Llama (original) (raw)
Llama is a family of large language models ranging from 7B to 65B parameters. These models are focused on efficient inference (important for serving language models) by training a smaller model on more tokens rather than training a larger model on fewer tokens. The Llama model is based on the GPT architecture, but it uses pre-normalization to improve training stability, replaces ReLU with SwiGLU to improve performance, and replaces absolute positional embeddings with rotary positional embeddings (RoPE) to better handle longer sequence lengths.
You can find all the original Llama checkpoints under the Huggy Llama organization.
Click on the Llama models in the right sidebar for more examples of how to apply Llama 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="huggyllama/llama-7b", torch_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 torchao to only quantize the weights to int4.
import torch from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
quantization_config = TorchAoConfig("int4_weight_only", group_size=128) model = AutoModelForCausalLM.from_pretrained( "huggyllama/llama-30b", torch_dtype=torch.bfloat16, device_map="auto", quantization_config=quantization_config )
tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-30b") input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to("cuda")
output = model.generate(**input_ids, 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("huggyllama/llama-7b") visualizer("Plants create energy through a process known as")
Notes
- The tokenizer is a byte-pair encoding model based on SentencePiece. During decoding, if the first token is the start of the word (for example, “Banana”), the tokenizer doesn’t prepend the prefix space to the string.
LlamaConfig
class transformers.LlamaConfig
( vocab_size = 32000 hidden_size = 4096 intermediate_size = 11008 num_hidden_layers = 32 num_attention_heads = 32 num_key_value_heads = None hidden_act = 'silu' max_position_embeddings = 2048 initializer_range = 0.02 rms_norm_eps = 1e-06 use_cache = True pad_token_id = None bos_token_id = 1 eos_token_id = 2 pretraining_tp = 1 tie_word_embeddings = False rope_theta = 10000.0 rope_scaling = None attention_bias = False attention_dropout = 0.0 mlp_bias = False head_dim = None **kwargs )
Parameters
- vocab_size (
int, optional, defaults to 32000) — Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by theinputs_idspassed when calling LlamaModel - hidden_size (
int, optional, defaults to 4096) — Dimension of the hidden representations. - intermediate_size (
int, optional, defaults to 11008) — Dimension of the MLP representations. - num_hidden_layers (
int, optional, defaults to 32) — Number of hidden layers in the Transformer decoder. - num_attention_heads (
int, optional, defaults to 32) — 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 2048) — The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens, Llama 2 up to 4096, CodeLlama up to 16384. - initializer_range (
float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - rms_norm_eps (
float, optional, defaults to 1e-06) — The epsilon used by the rms normalization layers. - use_cache (
bool, optional, defaults 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) — Padding token id. - bos_token_id (
int, optional, defaults to 1) — Beginning of stream token id. - eos_token_id (
int, optional, defaults to 2) — End of stream token id. - pretraining_tp (
int, optional, defaults to 1) — Experimental feature. Tensor parallelism rank used during pretraining. Please refer to this document to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to this issue. - tie_word_embeddings (
bool, optional, defaults toFalse) — 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.originalmaxpositionembeddings(int, optional_): Used with ‘dynamic’, ‘longrope’ and ‘llama3’. The original max position embeddings used during pretraining.attentionfactor(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.betafast(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.betaslow(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.shortfactor(List[float],_ optional_): Only used with ‘longrope’. The scaling factor to be applied to short contexts (<originalmaxpositionembeddings). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2longfactor(List[float],_ optional_): Only used with ‘longrope’. The scaling factor to be applied to long contexts (<originalmaxpositionembeddings). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2lowfreqfactor(float,_ optional_): Only used with ‘llama3’. Scaling factor applied to low frequency components of the RoPEhighfreqfactor(float,_ optional*): Only used with ‘llama3’. Scaling factor applied to high frequency components of the RoPE - attention_bias (
bool, 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. - mlp_bias (
bool, optional, defaults toFalse) — Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. - head_dim (
int, optional) — The attention head dimension. If None, it will default to hidden_size // num_attention_heads
This is the configuration class to store the configuration of a LlamaModel. It is used to instantiate an LLaMA model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LLaMA-7B.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
from transformers import LlamaModel, LlamaConfig
configuration = LlamaConfig()
model = LlamaModel(configuration)
configuration = model.config
LlamaTokenizer
class transformers.LlamaTokenizer
( vocab_file unk_token = '' bos_token = '' eos_token = '' pad_token = None sp_model_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None add_bos_token = True add_eos_token = False clean_up_tokenization_spaces = False use_default_system_prompt = False spaces_between_special_tokens = False legacy = None add_prefix_space = True **kwargs )
Parameters
- vocab_file (
str) — Path to the vocabulary file. - 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"<s>") — The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. - eos_token (
strortokenizers.AddedToken, optional, defaults to"</s>") — The end of sequence token. - pad_token (
strortokenizers.AddedToken, optional) — A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation. - sp_model_kwargs (
Dict[str, Any],Optional, optional) — Will be passed to theSentencePieceProcessor.__init__()method. The Python wrapper for SentencePiece can be used, among other things, to set:enable_sampling: Enable subword regularization.nbest_size: Sampling parameters for unigram. Invalid for BPE-Dropout.
