `torch_dtype="auto"` when initializing a model using `model = AutoModelForCausalLM.from_pretrained("path", torch_dtype = "auto")`. The reason is that the model will first be downloaded ( using the `dtype` of the checkpoints online), then it will be casted to the default `dtype` of `torch` (becomes `torch.float32`), and finally, if there is a `torch_dtype` provided in the config, it will be used.

Training the model in `float16` is not recommended and is known to produce `nan`; as such, the model should be trained in `bfloat16`.

The model and tokenizer can be loaded via:

    # pip install transformers
    from transformers import AutoTokenizer, AutoModelForCausalLM

    model_id = "CohereForAI/c4ai-command-r-v01"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id)

    # Format message with the command-r chat template
    messages = [{"role": "user", "content": "Hello, how are you?"}]
    input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
    ## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>

    gen_tokens = model.generate(
        input_ids,
        max_new_tokens=100,
        do_sample=True,
        temperature=0.3,
        )

    gen_text = tokenizer.decode(gen_tokens[0])
    print(gen_text)

- When using Flash Attention 2 via `attn_implementation="flash_attention_2"`, don’t pass `torch_dtype` to the `from_pretrained` class method and use Automatic Mixed-Precision training. When using `Trainer`, it is simply specifying either `fp16` or `bf16` to `True`. Otherwise, make sure you are using `torch.autocast`. This is required because the Flash Attention only support `fp16` and `bf16` data type.

##
Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Command-R. 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.

Text Generation

Loading FP16 model

    # pip install transformers
    from transformers import AutoTokenizer, AutoModelForCausalLM

    model_id = "CohereForAI/c4ai-command-r-v01"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id)

    # Format message with the command-r chat template
    messages = [{"role": "user", "content": "Hello, how are you?"}]
    input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
    ## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>

    gen_tokens = model.generate(
        input_ids,
        max_new_tokens=100,
        do_sample=True,
        temperature=0.3,
        )

    gen_text = tokenizer.decode(gen_tokens[0])
    print(gen_text)

Loading bitsnbytes 4bit quantized model

    # pip install transformers bitsandbytes accelerate
    from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

    bnb_config = BitsAndBytesConfig(load_in_4bit=True)

    model_id = "CohereForAI/c4ai-command-r-v01"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)

    gen_tokens = model.generate(
        input_ids,
        max_new_tokens=100,
        do_sample=True,
        temperature=0.3,
        )

    gen_text = tokenizer.decode(gen_tokens[0])
    print(gen_text)

##
CohereConfig

###
class transformers.CohereConfig

< source >

( vocab_size = 256000 hidden_size = 8192 intermediate_size = 22528 logit_scale = 0.0625 num_hidden_layers = 40 num_attention_heads = 64 num_key_value_heads = None hidden_act = 'silu' max_position_embeddings = 8192 initializer_range = 0.02 layer_norm_eps = 1e-05 use_cache = True pad_token_id = 0 bos_token_id = 5 eos_token_id = 255001 tie_word_embeddings = True rope_theta = 10000.0 rope_scaling = None attention_bias = False attention_dropout = 0.0 use_qk_norm = False **kwargs )

Parameters

- vocab_size (`int`, optional, defaults to 256000) — Vocabulary size of the Cohere model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling CohereModel
- hidden_size (`int`, optional, defaults to 8192) — Dimension of the hidden representations.
- intermediate_size (`int`, optional, defaults to 22528) — Dimension of the MLP representations.
- logit_scale (`float`, optional, defaults to 0.0625) — The scaling factor for the output logits.
- num_hidden_layers (`int`, optional, defaults to 40) — Number of hidden layers in the Transformer decoder.
- num_attention_heads (`int`, optional, defaults to 64) — Number of attention heads for each attention layer in the Transformer decoder.
- num_key_value_heads (`int`, optional) — 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 checkout this paper. If it is not specified, will default to `num_attention_heads`.
- hidden_act (`str` or `function`, optional, defaults to `"silu"`) — The non-linear activation function (function or string) in the decoder.
- 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.
- layer_norm_eps (`float`, optional, defaults to 1e-05) — The epsilon used by the layer normalization.
- 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.
- bos_token_id (`int`, optional, defaults to 5) — Beginning of stream token id.
- eos_token_id (`int`, optional, defaults to 255001) — End 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.
- rope_scaling (`Dict`, optional) — Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of [‘default’, ‘linear’, ‘dynamic’, ‘yarn’, ‘longrope’, ‘llama3’], with ‘default’ being the original RoPE implementation. `factor` (`float`, optional): Used with all rope types except ‘default’. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x original maximum pre-trained length. `original_max_position_embeddings` (`int`, optional): Used with ‘dynamic’, ‘longrope’ and ‘llama3’. The original max position embeddings used during pretraining. `attention_factor` (`float`, optional): Used with ‘yarn’ and ‘longrope’. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, optional): Only used with ‘yarn’. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, optional): Only used with ‘yarn’. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, optional): Only used with ‘longrope’. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, optional): Only used with ‘longrope’. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, optional): Only used with ‘llama3’. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, optional*): Only used with ‘llama3’. Scaling factor applied to high frequency components of the RoPE
- 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.
- use_qk_norm (`bool`, optional, defaults to `False`) — Whether to use query-key normalization in the attention

This is the configuration class to store the configuration of a CohereModel. It is used to instantiate an Cohere model according to the specified arguments, defining the model architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information. Instantiating a configuration with the defaults will yield a similar configuration to that of the CohereForAI/c4ai-command-r-v01 model.

