transformers/pr_41992/en/model_doc/mamba.md

Mamba

Mamba is a selective structured state space model (SSMs) designed to work around Transformers computational inefficiency when dealing with long sequences. It is a completely attention-free architecture, and comprised of a combination of H3 and gated MLP blocks (Mamba block). Mamba's "content-based reasoning" allows it to focus on specific parts of an input depending on the current token. Mamba also uses a new hardware-aware parallel algorithm to compensate for the lack of convolutional operations. As a result, Mamba has fast inference and can scale to very long sequences.

You can find all the original Mamba checkpoints under the State Space Models organization.

This model was contributed by Molbap and AntonV. Click on the Mamba models in the right sidebar for more examples of how to apply Mamba to different language tasks.

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

from transformers import pipeline

pipeline = pipeline(
    task="text-generation",
    model="state-spaces/mamba-130m-hf",
    device=0
)
pipeline("Plants create energy through a process known as")

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf")
model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-130m-hf", device_map="auto")
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)

output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))

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 4-bit integers.

from torchao.quantization import Int4WeightOnlyConfig

from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig

quantization_config = Int4WeightOnlyConfig(group_size=128)
quantization_config = TorchAoConfig(quant_type=quant_config)
tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-2.8b-hf")
model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-2.8b-hf", quantization_config=quantization_config, device_map="auto")
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)

output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Notes

• The current implementation uses the original CUDA kernels. The FlashAttention equivalent implementation is hosted in the mamba-ssm and causal_conv1d repositories. Make sure to install them if your hardware supports it!

• Mamba stacks mixer layers which are equivalent to Attention layers. You can find the main logic of Mamba in the MambaMixer class.

• The example below demonstrates how to fine-tune Mamba with PEFT.

  from datasets import load_dataset
  from trl import SFTConfig, SFTTrainer
  from peft import LoraConfig
  
  model_id = "state-spaces/mamba-130m-hf"
  dataset = load_dataset("Abirate/english_quotes", split="train")
  training_args = SFTConfig(dataset_text_field="quote")
  lora_config =  LoraConfig(target_modules=["x_proj", "embeddings", "in_proj", "out_proj"])
  trainer = SFTTrainer(
      model=model_id,
      args=training_args,
      train_dataset=dataset,
      peft_config=lora_config,
  )
  trainer.train()
  

MambaConfig[[transformers.MambaConfig]]

transformers.MambaConfig[[transformers.MambaConfig]]

Source

This is the configuration class to store the configuration of a MambaModel. It is used to instantiate a Mamba 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 state-spaces/mamba-2.8b

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

Example:

>>> from transformers import MambaConfig, MambaModel

>>> # Initializing a Mamba configuration
>>> configuration = MambaConfig()

>>> # Initializing a model (with random weights) from the configuration
>>> model = MambaModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

Parameters:

vocab_size (int, optional, defaults to 50280) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the input_ids.

hidden_size (int, optional, defaults to 768) : Dimension of the hidden representations.

state_size (int, optional, defaults to 16) : Size of the SSM state (latent state dimension) in the Mamba layers.

num_hidden_layers (int, optional, defaults to 32) : Number of hidden layers in the Transformer decoder.

layer_norm_epsilon (float, optional, defaults to 1e-05) : The epsilon to use in the layer normalization layers.

pad_token_id (int, optional, defaults to 0) : Token id used for padding in the vocabulary.

bos_token_id (int, optional, defaults to 0) : Token id used for beginning-of-stream in the vocabulary.

eos_token_id (Union[int, list[int]], optional, defaults to 0) : Token id used for end-of-stream in the vocabulary.

expand (int, optional, defaults to 2) : Expanding factor used to determine the intermediate size.

conv_kernel (int, optional, defaults to 4) : The size of the convolutional kernel.

use_bias (bool, optional, defaults to False) : Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block

use_conv_bias (bool, optional, defaults to True) : Whether or not to use bias in the convolution layer of the mixer block.

hidden_act (str, optional, defaults to silu) : The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.

initializer_range (float, optional, defaults to 0.1) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

residual_in_fp32 (bool, optional, defaults to True) : Whether or not residuals should be in float32. If set to False residuals will keep the same dtype as the rest of the model

time_step_rank (Union[str, int], optional, defaults to auto) : Rank of the delta (time step) projection. Can be "auto" to set it automatically.

time_step_scale (float, optional, defaults to 1.0) : Scale applied to the time step delta before discretization.

time_step_min (float, optional, defaults to 0.001) : Minimum time_step used to bound dt_proj.bias.

time_step_max (float, optional, defaults to 0.1) : Maximum time_step used to bound dt_proj.bias.

time_step_init_scheme (str, optional, defaults to random) : Initialization scheme for the time step delta. Can be "random" or "uniform".

time_step_floor (float, optional, defaults to 0.0001) : Minimum allowed value for the discrete time step delta after softplus activation.

rescale_prenorm_residual (bool, optional, defaults to False) : Whether or not to rescale out_proj weights when initializing.