*nbest_size = {0,1}: No sampling is performed.
*nbest_size > 1: samples from the nbest_size results.
*nbest_size < 0: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm.alpha: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout.
- add_bos_token (
bool, optional, defaults 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. - 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. - use_default_system_prompt (
bool, optional, defaults toFalse) — Whether or not the default system prompt for Llama should be used. - spaces_between_special_tokens (
bool, optional, defaults toFalse) — Whether or not to add spaces between special tokens. - legacy (
bool, optional) — Whether or not thelegacybehavior of the tokenizer should be used. Legacy is before the merge of #24622 and #25224 which includes fixes to properly handle tokens that appear after special tokens. Make sure to also setfrom_slowtoTrue. A simple example:legacy=True:
Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is no padding token in the original model.
build_inputs_with_special_tokens
( token_ids_0 token_ids_1 = None )
get_special_tokens_mask
( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: bool = False ) → List[int]
Parameters
- token_ids_0 (
List[int]) — List of IDs. - token_ids_1 (
List[int], optional) — Optional second list of IDs for sequence pairs. - already_has_special_tokens (
bool, optional, defaults toFalse) — Whether or not the token list is already formatted with special tokens for the model.
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model method.
create_token_type_ids_from_sequences
( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]
Parameters
- token_ids_0 (
List[int]) — List of ids. - token_ids_1 (
List[int], optional) — Optional second list of IDs for sequence pairs.
List of token type IDs according to the given sequence(s).
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence |
if token_ids_1 is None, only returns the first portion of the mask (0s).
save_vocabulary
( save_directory filename_prefix: typing.Optional[str] = None ) → Tuple(str)
Parameters
- save_directory (
str) — The directory in which to save the vocabulary.
Paths to the files saved.
Save the vocabulary and special tokens file to a directory.
LlamaTokenizerFast
class transformers.LlamaTokenizerFast
( vocab_file = None tokenizer_file = None clean_up_tokenization_spaces = False unk_token = '' bos_token = '' eos_token = '' add_bos_token = True add_eos_token = False use_default_system_prompt = False legacy = None add_prefix_space = None **kwargs )
Parameters
- vocab_file (
str, optional) —SentencePiece file (generally has a .model extension) that contains the vocabulary necessary to instantiate a tokenizer. - 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"<s>") — The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. - eos_token (
strortokenizers.AddedToken, optional, defaults to"</s>") — 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 Llama should be used - legacy (
bool, optional) — Whether or not thelegacybehavior of the tokenizer should be used. Legacy is before the merge of #24622 and #25224 which includes fixes to properly handle tokens that appear after special tokens. Make sure to also setfrom_slowtoTrue. A simple example:legacy=True:
Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.
This uses notably ByteFallback and no normalization.
from transformers import LlamaTokenizerFast
tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer") tokenizer.encode("Hello this is a test") [1, 15043, 445, 338, 263, 1243]
If you want to change the bos_token or the eos_token, make sure to specify them when initializing the model, or call tokenizer.update_post_processor() to make sure that the post-processing is correctly done (otherwise the values of the first token and final token of an encoded sequence will not be correct). For more details, checkout [post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.