    >>> from transformers import CohereModel, CohereConfig

    >>> # Initializing a Cohere model configuration
    >>> configuration = CohereConfig()

    >>> # Initializing a model from the Cohere configuration
    >>> model = CohereModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config

##
CohereTokenizerFast

###
class transformers.CohereTokenizerFast

< source >

( vocab_file = None merges_file = None tokenizer_file = None clean_up_tokenization_spaces = False unk_token = '<UNK>' bos_token = '<BOS_TOKEN>' eos_token = '<|END_OF_TURN_TOKEN|>' add_bos_token = True add_eos_token = False use_default_system_prompt = False add_prefix_space = False **kwargs )

Parameters

- vocab_file (`str`, optional) — Path to the vocabulary file.
- merges_file (`str`, optional) — Path to the merges file.
- tokenizer_file (`str`, optional) — 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_TOKEN>"`) — 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 `"<|END_OF_TURN_TOKEN|>"`) — The end of sequence 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.
- use_default_system_prompt (`bool`, optional, defaults to `False`) — Whether or not the default system prompt for Cohere tokenizer should be used.
- add_prefix_space (`bool`, optional, defaults to `False`) — Whether or not the tokenizer should automatically add a prefix space

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

This uses notably ByteFallback and NFC normalization.

    >>> from transformers import AutoTokenizer

    >>> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")
    >>> tokenizer.encode("Hello this is a test")
    [5, 28339, 2075, 1801, 1671, 3282]

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.

You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since the model was not pretrained this way, it might yield a decrease in performance.

When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.

This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

####
build_inputs_with_special_tokens

< source >

( token_ids_0 token_ids_1 = None )

####
get_special_tokens_mask

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: bool = False ) →
A list of integers in the range [0, 1]

Parameters

- token_ids_0 (`List[int]`) — List of ids of the first sequence.
- token_ids_1 (`List[int]`, optional) — List of ids of the second sequence.
- already_has_special_tokens (`bool`, optional, defaults to `False`) — Whether or not the token list is already formatted with special tokens for the model.

Returns

A list of integers in the range [0, 1]

1 for a special token, 0 for a sequence token.

Retrieves 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` or `encode_plus` methods.

####
create_token_type_ids_from_sequences

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) →
`List[int]`

Parameters

- token_ids_0 (`List[int]`) — The first tokenized sequence.
- token_ids_1 (`List[int]`, optional) — The second tokenized sequence.

Returns

`List[int]`

The token type ids.

Create the token type IDs corresponding to the sequences passed. What are token type IDs?

Should be overridden in a subclass if the model has a special way of building those.

####
update_post_processor

< source >

( )

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

####
save_vocabulary

< source >

( save_directory: str filename_prefix: typing.Optional[str] = None ) →
`Tuple(str)`

Parameters

- save_directory (`str`) — The directory in which to save the vocabulary.
- filename_prefix (`str`, optional) — An optional prefix to add to the named of the saved files.

Returns

`Tuple(str)`

Paths to the files saved.

Save only the vocabulary of the tokenizer (vocabulary + added tokens).

This method won’t save the configuration and special token mappings of the tokenizer. Use `_save_pretrained()` to save the whole state of the tokenizer.

##
CohereModel

###
class transformers.CohereModel

< source >

( config: CohereConfig )

Parameters

- config (CohereConfig) — 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() method to load the model weights.
- config — CohereConfig

The bare Cohere 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 etc.).

This model is also a PyTorch 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.

Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a `CohereDecoderLayer`

####
forward

< source >

( input_ids: LongTensor = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Union[transformers.cache_utils.Cache, typing.List[torch.FloatTensor], NoneType] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None **flash_attn_kwargs: typing_extensions.Unpack[transformers.modeling_flash_attention_utils.FlashAttentionKwargs] )

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are 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?

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  If `past_key_values` is used, optionally only the last `input_ids` have to be input (see `past_key_values`).

  If you want to change padding behavior, you should read `modeling_opt._prepare_decoder_attention_mask` and modify to your needs. See diagram 1 in the paper for more information on the default strategy.

  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- 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?

- past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, 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`.

  Two formats are allowed:

  - a Cache instance, see our kv cache guide;
  - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format.

  The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned.

  If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` 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`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

The CohereModel forward method, overrides the `__call__` special method.

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.

##
CohereForCausalLM

###
class transformers.CohereForCausalLM

< source >

( config )

####
forward

< source >

( input_ids: LongTensor = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None num_logits_to_keep: int = 0 **loss_kwargs ) →
transformers.modeling_outputs.CausalLMOutputWithPast or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are 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?

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  If `past_key_values` is used, optionally only the last `input_ids` have to be input (see `past_key_values`).

  If you want to change padding behavior, you should read `modeling_opt._prepare_decoder_attention_mask` and modify to your needs. See diagram 1 in the paper for more information on the default strategy.

  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- 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?

- past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, 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`.

  Two formats are allowed:

  - a Cache instance, see our kv cache guide;
  - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format.

  The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned.

  If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` 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`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- Args — 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]`.

  num_logits_to_keep (`int`, optional): Calculate logits for the last `num_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.

Returns

transformers.modeling_outputs.CausalLMOutputWithPast or `tuple(torch.FloatTensor)`

A 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 (CohereConfig) 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 (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`)

  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.

The CohereForCausalLM forward method, overrides the `__call__` special method.

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.

Example:

    >> from transformers import AutoTokenizer, CohereForCausalLM

    >> model = CohereForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01")
    >> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")

    >> prompt = "Hey, are you conscious? Can you talk to me?"
    >> 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]
    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."

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