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index 2c38123df..ecf1be2db 100644
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -95,6 +95,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
"HunYuanDenseV1ForCausalLM": "hunyuan",
"HunYuanMoEV1ForCausalLM": "hunyuan",
"HunYuanVLForConditionalGeneration": "hunyuan",
+ "HrmTextForCausalLM": "hrm_text",
"IQuestCoderForCausalLM": "llama",
"InternLM2ForCausalLM": "internlm",
"InternLM3ForCausalLM": "internlm",
diff --git a/conversion/hrm_text.py b/conversion/hrm_text.py
new file mode 100644
index 000000000..1f29ab55e
--- /dev/null
+++ b/conversion/hrm_text.py
@@ -0,0 +1,120 @@
+from __future__ import annotations
+
+import re
+import json
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+ from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("HrmTextForCausalLM")
+class HrmTextModel(TextModel):
+ model_arch = gguf.MODEL_ARCH.HRM_TEXT
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ with open(self.dir_model / "config.json", "r", encoding="utf-8") as f:
+ self.raw_hparams = json.load(f)
+
+ self.layers_per_stack = self.raw_hparams["num_hidden_layers"]
+ self.h_cycles = self.raw_hparams["H_cycles"]
+ self.l_cycles = self.raw_hparams["L_cycles"]
+ self.physical_block_count = self.layers_per_stack * 2
+ self.cache_block_count = self.layers_per_stack * self.h_cycles * (self.l_cycles + 1)
+
+ # GGUF tensors store one physical L stack followed by one physical H stack.
+ # The runtime expands these 32 physical layers across 128 KV-cache slots.
+ self.block_count = self.physical_block_count
+ self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+ def set_vocab(self):
+ # HRM-Text ships a Qwen2-style tokenizer.json. Keep it as a plain tokenizer;
+ # do not add a chat template for validation GGUFs.
+ self._set_vocab_gpt2()
+
+ def get_vocab_base_pre(self, tokenizer) -> str:
+ del tokenizer
+ return "qwen2"
+
+ def set_gguf_parameters(self):
+ hp = self.raw_hparams
+ head_dim = hp["head_dim"]
+
+ self.gguf_writer.add_context_length(hp["max_position_embeddings"])
+ self.gguf_writer.add_embedding_length(hp["hidden_size"])
+ self.gguf_writer.add_block_count(self.cache_block_count)
+ self.gguf_writer.add_feed_forward_length(hp["intermediate_size"])
+ self.gguf_writer.add_head_count(hp["num_attention_heads"])
+ self.gguf_writer.add_head_count_kv(hp["num_key_value_heads"])
+ self.gguf_writer.add_key_length(head_dim)
+ self.gguf_writer.add_value_length(head_dim)
+ self.gguf_writer.add_rope_dimension_count(head_dim)
+ self.gguf_writer.add_rope_freq_base(hp.get("rope_theta", 10000.0))
+ self.gguf_writer.add_layer_norm_rms_eps(hp["rms_norm_eps"])
+ self.gguf_writer.add_embedding_scale(hp["embedding_scale"])
+
+ arch = self.gguf_writer.arch
+ self.gguf_writer.add_uint32(gguf.Keys.LLM.HRM_LAYERS_PER_STACK.format(arch=arch), self.layers_per_stack)
+ self.gguf_writer.add_uint32(gguf.Keys.LLM.HRM_H_CYCLES.format(arch=arch), self.h_cycles)
+ self.gguf_writer.add_uint32(gguf.Keys.LLM.HRM_L_CYCLES.format(arch=arch), self.l_cycles)
+ self.gguf_writer.add_bool(gguf.Keys.LLM.HRM_PREFIX_LM.format(arch=arch), bool(hp.get("prefix_lm", False)))
+
+ def _format(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str:
+ return self.format_tensor_name(key, bid=bid, suffix=suffix)
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ if name == "model.embed_tokens.weight":
