Cohere
개요
The Cohere Command-R 모델은 Cohere팀이 Command-R: 프로덕션 규모의 검색 증강 생성라는 블로그 포스트에서 소개 되었습니다.
논문 초록:
Command-R은 기업의 프로덕션 규모 AI를 가능하게 하기 위해 RAG(검색 증강 생성)와 도구 사용을 목표로 하는 확장 가능한 생성 모델입니다. 오늘 우리는 대규모 프로덕션 워크로드를 목표로 하는 새로운 LLM인 Command-R을 소개합니다. Command-R은 높은 효율성과 강력한 정확성의 균형을 맞추는 ‘확장 가능한’ 모델 카테고리를 대상으로 하여, 기업들이 개념 증명을 넘어 프로덕션 단계로 나아갈 수 있게 합니다.
*Command-R은 검색 증강 생성(RAG)이나 외부 API 및 도구 사용과 같은 긴 문맥 작업에 최적화된 생성 모델입니다. 이 모델은 RAG 애플리케이션을 위한 최고 수준의 통합을 제공하고 기업 사용 사례에서 뛰어난 성능을 발휘하기 위해 우리의 업계 선도적인 Embed 및 Rerank 모델과 조화롭게 작동하도록 설계되었습니다. 기업이 대규모로 구현할 수 있도록 만들어진 모델로서, Command-R은 다음과 같은 특징을 자랑합니다:
- RAG 및 도구 사용에 대한 강력한 정확성
- 낮은 지연 시간과 높은 처리량
- 더 긴 128k 컨텍스트와 낮은 가격
- 10개의 주요 언어에 걸친 강력한 기능
- 연구 및 평가를 위해 HuggingFace에서 사용 가능한 모델 가중치
모델 체크포인트는 이곳에서 확인하세요. 이 모델은 Saurabh Dash과 Ahmet Üstün에 의해 기여 되었습니다. Hugging Face에서 이 코드의 구현은 GPT-NeoX에 기반하였습니다.
사용 팁
Hub에 업로드된 체크포인트들은 torch_dtype = 'float16'을 사용합니다.
이는 AutoModel API가 체크포인트를 torch.float32에서 torch.float16으로 변환하는 데 사용됩니다.
온라인 가중치의 dtype은 model = AutoModelForCausalLM.from_pretrained("path", torch_dtype = "auto")를 사용하여 모델을 초기화할 때 torch_dtype="auto"를 사용하지 않는 한 대부분 무관합니다. 그 이유는 모델이 먼저 다운로드되고(온라인 체크포인트의 dtype 사용), 그 다음 torch의 기본 dtype으로 변환되며(이때 torch.float32가 됨), 마지막으로 config에 torch_dtype이 제공된 경우 이를 사용하기 때문입니다.
모델을 float16으로 훈련하는 것은 권장되지 않으며 nan을 생성하는 것으로 알려져 있습니다. 따라서 모델은 bfloat16으로 훈련해야 합니다.
# 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)- Flash Attention 2를
attn_implementation="flash_attention_2"를 통해 사용할 때는,from_pretrained클래스 메서드에torch_dtype을 전달하지 말고 자동 혼합 정밀도 훈련(Automatic Mixed-Precision training)을 사용하세요.Trainer를 사용할 때는 단순히fp16또는bf16을True로 지정하면 됩니다. 그렇지 않은 경우에는torch.autocast를 사용하고 있는지 확인하세요. 이는 Flash Attention이fp16와bf16데이터 타입만 지원하기 때문에 필요합니다.
리소스
Command-R을 시작하는 데 도움이 되는 Hugging Face와 community 자료 목록(🌎로 표시됨) 입니다. 여기에 포함될 자료를 제출하고 싶으시다면 PR(Pull Request)를 열어주세요. 리뷰 해드리겠습니다! 자료는 기존 자료를 복제하는 대신 새로운 내용을 담고 있어야 합니다.
