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This model was released on 2024-03-12 and added to Hugging Face Transformers on 2024-03-15.

PyTorch FlashAttention SDPA Tensor parallelism

Cohere

Cohere Command-R is a 35B parameter multilingual large language model designed for long context tasks like retrieval-augmented generation (RAG) and calling external APIs and tools. The model is specifically trained for grounded generation and supports both single-step and multi-step tool use. It supports a context length of 128K tokens.

You can find all the original Command-R checkpoints under the Command Models collection.

Click on the Cohere models in the right sidebar for more examples of how to apply Cohere to different language tasks.

The example below demonstrates how to generate text with Pipeline or the AutoModel, and from the command line.

Pipeline
AutoModel
transformers CLI 
import torch
from transformers import pipeline

pipeline = pipeline(
    task="text-generation",
    model="CohereForAI/c4ai-command-r-v01",
    dtype=torch.float16,
    device=0
)
pipeline("Plants create energy through a process known as")

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

The example below uses bitsandbytes to quantize the weights to 4-bits.

import torch
from transformers import BitsAndBytesConfig, AutoTokenizer, AutoModelForCausalLM

bnb_config = BitsAndBytesConfig(load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")
model = AutoModelForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01", dtype=torch.float16, device_map="auto", quantization_config=bnb_config, attn_implementation="sdpa")

# format message with the Command-R chat template
messages = [{"role": "user", "content": "How do plants make energy?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
output = model.generate(
    input_ids,
    max_new_tokens=100,
    do_sample=True,
    temperature=0.3,
    cache_implementation="static",
)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Use the AttentionMaskVisualizer to better understand what tokens the model can and cannot attend to.

from transformers.utils.attention_visualizer import AttentionMaskVisualizer

visualizer = AttentionMaskVisualizer("CohereForAI/c4ai-command-r-v01")
visualizer("Plants create energy through a process known as")

Notes

CohereConfig

class transformers.CohereConfig

< >

( vocab_size: typing.Optional[int] = 256000 hidden_size: typing.Optional[int] = 8192 intermediate_size: typing.Optional[int] = 22528 logit_scale: typing.Optional[float] = 0.0625 num_hidden_layers: typing.Optional[int] = 40 num_attention_heads: typing.Optional[int] = 64 num_key_value_heads: typing.Optional[int] = None hidden_act: typing.Optional[str] = 'silu' max_position_embeddings: typing.Optional[int] = 8192 initializer_range: typing.Optional[float] = 0.02 layer_norm_eps: typing.Optional[int] = 1e-05 use_cache: typing.Optional[bool] = True pad_token_id: typing.Optional[int] = 0 bos_token_id: typing.Optional[int] = 5 eos_token_id: typing.Optional[int] = 255001 tie_word_embeddings: typing.Optional[bool] = True rope_parameters: typing.Union[transformers.modeling_rope_utils.RopeParameters, dict[str, transformers.modeling_rope_utils.RopeParameters], NoneType] = None attention_bias: typing.Optional[bool] = False attention_dropout: typing.Optional[float] = 0.0 use_qk_norm: typing.Optional[bool] = 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, check out 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_parameters (RopeParameters, optional) — Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for rope_theta and optionally parameters used for scaling in case you want to use RoPE with longer max_position_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.
  • 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

CohereTokenizer

class transformers.CohereTokenizer

< >

( errors: str = 'replace' unk_token: str = '<UNK>' bos_token: str = '<BOS_TOKEN>' eos_token: str = '<|END_OF_TURN_TOKEN|>' pad_token: str = '<PAD>' cls_token: str = '<CLS>' sep_token: str = '<SEP>' mask_token: str = '<MASK_TOKEN>' add_bos_token: bool = True add_eos_token: bool = False use_default_system_prompt: bool = False add_prefix_space: bool = False vocab: typing.Optional[dict] = None merges: typing.Optional[list] = None **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
  • vocab (dict, optional) — Custom vocabulary dictionary. If not provided, vocabulary is loaded from vocab_file.
  • merges (list, optional) — Custom merges list. If not provided, merges are loaded from merges_file.