This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
build_inputs_with_special_tokens
( token_ids_0 token_ids_1 = None )
get_special_tokens_mask
( 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
( 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.
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.
Updates the underlying post processor with the current bos_token and eos_token.
save_vocabulary
( save_directory: str filename_prefix: typing.Optional[str] = None )
LlamaModel
class transformers.LlamaModel
( config: LlamaConfig )
Parameters
- config (LlamaConfig) — 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 thefrom_pretrained() method to load the model weights.
- config — LlamaConfig
The bare LLaMA 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.
Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a LlamaDecoderLayer
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 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 **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() andPreTrainedTokenizer.call() for details.
What are input IDs? - attention_mask (
torch.Tensorof shape(batch_size, sequence_length) orBlockMask, *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.
If the model is configured to use flex_attention, it will attempt to convert the mask Tensor into a BlockMask, but you can also pass aBlockMaskobject directly here.
What are attention masks?
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
Ifpast_key_valuesis used, optionally only the lastinput_idshave to be input (seepast_key_values).
If you want to change padding behavior, you should readmodeling_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].
What are position IDs? - past_key_values (
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 thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=Trueorconfig.use_cache=True.
It is a Cache instance. For more details, see our kv cache guide.
Ifpast_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. - cache_position (
torch.LongTensorof shape(sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily toposition_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.
The LlamaModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
LlamaForCausalLM
class transformers.LlamaForCausalLM
( config )
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.models.llama.modeling_llama.KwargsForCausalLM] ) → 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() andPreTrainedTokenizer.call() for details.
What are input IDs? - attention_mask (
torch.Tensorof shape(batch_size, sequence_length) orBlockMask, *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.
If the model is configured to use flex_attention, it will attempt to convert the mask Tensor into a BlockMask, but you can also pass aBlockMaskobject directly here.
What are attention masks?
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
Ifpast_key_valuesis used, optionally only the lastinput_idshave to be input (seepast_key_values).
If you want to change padding behavior, you should readmodeling_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].
What are position IDs? - past_key_values (
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 thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=Trueorconfig.use_cache=True.
It is a Cache instance. For more details, see our kv cache guide.
Ifpast_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. - cache_position (
torch.LongTensorof shape(sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily toposition_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. - 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]. - logits_to_keep (
intortorch.Tensor, optional) — If anint, compute logits for the lastlogits_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. If atorch.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).
A transformers.modeling_outputs.CausalLMOutputWithPast or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (LlamaConfig) 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 (seepast_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 LlamaForCausalLM forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance 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, LlamaForCausalLM
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
prompt = "Hey, are you conscious? Can you talk to me?" inputs = tokenizer(prompt, return_tensors="pt")
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."
LlamaForSequenceClassification
class transformers.LlamaForSequenceClassification
( config )
Parameters
- config (LlamaConfig) — 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 thefrom_pretrained() method to load the model weights.
The LLaMa Model transformer with a sequence classification head on top (linear layer).
LlamaForSequenceClassification uses the last token in order to do the classification, as other causal models (e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If apad_token_id is defined in the configuration, it finds the last token that is not a padding token in each row. If no pad_token_id is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when inputs_embeds are passed instead of input_ids, it does the same (take the last value in each row of the batch).
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 )
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() andPreTrainedTokenizer.call() for details.
What are input IDs? - attention_mask (
torch.Tensorof shape(batch_size, sequence_length) orBlockMask, *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.
If the model is configured to use flex_attention, it will attempt to convert the mask Tensor into a BlockMask, but you can also pass aBlockMaskobject directly here.
What are attention masks?
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
Ifpast_key_valuesis used, optionally only the lastinput_idshave to be input (seepast_key_values).
If you want to change padding behavior, you should readmodeling_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].
What are position IDs? - past_key_values (
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 thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=Trueorconfig.use_cache=True.
It is a Cache instance. For more details, see our kv cache guide.
Ifpast_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. - cache_position (
torch.LongTensorof shape(sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily toposition_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. - labels (
torch.LongTensorof shape(batch_size,), optional) — Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]. Ifconfig.num_labels == 1a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1a classification loss is computed (Cross-Entropy).
