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NemotronHConfig

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This model was released on 2025-12-15 and added to Hugging Face Transformers on 2026-03-02.

NemotronHConfig

class transformers.NemotronHConfig

< >

( output_hidden_states: bool | None = False return_dict: bool | None = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False id2label: dict[int, str] | dict[str, str] | None = None label2id: dict[str, int] | dict[str, str] | None = None problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None tokenizer_class: str | None = None vocab_size: int = 131072 hidden_size: int = 4096 layers_block_type: list[str] | None = None tie_word_embeddings: bool = False use_cache: bool = True num_logits_to_keep: int = 1 pad_token_id: int | None = 0 bos_token_id: int | None = 1 eos_token_id: int | None = 2 num_attention_heads: int = 32 num_key_value_heads: int = 8 head_dim: int = 128 max_position_embeddings: int = 4096 attention_bias: bool = False attention_dropout: float = 0.0 sliding_window: int | None = None intermediate_size: int = 21504 mlp_hidden_act: str = 'relu2' mlp_bias: bool = False use_mamba_kernels: bool = True ssm_state_size: int = 128 mamba_num_heads: int = 128 mamba_head_dim: int = 64 mamba_hidden_act: str = 'silu' n_groups: int = 8 conv_kernel: int = 4 expand: int = 2 time_step_min: float = 0.001 time_step_max: float = 0.1 time_step_limit: list[float] | tuple[float, ...] = (0.0, inf) time_step_floor: float = 0.0001 use_conv_bias: bool = True chunk_size: int = 128 mamba_proj_bias: bool = False mamba_ssm_cache_dtype: str = 'float32' n_routed_experts: int = 8 n_shared_experts: int = 1 moe_intermediate_size: int = 7688 moe_shared_expert_intermediate_size: int = 7688 moe_latent_size: int | None = None moe_shared_expert_overlap: bool = True num_experts_per_tok: int = 2 routed_scaling_factor: float | int = 1.0 n_group: int = 1 topk_group: int = 1 norm_topk_prob: bool = True num_nextn_predict_layers: int = 0 mtp_layers_block_type: list[str] | None = None use_bias: bool = False initializer_range: float = 0.02 layer_norm_epsilon: float = 1e-05 residual_in_fp32: bool = False hidden_dropout: float | int = 0.0 rescale_prenorm_residual: bool = True )

