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HRM-Text

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This model was released on 2025-06-26 and added to Hugging Face Transformers on 2026-05-18.

HRM-Text

Overview

HRM-Text is the improved autoregressive language-modeling variant of the Hierarchical Reasoning Model (HRM, Hierarchical Reasoning Model) by the Sapient AI team. It is a base model that uses a hierarchical recurrent forward — two transformer stacks (H for slow, abstract planning, and L for fast, detailed computation) are reused inside a nested recurrence:

for h in range(H_cycles):
    for l in range(L_cycles):
        z_L = L(z_L + z_H)
    z_H = H(z_H + z_L)

Architectural traits:

PrefixLM attention: instruction tokens attend bidirectionally, response tokens attend causally. Controlled by config.prefix_lm (default True); see 4D-masks blog / FlexAttention blog for the canonical form.
Per-head sigmoid output gate applied to the attention output before o_proj (Qwen3-Next-style; see Qwen3NextAttention). Legacy checkpoints stored as a single fused gqkv_proj are split into gate_proj / q_proj / k_proj / v_proj at load time by the registered HRM-Text checkpoint conversion mapping.
Parameterless RMSNorm — F.rms_norm with no learnable scale.
L_bp_cycles — the k-step grad trick from HRM. At training time, only the trailing L_bp_cycles[i] of the L_cycles low-level iterations propagate gradients; earlier iterations run under torch.no_grad() so their activations are not stored. No effect at inference.

Usage

HRM-Text-1B is a base language model. It does not ship a chat_template and apply_chat_template is intentionally not supported for this release — the prompt format used during pre-training is still evolving, and an instruction-tuned variant with a stable chat template will follow in a separate release. Drive the base model through plain AutoTokenizer + AutoModelForCausalLM.generate(...):

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sapientinc/HRM-Text-1B")
model = AutoModelForCausalLM.from_pretrained(
    "sapientinc/HRM-Text-1B", device_map="auto",
)

inputs = tokenizer("The quick brown fox", return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=16, do_sample=False)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Attention backends

"sdpa" is the default, and is the right choice for most workloads. "flex_attention" is supported and pays off at long context — but it carries a fixed BlockMask construction cost per forward that does not amortise to the win you might expect from HRM-Text’s recurrent stack reuse. Indicative prefill latency on a single H100 with the released 1.2B base checkpoint and the default H_cycles=2, L_cycles=3:

seq_len	sdpa	flex_attention	recommendation
64	41 ms	70 ms	sdpa
256	41 ms	70 ms	sdpa
1024	42 ms	69 ms	sdpa
2048	85 ms	78 ms	flex (≈ 1.1x)

So pick the backend by the workload:

# Default — short / medium context
model = AutoModelForCausalLM.from_pretrained("sapientinc/HRM-Text-1B", device_map="auto")

# Long context (≥ 2K tokens) — FlexAttention's per-block sparsity overtakes SDPA
model = AutoModelForCausalLM.from_pretrained(
    "sapientinc/HRM-Text-1B", device_map="auto", attn_implementation="flex_attention",
)

Both backends produce equivalent logits (verified top-1 100% match end-to-end against the torch reference). "eager" is supported and produces the same logits, but is rarely the fastest option on modern hardware. Its main use is output_attentions=True — SDPA / FlexAttention do not return per-head attention weights, so passes that need them for analysis or visualisation should run with attn_implementation="eager".

Any FlashAttention variation — FA 2/3/4 and HF Hub kernel implementations that may not follow the flash_attention_* naming convention — is rejected by HrmTextModel at init whenever config.prefix_lm=True (the default). FA backends only accept causal vs. non-causal masks and cannot represent the PrefixLM 4-D overlay. Use "sdpa" (default) or "flex_attention" for PrefixLM. Setting config.prefix_lm=False makes the mask pure causal and re-enables FA — useful for causal-only fine-tuning or inference paths where FA is the fastest option.

PrefixLM training

For supervised fine-tuning that respects the instruction / response boundary, emit token_type_ids from the data collator alongside input_ids — positions inside the instruction get 1, response and padding get 0. The model treats every position with token_type_ids == 1 as part of a single bidirectional block; everything else stays causal:

import torch

def collate_prefixlm(batch, pad_token_id=0, ignore_label_id=-100):
    """`batch[i] = {"instruction_ids": [...], "response_ids": [...]}`."""
    full_ids = [b["instruction_ids"] + b["response_ids"] for b in batch]
    prefix_lens = [len(b["instruction_ids"]) for b in batch]
    max_len = max(len(ids) for ids in full_ids)

    input_ids = torch.full((len(batch), max_len), pad_token_id, dtype=torch.long)
    token_type_ids = torch.zeros_like(input_ids)
    labels = torch.full_like(input_ids, ignore_label_id)
    attention_mask = torch.zeros_like(input_ids)

    for i, (ids, plen) in enumerate(zip(full_ids, prefix_lens)):
        input_ids[i, : len(ids)] = torch.tensor(ids)
        token_type_ids[i, :plen] = 1                      # bidirectional prefix
        labels[i, plen : len(ids)] = input_ids[i, plen : len(ids)]  # loss on response only
        attention_mask[i, : len(ids)] = 1
    return {
        "input_ids": input_ids,
        "token_type_ids": token_type_ids,
        "attention_mask": attention_mask,
        "labels": labels,
    }

See HrmTextModel.forward() for the accepted shape.