The LlamaForSequenceClassification forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
LlamaForQuestionAnswering
class transformers.LlamaForQuestionAnswering
( config )
Parameters
- config (LlamaConfig) — 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 thefrom_pretrained() method to load the model weights.
The Llama Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).
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 start_positions: typing.Optional[torch.LongTensor] = None end_positions: typing.Optional[torch.LongTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None **kwargs )
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() andPreTrainedTokenizer.call() for details.
What are input IDs? - attention_mask (
torch.Tensorof shape(batch_size, sequence_length) orBlockMask, *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.
If the model is configured to use flex_attention, it will attempt to convert the mask Tensor into a BlockMask, but you can also pass aBlockMaskobject directly here.
What are attention masks?
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
Ifpast_key_valuesis used, optionally only the lastinput_idshave to be input (seepast_key_values).
If you want to change padding behavior, you should readmodeling_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].
What are position IDs? - past_key_values (
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 thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=Trueorconfig.use_cache=True.
It is a Cache instance. For more details, see our kv cache guide.
Ifpast_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. - cache_position (
torch.LongTensorof shape(sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily toposition_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. - start_positions (
torch.LongTensorof shape(batch_size,), optional) — Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss. - end_positions (
torch.LongTensorof shape(batch_size,), optional) — Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.
The LlamaForQuestionAnswering forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
LlamaForTokenClassification
class transformers.LlamaForTokenClassification
( config )
Parameters
- config (LlamaConfig) — 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 thefrom_pretrained() method to load the model weights.
The Llama Model transformer with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
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 ) → transformers.modeling_outputs.TokenClassifierOutput 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() andPreTrainedTokenizer.call() for details.
What are input IDs? - attention_mask (
torch.Tensorof shape(batch_size, sequence_length) orBlockMask, *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.
If the model is configured to use flex_attention, it will attempt to convert the mask Tensor into a BlockMask, but you can also pass aBlockMaskobject directly here.
What are attention masks?
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
Ifpast_key_valuesis used, optionally only the lastinput_idshave to be input (seepast_key_values).
If you want to change padding behavior, you should readmodeling_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].
What are position IDs? - past_key_values (
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 thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=Trueorconfig.use_cache=True.
It is a Cache instance. For more details, see our kv cache guide.
Ifpast_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. - cache_position (
torch.LongTensorof shape(sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily toposition_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. - labels (
torch.LongTensorof shape(batch_size,), optional) — Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]. Ifconfig.num_labels == 1a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1a classification loss is computed (Cross-Entropy).
A transformers.modeling_outputs.TokenClassifierOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (LlamaConfig) and inputs.
- loss (
torch.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Classification loss. - logits (
torch.FloatTensorof shape(batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax). - 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 LlamaForTokenClassification forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance 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, LlamaForTokenClassification import torch
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf") model = LlamaForTokenClassification.from_pretrained("meta-llama/Llama-2-7b-hf")
inputs = tokenizer( ... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt" ... )
with torch.no_grad(): ... logits = model(**inputs).logits
predicted_token_class_ids = logits.argmax(-1)
predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
labels = predicted_token_class_ids loss = model(**inputs, labels=labels).loss
FlaxLlamaModel
class transformers.FlaxLlamaModel
( config: LlamaConfig input_shape: typing.Tuple = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs )
Parameters
- config (LlamaConfig) — 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.
- dtype (
jax.numpy.dtype, optional, defaults tojax.numpy.float32) — The data type of the computation. Can be one ofjax.numpy.float32,jax.numpy.float16, orjax.numpy.bfloat16.
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the givendtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.
If you wish to change the dtype of the model parameters, see to_fp16() andto_bf16().
The bare Llama Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from FlaxPreTrainedModel. 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 Flax Linenflax.nn.Module subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
__call__
( input_ids attention_mask = None position_ids = None params: typing.Optional[dict] = None past_key_values: typing.Optional[dict] = None dropout_rng: <function PRNGKey at 0x7f3493177760> = None train: bool = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
numpy.ndarrayof shape(batch_size, input_ids_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() andPreTrainedTokenizer.call() for details.