Parameters

  • output_hidden_states (bool, optional, defaults to False) — Whether or not the model should return all hidden-states.
  • return_dict (bool, optional, defaults to True) — Whether to return a ModelOutput (dataclass) instead of a plain tuple.
  • dtype (Union[str, torch.dtype], optional) — The chunk size of all feed forward layers in the residual attention blocks. A chunk size of 0 means that the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes n < sequence_length embeddings at a time. For more information on feed forward chunking, see How does Feed Forward Chunking work?.
  • chunk_size_feed_forward (int, optional, defaults to 0) — The dtype of the weights. This attribute can be used to initialize the model to a non-default dtype (which is normally float32) and thus allow for optimal storage allocation. For example, if the saved model is float16, ideally we want to load it back using the minimal amount of memory needed to load float16 weights.
  • is_encoder_decoder (bool, optional, defaults to False) — Whether the model is used as an encoder/decoder or not.
  • id2label (Union[dict[int, str], dict[str, str]], optional) — A map from index (for instance prediction index, or target index) to label.
  • label2id (Union[dict[str, int], dict[str, str]], optional) — A map from label to index for the model.
  • problem_type (Literal[regression, single_label_classification, multi_label_classification], optional) — Problem type for XxxForSequenceClassification models. Can be one of "regression", "single_label_classification" or "multi_label_classification".
  • tokenizer_class (str, optional) — The class name of model’s tokenizer.
  • vocab_size (int, optional, defaults to 131072) — 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 4096) — Dimension of the hidden representations.
  • layers_block_type (list, optional) — Explicit list of layer types for each layer. Each element must be one of: “mamba”, “attention”, or “moe”. The number of layers is determined by the length of this list.
  • tie_word_embeddings (bool, optional, defaults to False) — Whether to tie weight embeddings according to model’s tied_weights_keys mapping.
  • 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.
  • num_logits_to_keep (int, optional, defaults to 1) — Number of prompt logits to calculate during generation. If None, all logits will be calculated.
  • pad_token_id (int, optional, defaults to 0) — Token id used for padding in the vocabulary.
  • bos_token_id (int, optional, defaults to 1) — Token id used for beginning-of-stream in the vocabulary.
  • eos_token_id (int, optional, defaults to 2) — Token id used for end-of-stream in the vocabulary.
  • 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, defaults to 8) — This is the number of key_value heads that should be used to implement Grouped Query Attention. If num_key_value_heads=num_attention_heads, the model will use Multi Head Attention (MHA), if num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out this paper. If it is not specified, will default to num_attention_heads.
  • head_dim (int, optional, defaults to 128) — The attention head dimension. If None, it will default to hidden_size // num_attention_heads
  • max_position_embeddings (int, optional, defaults to 4096) — The maximum sequence length that this model might ever be used with.
  • attention_bias (bool, optional, defaults to False) — Whether to use a bias in the query, key, value and output projection layers during self-attention.
  • attention_dropout (float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
  • sliding_window (int, optional) — Sliding window attention window size. If None, no sliding window is applied.
  • intermediate_size (int, optional, defaults to 21504) — Dimension of the MLP representations.
  • mlp_hidden_act (str, optional, defaults to relu2) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
  • mlp_bias (bool, optional, defaults to False) — Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
  • use_mamba_kernels (bool, optional, defaults to True) — Flag indicating whether or not to use the fast mamba kernels.
  • ssm_state_size (int, optional, defaults to 128) — The dimension of the mamba state space latents.
  • mamba_num_heads (int, optional, defaults to 128) — The number of mamba heads used in the v2 implementation.
  • mamba_head_dim (int, optional, defaults to 64) — Head embedding dimension size
  • mamba_hidden_act (str, optional, defaults to "silu") — The non-linear activation function in the Mamba layers.
  • conv_kernel (int, optional, defaults to 4) — The size of the convolutional kernel.
  • 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_limit (Union[list[float], tuple[float, ...]], optional, defaults to (0.0, inf)) — Accepted range of time step values for clamping.
  • time_step_floor (float, optional, defaults to 0.0001) — Minimum allowed value for the discrete time step delta after softplus activation.
  • mamba_proj_bias (bool, optional, defaults to False) — Flag indicating whether or not to use bias in the input and output projections ([“in_proj”, “out_proj”]) of the mamba mixer block
  • mamba_ssm_cache_dtype (str, optional, defaults to "float32") — Data type for Mamba SSM cache states.
  • n_routed_experts (int, optional, defaults to 8) — Number of routed experts.
  • n_shared_experts (int, optional, defaults to 1) — Number of shared experts.
  • moe_intermediate_size (int, optional, defaults to 7688) — Intermediate size of the routed expert MLPs.
  • moe_shared_expert_intermediate_size (int, optional, defaults to 7688) — Dimension of the MLP representations in shared experts.
  • moe_latent_size (int, optional) — Latent size for MoE expert projections. If None, uses hidden_size.
  • moe_shared_expert_overlap (bool, optional, defaults to True) — Whether shared experts overlap with routed experts.
  • num_experts_per_tok (int, optional, defaults to 2) — Number of experts to route each token to. This is the top-k value for the token-choice routing.
  • routed_scaling_factor (Union[float, int], optional, defaults to 1.0) — Scaling factor or routed experts.
  • n_group (int, optional, defaults to 1) — Number of groups for expert routing.
  • topk_group (int, optional, defaults to 1) — Number of selected groups for each token (for each token, ensuring the selected experts is only within topk_group groups).
  • norm_topk_prob (bool, optional, defaults to True) — Whether to normalize the weights of the routed experts.
  • num_nextn_predict_layers (int, optional, defaults to 0) — Number of additional layers for multi-token prediction. If 0, multi-token prediction is disabled.
  • mtp_layers_block_type (list, optional, defaults to ['attention', 'moe']) — Explicit list of layer types for multi-token prediction layers when num_nextn_predict_layers > 0.
  • use_bias (bool, optional, defaults to False) — Whether to use bias in the model.
  • initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
  • layer_norm_epsilon (float, optional, defaults to 1e-05) — The epsilon used by the layer normalization layers.
  • residual_in_fp32 (bool, optional, defaults to False) — Whether or not residuals should be in float32.
  • hidden_dropout (Union[float, int], optional, defaults to 0.0) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
  • rescale_prenorm_residual (bool, optional, defaults to True) — Whether to rescale the pre-normalization residual connections.
  • ```python —

    from transformers import NemotronHModel, NemotronHConfig

This is the configuration class to store the configuration of a NemotronHModel. It is used to instantiate a Nemotron H 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 nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16

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

validate_layers_block_type

< >

( )

Validate layers_block_type list.

NemotronHForCausalLM

class transformers.NemotronHForCausalLM

< >

( config )

forward

< >

( input_ids: torch.LongTensor | None = None attention_mask: torch.Tensor | None = None position_ids: torch.LongTensor | None = None past_key_values: transformers.models.nemotron_h.modeling_nemotron_h.NemotronHHybridDynamicCache | None = None inputs_embeds: torch.FloatTensor | None = None labels: torch.LongTensor | None = None use_cache: bool | None = None logits_to_keep: int | torch.Tensor = 0 **kwargs ) → CausalLMOutputWithPast or tuple(torch.FloatTensor)

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

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

    What are input IDs?

  • attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,
    • 0 for tokens that are masked.

    What are attention masks?

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].

    What are position IDs?

  • past_key_values (~models.nemotron_h.modeling_nemotron_h.NemotronHHybridDynamicCache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].
  • use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).
  • 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).

Returns

CausalLMOutputWithPast or tuple(torch.FloatTensor)

A 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 (NemotronHConfig) and inputs.

The NemotronHForCausalLM 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).

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

    Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> from transformers import AutoTokenizer, NemotronHForCausalLM

>>> model = NemotronHForCausalLM.from_pretrained("Zyphra/NemotronH-7B-v1")
>>> tokenizer = AutoTokenizer.from_pretrained("Zyphra/NemotronH-7B-v1")

>>> 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."

NemotronHModel

class transformers.NemotronHModel

< >

( config )

forward

< >

( input_ids: torch.LongTensor | None = None inputs_embeds: torch.LongTensor | None = None position_ids: torch.LongTensor | None = None past_key_values: transformers.models.nemotron_h.modeling_nemotron_h.NemotronHHybridDynamicCache | None = None use_cache: bool | None = None attention_mask: torch.Tensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] )

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