HrmTextConfig

class transformers.HrmTextConfig

< source >

( transformers_version: str | None = None architectures: list[str] | None = None 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 vocab_size: int = 151808 hidden_size: int = 1536 intermediate_size: int = 4096 num_hidden_layers: int = 16 num_attention_heads: int = 12 hidden_act: str = 'silu' max_position_embeddings: int = 2048 initializer_range: float = 0.02 rms_norm_eps: float = 1e-06 use_cache: bool = True pad_token_id: int | None = None bos_token_id: int | None = None eos_token_id: int | list[int] | None = None tie_word_embeddings: bool = False rope_parameters: transformers.modeling_rope_utils.RopeParameters | dict | None = None attention_bias: bool = False attention_dropout: int | float | None = 0.0 mlp_bias: bool = False head_dim: int = 128 H_cycles: int = 2 L_cycles: int = 3 L_bp_cycles: list[int] | None = None embedding_scale: float | None = None prefix_lm: bool = True num_layers_per_stack: int | None = None )

Parameters

vocab_size (int, optional, defaults to 151808) — 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 1536) — Dimension of the hidden representations.
intermediate_size (int, optional, defaults to 4096) — Dimension of the MLP representations.
num_hidden_layers (int, optional, defaults to 16) — Number of hidden layers in the Transformer decoder.
num_attention_heads (int, optional, defaults to 12) — Number of attention heads for each attention layer in the Transformer decoder.
hidden_act (str, optional, defaults to silu) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
max_position_embeddings (int, optional, defaults to 2048) — The maximum sequence length that this model might ever be used with.
initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (float, optional, defaults to 1e-06) — The epsilon used by the rms normalization layers.
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.
pad_token_id (int, optional) — Token id used for padding in the vocabulary.
bos_token_id (int, optional) — Token id used for beginning-of-stream in the vocabulary.
eos_token_id (Union[int, list[int]], optional) — Token id used for end-of-stream in the vocabulary.
tie_word_embeddings (bool, optional, defaults to False) — Whether to tie weight embeddings according to model’s tied_weights_keys mapping.
rope_parameters (Union[~modeling_rope_utils.RopeParameters, dict], optional) — Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for rope_theta and optionally parameters used for scaling in case you want to use RoPE with longer max_position_embeddings.
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 (Union[int, float], optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
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.
head_dim (int, optional, defaults to 128) — The attention head dimension. If None, it will default to hidden_size // num_attention_heads
H_cycles (int, optional, defaults to 2) — Number of high-level cycles.
L_cycles (int, optional, defaults to 3) — Number of low-level cycles per H-cycle.
L_bp_cycles (list[int], optional, defaults to [2]) — Training-time gradient-routing list; left-padded with 1s up to L_cycles inside the model. Inference-time no-op.
embedding_scale (float, optional) — Token-embedding multiplier. If None, defaults to 1 / initializer_range.
prefix_lm (bool, optional, defaults to True) — Instruction tokens attend bidirectionally, response tokens attend causally.
num_layers_per_stack (int, optional) — Real number of transformer blocks inside each of the H / L stacks. Set automatically on first construction: the value passed as num_hidden_layers is remembered here and num_hidden_layers is then rewritten to num_layers_per_stack * H_cycles * (L_cycles + 1) so that DynamicCache(config=...) pre-allocates one slot per unique attention invocation under the recurrent forward. Do not set this directly on first construction — pass the real per-stack count as num_hidden_layers and let __post_init__ split it.

This is the configuration class to store the configuration of a HrmTextModel. It is used to instantiate a Hrm Text 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 sapientinc/HRM-Text-1B

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

HrmTextModel

class transformers.HrmTextModel

< source >

( config: HrmTextConfig )

Parameters

config (HrmTextConfig) — 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.

The bare Hrm Text Text Model outputting raw hidden-states without any specific head on to.

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

< source >

( input_ids: torch.LongTensor | None = None attention_mask: torch.Tensor | None = None position_ids: torch.LongTensor | None = None past_key_values: transformers.cache_utils.Cache | None = None token_type_ids: torch.LongTensor | None = None inputs_embeds: torch.FloatTensor | None = None use_cache: bool | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → BaseModelOutputWithPast 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 (~cache_utils.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 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).
token_type_ids (torch.LongTensor of shape (batch, seq_len), optional) — Per-position bidirectional/causal indicator. Tokens with token_type_ids == 1 form a single bidirectional block; all other positions are causal.
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.
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).

Returns

BaseModelOutputWithPast or tuple(torch.FloatTensor)

A BaseModelOutputWithPast 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 (HrmTextConfig) and inputs.

The HrmTextModel 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)) — Sequence of hidden-states at the output of the last layer of the model.

If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
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 and optionally if config.is_encoder_decoder=True in the cross-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.

HrmTextForCausalLM

class transformers.HrmTextForCausalLM

< source >

( config )

Parameters

config (HrmTextForCausalLM) — 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.

The Hrm Text Model for causal language modeling.

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

< source >

( input_ids: torch.LongTensor | None = None attention_mask: torch.Tensor | None = None position_ids: torch.LongTensor | None = None past_key_values: transformers.cache_utils.Cache | None = None token_type_ids: torch.LongTensor | 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: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → 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 (~cache_utils.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 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).
token_type_ids (torch.LongTensor of shape (batch, seq_len), optional) — Per-position bidirectional/causal indicator. Tokens with token_type_ids == 1 form a single bidirectional block; all other positions are causal.
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 (HrmTextConfig) and inputs.

The HrmTextForCausalLM 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:

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