What are input IDs? - attention_mask (
numpy.ndarrayof 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() andPreTrainedTokenizer.call() for details.
Ifpast_key_valuesis used, optionally only the lastdecoder_input_idshave to be input (seepast_key_values).
If you want to change padding behavior, you should readmodeling_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 (
numpy.ndarrayof shape(batch_size, input_ids_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 (
Dict[str, np.ndarray], optional, returned byinit_cacheor when passing previouspast_key_values) — Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length]. - 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.
A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (LlamaConfig) and inputs.
- last_hidden_state (
jnp.ndarrayof shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model. - hidden_states (
tuple(jnp.ndarray), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple ofjnp.ndarray(one for the output of the embeddings + 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 initial embedding outputs. - attentions (
tuple(jnp.ndarray), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple ofjnp.ndarray(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 FlaxLlamaPreTrainedModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
This example uses a random model as the real ones are all very big. To get proper results, you should use openlm-research/open_llama_3b_v2 instead of afmck/testing-llama-tiny. If you get out-of-memory when loading that checkpoint, you can try adding device_map="auto" in the from_pretrained call.
Example:
from transformers import AutoTokenizer, FlaxLlamaModel
tokenizer = AutoTokenizer.from_pretrained("afmck/testing-llama-tiny") model = FlaxLlamaModel.from_pretrained("afmck/testing-llama-tiny")
inputs = tokenizer("Hello, my dog is cute", return_tensors="jax") outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
FlaxLlamaForCausalLM
class transformers.FlaxLlamaForCausalLM
( config: LlamaConfig input_shape: typing.Tuple = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs )
Parameters
- config (LlamaConfig) — 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.
- dtype (
jax.numpy.dtype, optional, defaults tojax.numpy.float32) — The data type of the computation. Can be one ofjax.numpy.float32,jax.numpy.float16, orjax.numpy.bfloat16.
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the givendtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.
If you wish to change the dtype of the model parameters, see to_fp16() andto_bf16().
The Llama Model transformer with a language modeling head (linear layer) on top.
This model inherits from FlaxPreTrainedModel. 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 Flax Linenflax.nn.Module subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
__call__
( input_ids attention_mask = None position_ids = None params: typing.Optional[dict] = None past_key_values: typing.Optional[dict] = None dropout_rng: <function PRNGKey at 0x7f3493177760> = None train: bool = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
numpy.ndarrayof shape(batch_size, input_ids_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() andPreTrainedTokenizer.call() for details.
What are input IDs? - attention_mask (
numpy.ndarrayof 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() andPreTrainedTokenizer.call() for details.
Ifpast_key_valuesis used, optionally only the lastdecoder_input_idshave to be input (seepast_key_values).
If you want to change padding behavior, you should readmodeling_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 (
numpy.ndarrayof shape(batch_size, input_ids_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 (
Dict[str, np.ndarray], optional, returned byinit_cacheor when passing previouspast_key_values) — Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length]. - 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.
A transformers.modeling_flax_outputs.FlaxMaskedLMOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (LlamaConfig) and inputs.
- logits (
jnp.ndarrayof shape(batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). - hidden_states (
tuple(jnp.ndarray), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple ofjnp.ndarray(one for the output of the embeddings + 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 initial embedding outputs. - attentions (
tuple(jnp.ndarray), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple ofjnp.ndarray(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 FlaxLlamaPreTrainedModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
This example uses a random model as the real ones are all very big. To get proper results, you should use openlm-research/open_llama_3b_v2 instead of afmck/testing-llama-tiny. If you get out-of-memory when loading that checkpoint, you can try adding device_map="auto" in the from_pretrained call.
Example:
from transformers import AutoTokenizer, FlaxLlamaForCausalLM
tokenizer = AutoTokenizer.from_pretrained("afmck/testing-llama-tiny") model = FlaxLlamaForCausalLM.from_pretrained("afmck/testing-llama-tiny")
inputs = tokenizer("Hello, my dog is cute", return_tensors="np") outputs = model(**inputs)
next_token_logits = outputs.logits[:, -1]