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 TokenizersBackend which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

apply_grounded_generation_template

< >

( conversation: list documents: list citation_mode: typing.Literal['fast', 'accurate'] = 'accurate' **kwargs ) → str

Parameters

  • conversation (list[dict[str, str]]) — A list of dicts with “role” and “content” keys, representing the chat history so far.
  • documents (list[dict[str, str]) — A list of dicts, representing documents or tool outputs to ground your generation on. A document is a semistructured dict, with a string to string mapping. Common fields are url, title, snippet etc but should be descriptive of the key. They will get rendered into the prompt.
  • citation_mode — either “accurate” (prompt the model to generate an answer first, then rewrite it with citation spans in) or “fast”, where the prompt instructs the model to generate an answer with citations in directly. The former has higher quality citations, the latter requires fewer tokens to be generated.
  • add_generation_prompt (bool, optional) — Whether to end the prompt with the token(s) that indicate the start of an assistant message. This is useful when you want to generate a response from the model. Note that this argument will be passed to the chat template, and so it must be supported in the template for this argument to have any effect.
  • tokenize (bool, defaults to True) — Whether to tokenize the output. If False, the output will be a string.
  • padding (bool, defaults to False) — Whether to pad sequences to the maximum length. Has no effect if tokenize is False.
  • truncation (bool, defaults to False) — Whether to truncate sequences at the maximum length. Has no effect if tokenize is False.
  • max_length (int, optional) — Maximum length (in tokens) to use for padding or truncation. Has no effect if tokenize is False. If not specified, the tokenizer’s max_length attribute will be used as a default.
  • return_tensors (str or TensorType, optional) — If set, will return tensors of a particular framework. Has no effect if tokenize is False. Acceptable values are:
    • 'pt': Return PyTorch torch.Tensor objects.
    • 'np': Return NumPy np.ndarray objects.
  • return_dict (bool, optional, defaults to False) — Whether to return a dictionary with named outputs. Has no effect if tokenize is False.
  • **tokenizer_kwargs — Additional kwargs to pass to the tokenizer.

Returns

str

A rendered prompt string. or if tokenize=True: list[int]: A list of token ids representing the tokenized chat so far, including control tokens. This output is ready to pass to the model, either directly or via methods like generate().

Create a Command-R grounded generation (aka RAG) prompt.

Once rendered, the prompt instructs the model to generate a response with citations in, based on supplied documents.

Conceptually, this works in the same way as apply_chat_format, but takes additional documents and parameter citation_mode parameters.

Converts a list of dictionaries with "role" and "content" keys and a list of documents for the model to ground its response on into a prompt string, or a list of token ids. This method will use the tokenizer’s grounded_generation_template template specified at the class level. You can override the default template using the grounded_generation_template kwarg but the quality of your results may decrease.

Examples:

>> tokenizer = CohereTokenizer.from_pretrained('CohereForAI/c4ai-command-r-v01')

>> # define documents:
>> documents = [
    { "title": "Tall penguins", "text": "Emperor penguins are the tallest." },
    { "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica."}
]
>> # define a conversation:
>> conversation = [
    {"role": "user", "content": "Whats the biggest penguin in the world?"}
]
>> # render the prompt, ready for user to inspect, or for input into the model:
>> grounded_generation_prompt = tokenizer.apply_grounded_generation_template(conversation, documents=documents, tokenize=False, add_generation_prompt=True)
>> print(grounded_generation_prompt)
>> inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors='pt')
>> outputs = model.generate(inputs, max_new_tokens=128)
>> print(tokenizer.decode(outputs[0]))