FP16 모델 로딩
# 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)
# command-r 챗 템플릿으로 메세지 형식을 정하세요
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)bitsandbytes 라이브러리를 이용해서 4bit 양자화된 모델 로딩
# 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 theinputs_idspassed 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. Ifnum_key_value_heads=num_attention_heads, the model will use Multi Head Attention (MHA), ifnum_key_value_heads=1the 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 tonum_attention_heads. - hidden_act (
strorfunction, 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 toTrue) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant ifconfig.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 toTrue) — 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 longermax_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, afactorof 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 thefactorfield 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 2long_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 2low_freq_factor(float, optional): Only used with ‘llama3’. Scaling factor applied to low frequency components of the RoPEhigh_freq_factor(float, optional*): Only used with ‘llama3’. Scaling factor applied to high frequency components of the RoPE - attention_bias (
bool, defaults toFalse, optional, defaults toFalse) — 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 toFalse) — 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.configCohereTokenizerFast
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 toFalse) — Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra spaces. - unk_token (
strortokenizers.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 (
strortokenizers.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 (
strortokenizers.AddedToken, optional, defaults to"<|END_OF_TURN_TOKEN|>") — The end of sequence token. - add_bos_token (
bool, optional, defaults toTrue) — Whether or not to add anbos_tokenat the start of sequences. - add_eos_token (
bool, optional, defaults toFalse) — Whether or not to add aneos_tokenat the end of sequences. - use_default_system_prompt (
bool, optional, defaults toFalse) — Whether or not the default system prompt for Cohere tokenizer should be used. - add_prefix_space (
bool, optional, defaults toFalse) — 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.
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 toFalse) — 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]
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.
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)
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.LongTensorof 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.
- attention_mask (
torch.Tensorof 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.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
If
past_key_valuesis used, optionally only the lastinput_idshave to be input (seepast_key_values).If you want to change padding behavior, you should read
modeling_opt._prepare_decoder_attention_maskand 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.LongTensorof 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]. - past_key_values (
Cacheortuple(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 thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=Trueorconfig.use_cache=True.Two formats are allowed:
- a Cache instance, see our kv cache guide;
- Tuple of
tuple(torch.FloatTensor)of lengthconfig.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_valuesare passed, the legacy cache format will be returned.If
past_key_valuesare used, the user can optionally input only the lastinput_ids(those that don’t have their past key value states given to this model) of shape(batch_size, 1)instead of allinput_idsof shape(batch_size, sequence_length). - inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. - use_cache (
bool, optional) — If set toTrue,past_key_valueskey value states are returned and can be used to speed up decoding (seepast_key_values). - output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder 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
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.LongTensorof 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.
- attention_mask (
torch.Tensorof 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.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
If
past_key_valuesis used, optionally only the lastinput_idshave to be input (seepast_key_values).If you want to change padding behavior, you should read
modeling_opt._prepare_decoder_attention_maskand 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.LongTensorof 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]. - past_key_values (
Cacheortuple(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 thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=Trueorconfig.use_cache=True.Two formats are allowed:
- a Cache instance, see our kv cache guide;
- Tuple of
tuple(torch.FloatTensor)of lengthconfig.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_valuesare passed, the legacy cache format will be returned.If
past_key_valuesare used, the user can optionally input only the lastinput_ids(those that don’t have their past key value states given to this model) of shape(batch_size, 1)instead of allinput_idsof shape(batch_size, sequence_length). - inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. - use_cache (
bool, optional) — If set toTrue,past_key_valueskey value states are returned and can be used to speed up decoding (seepast_key_values). - output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. - return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. - Args —
labels (
torch.LongTensorof 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 (seeinput_idsdocstring). Tokens with indices set to-100are 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 lastnum_logits_to_keeptokens. If0, calculate logits for allinput_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.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Language modeling loss (for next-token prediction). -
logits (
torch.FloatTensorof 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 whenuse_cache=Trueis passed or whenconfig.use_cache=True) — Tuple oftuple(torch.FloatTensor)of lengthconfig.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_valuesinput) to speed up sequential decoding. -
hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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."