+ yield self._format(gguf.MODEL_TENSOR.TOKEN_EMBD), data_torch
+ return
+
+ if name == "lm_head.weight":
+ yield self._format(gguf.MODEL_TENSOR.OUTPUT), data_torch
+ return
+
+ if name == "model.z_L_init":
+ yield self._format(gguf.MODEL_TENSOR.HRM_Z_L_INIT, suffix=""), data_torch
+ return
+
+ match = re.fullmatch(r"model\.([LH])_module\.layers\.(\d+)\.(.+)", name)
+ if match is None:
+ raise ValueError(f"Can not map tensor {name!r}")
+
+ stack, layer_s, tensor_name = match.groups()
+ layer_idx = int(layer_s)
+ if layer_idx >= self.layers_per_stack:
+ raise ValueError(f"Layer index {layer_idx} outside HRM stack size {self.layers_per_stack}")
+
+ physical_bid = layer_idx + (self.layers_per_stack if stack == "H" else 0)
+
+ if tensor_name == "attn.gqkv_proj.weight":
+ gate, q, k, v = torch.chunk(data_torch, 4, dim=0)
+ logger.debug("Split %s as gate, q, k, v", name)
+ yield self._format(gguf.MODEL_TENSOR.ATTN_GATE, physical_bid), gate.contiguous()
+ yield self._format(gguf.MODEL_TENSOR.ATTN_Q, physical_bid), q.contiguous()
+ yield self._format(gguf.MODEL_TENSOR.ATTN_K, physical_bid), k.contiguous()
+ yield self._format(gguf.MODEL_TENSOR.ATTN_V, physical_bid), v.contiguous()
+ return
+
+ if tensor_name == "attn.o_proj.weight":
+ yield self._format(gguf.MODEL_TENSOR.ATTN_OUT, physical_bid), data_torch
+ return
+
+ if tensor_name == "mlp.gate_up_proj.weight":
+ gate, up = torch.chunk(data_torch, 2, dim=0)
+ logger.debug("Split %s as gate, up", name)
+ yield self._format(gguf.MODEL_TENSOR.FFN_GATE, physical_bid), gate.contiguous()
+ yield self._format(gguf.MODEL_TENSOR.FFN_UP, physical_bid), up.contiguous()
+ return
+
+ if tensor_name == "mlp.down_proj.weight":
+ yield self._format(gguf.MODEL_TENSOR.FFN_DOWN, physical_bid), data_torch
+ return
+
+ raise ValueError(f"Can not map tensor {name!r}")
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 7fdcf03d7..b84cc8827 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -144,6 +144,10 @@ class Keys:
TOKEN_SHIFT_COUNT = "{arch}.token_shift_count"
INTERLEAVE_MOE_LAYER_STEP = "{arch}.interleave_moe_layer_step"
FULL_ATTENTION_INTERVAL = "{arch}.full_attention_interval"
+ HRM_LAYERS_PER_STACK = "{arch}.layers_per_stack"
+ HRM_H_CYCLES = "{arch}.h_cycles"
+ HRM_L_CYCLES = "{arch}.l_cycles"
+ HRM_PREFIX_LM = "{arch}.prefix_lm"
ACTIVATION_SPARSITY_SCALE = "{arch}.activation_sparsity_scale"
ALTUP_ACTIVE_IDX = "{arch}.altup.active_idx"
ALTUP_NUM_INPUTS = "{arch}.altup.num_inputs"
@@ -410,6 +414,7 @@ class MODEL_ARCH(IntEnum):
QWEN3 = auto()
QWEN3MOE = auto()
QWEN3NEXT = auto()
+ HRM_TEXT = auto()
QWEN3VL = auto()
QWEN3VLMOE = auto()
QWEN35 = auto()
@@ -527,6 +532,7 @@ class MODEL_TENSOR(IntEnum):
TOKEN_TYPES = auto()
POS_EMBD = auto()
OUTPUT = auto()
+ HRM_Z_L_INIT = auto()
DENSE_2_OUT = auto() # embeddinggemma 2_Dense
DENSE_3_OUT = auto() # embeddinggemma 3_Dense
OUTPUT_NORM = auto()
@@ -925,6 +931,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.QWEN3: "qwen3",
MODEL_ARCH.QWEN3MOE: "qwen3moe",
MODEL_ARCH.QWEN3NEXT: "qwen3next",
+ MODEL_ARCH.HRM_TEXT: "hrm_text",
MODEL_ARCH.QWEN3VL: "qwen3vl",
MODEL_ARCH.QWEN3VLMOE: "qwen3vlmoe",
MODEL_ARCH.QWEN35: "qwen35",
@@ -1042,6 +1049,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.POS_EMBD: "position_embd",
MODEL_TENSOR.OUTPUT_NORM: "output_norm",
MODEL_TENSOR.OUTPUT: "output",
