## NemotronHConfig[[transformers.NemotronHConfig]] #### transformers.NemotronHConfig[[transformers.NemotronHConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/nemotron_h/configuration_nemotron_h.py#L23) This is the configuration class to store the configuration of a [NemotronHModel](/docs/transformers/v5.3.0/en/model_doc/nemotron_h#transformers.NemotronHModel). It is used to instantiate a NemotronH model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16). Configuration objects inherit from [PretrainedConfig](/docs/transformers/v5.3.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](/docs/transformers/v5.3.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. ```python >>> from transformers import NemotronHModel, NemotronHConfig >>> # Initializing a NemotronH configuration >>> configuration = NemotronHConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = NemotronHModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` **Parameters:** vocab_size (`int`, *optional*, defaults to 131072) : Vocabulary size of the NemotronH model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [NemotronHModel](/docs/transformers/v5.3.0/en/model_doc/nemotron_h#transformers.NemotronHModel). 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. num_hidden_layers (`int`, *optional*) : Number of hidden layers in the Transformer encoder. This parameter is deprecated and only kept for backward compatibility. The number of layers is now determined by the length of `layers_block_type`. tie_word_embeddings (`bool`, *optional*, defaults to `False`) : Whether the model's input and output word embeddings should be tied. use_cache (`bool`, *optional*, defaults to `True`) : Whether or not the model should return the last key/values attentions. 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) : The id of the padding token. bos_token_id (`int`, *optional*, defaults to 1) : The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 2) : The id of the "end-of-sequence" token. num_attention_heads (`int`, *optional*, defaults to 32) : Number of attention heads for each attention layer in the Transformer encoder. 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. head_dim (`int`, *optional*, defaults to 128) : Dimension of each attention head. 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 bias in attention layers. attention_dropout (`float`, *optional*, defaults to 0.0) : The dropout ratio for the attention probabilities. sliding_window (`int`, *optional*) : Sliding window attention window size. 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 in the MLP layers. mlp_bias (`bool`, *optional*, defaults to `False`) : Whether to use bias in 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) : Number of heads in Mamba layers. mamba_n_groups (`int`, *optional*, defaults to 8) : Number of groups in Mamba layers. mamba_head_dim (`int`, *optional*, defaults to 64) : Dimension of each Mamba head. mamba_d_conv (`int`, *optional*, defaults to 4) : The size of the mamba convolution kernel. mamba_expand (`int`, *optional*, defaults to 2) : Expanding factor used to determine the mamba intermediate size. mamba_hidden_act (`str`, *optional*, defaults to `"silu"`) : The non-linear activation function in the Mamba layers. mamba_dt_min (`float`, *optional*, defaults to 0.001) : Minimum value for the time step in Mamba. mamba_dt_max (`float`, *optional*, defaults to 0.1) : Maximum value for the time step in Mamba. mamba_dt_limit (`tuple`, *optional*, defaults to `(0.0, inf)`) : Limits for the time step in Mamba. mamba_dt_init_floor (`float`, *optional*, defaults to 0.0001) : Floor value for time step initialization in Mamba. mamba_conv_bias (`bool`, *optional*, defaults to `True`) : Whether to use bias in the convolution layer of the mamba mixer block. mamba_proj_bias (`bool`, *optional*, defaults to `False`) : Whether to use bias in the input and output projections of the mamba mixer block. mamba_chunk_size (`int`, *optional*, defaults to 128) : Size of chunks for Mamba processing. 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 in MoE layers. n_shared_experts (`int`, *optional*, defaults to 1) : Number of shared experts that are always activated in MoE layers. moe_intermediate_size (`int`, *optional*, defaults to 7688) : Dimension of the MLP representations in routed experts. 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) : The number of experts to route per token (top-k routing parameter). routed_scaling_factor (`float`, *optional*, defaults to 1.0) : Scaling factor applied to routed expert outputs. n_group (`int`, *optional*, defaults to 1) : Number of groups for expert routing. topk_group (`int`, *optional*, defaults to 1) : Top-k group parameter for expert selection. norm_topk_prob (`bool`, *optional*, defaults to `True`) : Whether to normalize top-k probabilities in expert routing. 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 (`float`, *optional*, defaults to 0.0) : The dropout ratio for the hidden states. rescale_prenorm_residual (`bool`, *optional*, defaults to `True`) : Whether to rescale the pre-normalization residual connections. ## NemotronHForCausalLM[[transformers.NemotronHForCausalLM]] #### transformers.NemotronHForCausalLM[[transformers.NemotronHForCausalLM]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/nemotron_h/modeling_nemotron_h.py#L1270) forwardtransformers.NemotronHForCausalLM.forwardhttps://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/nemotron_h/modeling_nemotron_h.py#L1282[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.models.nemotron_h.modeling_nemotron_h.NemotronHHybridDynamicCache | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "cache_position", "val": ": torch.LongTensor | 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](/docs/transformers/v5.3.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.3.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.3.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#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?](../glossary#attention-mask) - **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?](../glossary#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](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.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`). - **cache_position** (`torch.LongTensor` of shape `(sequence_length)`, *optional*) -- Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. - **logits_to_keep** (`Union[int, torch.Tensor]`, *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).0[CausalLMOutputWithPast](/docs/transformers/v5.3.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or `tuple(torch.FloatTensor)`A [CausalLMOutputWithPast](/docs/transformers/v5.3.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([NemotronHConfig](/docs/transformers/v5.3.0/en/model_doc/nemotron_h#transformers.NemotronHConfig)) and inputs. The [NemotronHForCausalLM](/docs/transformers/v5.3.0/en/model_doc/nemotron_h#transformers.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](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). 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: ```python >>> 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." ``` **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](/docs/transformers/v5.3.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.3.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.3.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#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?](../glossary#attention-mask) 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?](../glossary#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](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.3.0/en/internal/generation_utils#transformers.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`). cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*) : Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. logits_to_keep (`Union[int, torch.Tensor]`, *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](/docs/transformers/v5.3.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or `tuple(torch.FloatTensor)`` A [CausalLMOutputWithPast](/docs/transformers/v5.3.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([NemotronHConfig](/docs/transformers/v5.3.0/en/model_doc/nemotron_h#transformers.NemotronHConfig)) and inputs. ## NemotronHModel[[transformers.NemotronHModel]] #### transformers.NemotronHModel[[transformers.NemotronHModel]] [Source](https://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/nemotron_h/modeling_nemotron_h.py#L1161) forwardtransformers.NemotronHModel.forwardhttps://github.com/huggingface/transformers/blob/v5.3.0/src/transformers/models/nemotron_h/modeling_nemotron_h.py#L1178[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.LongTensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.models.nemotron_h.modeling_nemotron_h.NemotronHHybridDynamicCache | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "cache_position", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]