apply_tool_use_template

< >

( conversation: list tools: list **kwargs ) → str

Parameters

  • conversation (list[dict[str, str]]) — A list of dicts with “role” and “content” keys, representing the chat history so far.
  • tools (list[Dict]) — a list of tools to render into the prompt for the model to choose from. See an example at the bottom of the docstring. The format should be:
    • name (str): The name of the tool to be called. Valid names contain only the characters a-z, A-Z, 0-9, _ and must not begin with a digit.
    • description (str): The description of what the tool does, the model uses the description to choose when and how to call the function.
    • parameterdefinitions (list[Dict]): The input parameters of the tool. Accepts a dictionary where the key is the name of the parameter and the value is the parameter spec. Valid parameter names contain only the characters a-z, A-Z, 0-9, and must not begin with a digit. Parameter specs are as follows:
      • description (str): The description of the parameter.
      • type (str): the type of the parameter - most effective for python builtin data types, such as ‘str’, ‘bool’
      • required: boolean: Denotes whether the parameter is always present (required) or not. Defaults to not required.
  • add_generation_prompt (bool, optional) — Whether to end the prompt with the token(s) that indicate the start of an assistant message. This is useful when you want to generate a response from the model. Note that this argument will be passed to the chat template, and so it must be supported in the template for this argument to have any effect.
  • tokenize (bool, defaults to True) — Whether to tokenize the output. If False, the output will be a string.
  • padding (bool, defaults to False) — Whether to pad sequences to the maximum length. Has no effect if tokenize is False.
  • truncation (bool, defaults to False) — Whether to truncate sequences at the maximum length. Has no effect if tokenize is False.
  • max_length (int, optional) — Maximum length (in tokens) to use for padding or truncation. Has no effect if tokenize is False. If not specified, the tokenizer’s max_length attribute will be used as a default.
  • return_tensors (str or TensorType, optional) — If set, will return tensors of a particular framework. Has no effect if tokenize is False. Acceptable values are:
    • 'pt': Return PyTorch torch.Tensor objects.
    • 'np': Return NumPy np.ndarray objects.
  • return_dict (bool, optional, defaults to False) — Whether to return a dictionary with named outputs. Has no effect if tokenize is False.
  • **tokenizer_kwargs — Additional kwargs to pass to the tokenizer.

Returns

str

A rendered prompt string. or if tokenize=True: list[int]: A list of token ids representing the tokenized chat so far, including control tokens. This output is ready to pass to the model, either directly or via methods like generate().

Create a Command-R tool-use prompt.

Once rendered, the prompt instructs the model to generate a list of actions to perform on a set of user supplied tools to help carry out the user’s requests.

Conceptually, this works in the same way as apply_chat_format, but takes an additional tools parameter.

Converts a chat in the form of a list of dictionaries with "role" and "content" keys and a list of available tools for the model to use into a prompt string, or a list of token ids. This method will use the tokenizer’s default_tool_use_template template specified at the class level. You can override the default template using the tool_use_template kwarg but the quality of your results may decrease.

Examples:

>> tokenizer = CohereTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")
>> tools = [
    {
        "name": "internet_search",
        "description": "Returns a list of relevant document snippets for a textual query retrieved from the internet",
        "parameter_definitions": {
            "query": {
                "description": "Query to search the internet with",
                "type": "str",
                "required": True,
            }
        },
    },
    {
        "name": "directly_answer",
        "description": "Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history",
        "parameter_definitions": {},
    },
]
>> conversation = [
    {"role": "user", "content": "Whats the biggest penguin in the world?"},
]
>> # Render the prompt, ready for user to inspect, or for input into the model
>> prompt = tokenizer.apply_tool_use_template(conversation, tools=tools, tokenize=False, add_generation_prompt=True)
>> print(prompt)
>> inputs = tokenizer.encode(grounded_generation_prompt, add_special_tokens=False, return_tensors='pt')
>> outputs = model.generate(inputs, max_new_tokens=128)
>> print(tokenizer.decode(outputs[0]))
[
    {
        "tool_name": "internet_search",
        "parameters": {
            "query": "biggest penguin in the world"
        }
    }
]

CohereModel

class transformers.CohereModel

< >

( 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.

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, pruning heads 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.

forward

< >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[transformers.cache_utils.Cache] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None cache_position: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.modeling_outputs.BaseModelOutputWithPast or tuple(torch.FloatTensor)

Parameters

  • 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. 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?

  • 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_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 instance is allowed as input, see our kv cache guide. If no past_key_values are passed, 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.
  • 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.
  • 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).

Returns

transformers.modeling_outputs.BaseModelOutputWithPast or tuple(torch.FloatTensor)

A 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 (CohereConfig) 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 instance. For more details, see our kv cache guide.

    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.

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

< >

( config )

Parameters

  • config (CohereForCausalLM) — 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.

The Cohere Model for causal language modeling.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model 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.

forward

< >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[transformers.cache_utils.Cache] = 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 cache_position: typing.Optional[torch.LongTensor] = None logits_to_keep: typing.Union[int, torch.Tensor] = 0 **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.modeling_outputs.CausalLMOutputWithPast or tuple(torch.FloatTensor)

Parameters

  • 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. 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?

  • 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_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 instance is allowed as input, see our kv cache guide. If no past_key_values are passed, 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).
  • 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.
  • 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).

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 (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.

    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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