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 or when the model is a decoder-only generative model.

use_mambapy (bool, optional, defaults to False) : Determines the fallback strategy during training if the CUDA-based official implementation of Mamba is not available. If True, the mamba.py implementation is used. If False, the naive and slower implementation is used. Consider switching to the naive version if memory is limited.

use_associative_scan (bool, optional, defaults to True) : Whether to use PyTorch's torch._higher_order_ops.associative_scan for the parallel scan instead of the naive sequential implementation. The associative scan is only active during torch.compile tracing and requires torch >= 2.9.0. Both paths are tested to produce numerically identical results (see test_associative_scan_matches_sequential). Set to False to fall back to the sequential loop.

tie_word_embeddings (bool, optional, defaults to True) : Whether to tie weight embeddings according to model's tied_weights_keys mapping.

MambaModel[[transformers.MambaModel]]

transformers.MambaModel[[transformers.MambaModel]]

Source

The bare Mamba 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.

forwardtransformers.MambaModel.forwardhttps://github.com/huggingface/transformers/blob/vr_41992/src/transformers/models/mamba/modeling_mamba.py#L548[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.LongTensor | None = None"}, {"name": "cache_params", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "**kwargs", "val": ""}]- 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?

• inputs_embeds (torch.LongTensor 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_params (Cache, optional) -- If passed along, the model uses the previous state in all the blocks (which will give the output for the input_ids provided as if the model add state_input_ids + input_ids as context).

• use_cache (bool, optional) -- If set to True, the cache_params is returned and can be used to quickly generate the next logits.

• output_hidden_states (bool, optional) -- Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

• return_dict (bool, optional) -- Whether or not to return a ModelOutput instead of a plain tuple.

• attention_mask (torch.LongTensor 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?0MambaOutput or tuple(torch.FloatTensor)A MambaOutput 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 (MambaConfig) and inputs. The MambaModel 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.

• last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) -- Sequence of hidden-states at the output of the last layer of the model.

• cache_params (~cache_utils.Cache, optional) -- The state of the model at the last time step. Can be used in a forward method with the next input_ids to avoid providing the old input_ids.

  Includes both the State space model state matrices after the selective scan, and the Convolutional states

• 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.

Parameters:

config (MambaModel) : 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.

Returns:

MambaOutput` or `tuple(torch.FloatTensor)

A MambaOutput 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 (MambaConfig) and inputs.

MambaLMHeadModel[[transformers.MambaForCausalLM]]

transformers.MambaForCausalLM[[transformers.MambaForCausalLM]]

Source

The MAMBA Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).

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.

forwardtransformers.MambaForCausalLM.forwardhttps://github.com/huggingface/transformers/blob/vr_41992/src/transformers/models/mamba/modeling_mamba.py#L659[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "cache_params", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "logits_to_keep", "val": ": int | torch.Tensor = 0"}, {"name": "**kwargs", "val": ""}]- 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.LongTensor 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?

• 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_params (Cache, optional) -- If passed along, the model uses the previous state in all the blocks (which will give the output for the input_ids provided as if the model add state_input_ids + input_ids as context).

• labels (torch.LongTensor of shape (batch_size, sequence_length), optional) -- Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set labels = input_ids Indices are selected in [-100, 0, ..., config.vocab_size] All labels set to -100 are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size]

• output_hidden_states (bool, optional) -- Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

• return_dict (bool, optional) -- Whether or not to return a ModelOutput instead of a plain tuple.

• use_cache (bool, optional) -- If set to True, the cache_params is returned and can be used to quickly generate the next logits.

• logits_to_keep (Union[int, torch.Tensor], optional, 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).0MambaCausalLMOutput or tuple(torch.FloatTensor)A MambaCausalLMOutput 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 (MambaConfig) and inputs. The MambaForCausalLM 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.

• 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).

• cache_params (~cache_utils.Cache, optional) -- The state of the model at the last time step. Can be used in a forward method with the next input_ids to avoid providing the old input_ids.

  Includes both the State space model state matrices after the selective scan, and the Convolutional states

• 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.

Example:

Parameters:

config (MambaForCausalLM) : 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.

Returns:

MambaCausalLMOutput` or `tuple(torch.FloatTensor)

A MambaCausalLMOutput 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 (MambaConfig) and inputs.