+ MODEL_TENSOR.HRM_Z_L_INIT: "hrm.z_l_init",
MODEL_TENSOR.DENSE_2_OUT: "dense_2", # embeddinggemma 2_Dense
MODEL_TENSOR.DENSE_3_OUT: "dense_3", # embeddinggemma 2_Dense
MODEL_TENSOR.ROPE_FREQS: "rope_freqs",
@@ -2057,6 +2065,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.SSM_BETA_ALPHA,
MODEL_TENSOR.SSM_OUT
],
+ MODEL_ARCH.HRM_TEXT: [
+ MODEL_TENSOR.TOKEN_EMBD,
+ MODEL_TENSOR.OUTPUT,
+ MODEL_TENSOR.HRM_Z_L_INIT,
+ MODEL_TENSOR.ATTN_Q,
+ MODEL_TENSOR.ATTN_K,
+ MODEL_TENSOR.ATTN_V,
+ MODEL_TENSOR.ATTN_GATE,
+ MODEL_TENSOR.ATTN_OUT,
+ MODEL_TENSOR.FFN_GATE,
+ MODEL_TENSOR.FFN_DOWN,
+ MODEL_TENSOR.FFN_UP,
+ ],
MODEL_ARCH.QWEN3VL: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index c9eead18a..5b8ee3781 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -37,6 +37,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_QWEN3, "qwen3" },
{ LLM_ARCH_QWEN3MOE, "qwen3moe" },
{ LLM_ARCH_QWEN3NEXT, "qwen3next" },
+ { LLM_ARCH_HRM_TEXT, "hrm_text" },
{ LLM_ARCH_QWEN3VL, "qwen3vl" },
{ LLM_ARCH_QWEN3VLMOE, "qwen3vlmoe" },
{ LLM_ARCH_QWEN35, "qwen35" },
@@ -209,6 +210,10 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_TOKEN_SHIFT_COUNT, "%s.token_shift_count" },
{ LLM_KV_INTERLEAVE_MOE_LAYER_STEP, "%s.interleave_moe_layer_step" },
{ LLM_KV_FULL_ATTENTION_INTERVAL, "%s.full_attention_interval" },
+ { LLM_KV_HRM_LAYERS_PER_STACK, "%s.layers_per_stack" },
+ { LLM_KV_HRM_H_CYCLES, "%s.h_cycles" },
+ { LLM_KV_HRM_L_CYCLES, "%s.l_cycles" },
+ { LLM_KV_HRM_PREFIX_LM, "%s.prefix_lm" },
{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
@@ -346,6 +351,7 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT_NORM_LFM2, "token_embd_norm" }, // fix for wrong tensor name
{ LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_HRM_Z_L_INIT, "hrm.z_l_init" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
@@ -565,6 +571,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_POS_EMBD, {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
{LLM_TENSOR_TOKEN_TYPES, {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
{LLM_TENSOR_TOKEN_EMBD_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, // do the norms on the first layer (not the input layer)
+ {LLM_TENSOR_HRM_Z_L_INIT, {LLM_TENSOR_LAYER_INPUT, GGML_OP_MUL}},
{LLM_TENSOR_OUTPUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_CLS, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_CLS_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 89cf16cc3..fa04b684b 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -41,6 +41,7 @@ enum llm_arch {
LLM_ARCH_QWEN3,
LLM_ARCH_QWEN3MOE,
LLM_ARCH_QWEN3NEXT,
+ LLM_ARCH_HRM_TEXT,
LLM_ARCH_QWEN3VL,
LLM_ARCH_QWEN3VLMOE,
LLM_ARCH_QWEN35,
@@ -213,6 +214,10 @@ enum llm_kv {
LLM_KV_TOKEN_SHIFT_COUNT,
LLM_KV_INTERLEAVE_MOE_LAYER_STEP,
LLM_KV_FULL_ATTENTION_INTERVAL,
+ LLM_KV_HRM_LAYERS_PER_STACK,
+ LLM_KV_HRM_H_CYCLES,
+ LLM_KV_HRM_L_CYCLES,
+ LLM_KV_HRM_PREFIX_LM,
LLM_KV_ATTENTION_HEAD_COUNT,
LLM_KV_ATTENTION_HEAD_COUNT_KV,
@@ -354,6 +359,7 @@ enum llm_tensor {
LLM_TENSOR_DENSE_2_OUT,
LLM_TENSOR_DENSE_3_OUT,
LLM_TENSOR_OUTPUT,
+ LLM_TENSOR_HRM_Z_L_INIT,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_OUTPUT_NORM_LFM2, // fix for wrong tensor name
LLM_TENSOR_ROPE_FREQS,
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index ad36c0666..fa80f4260 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -2208,6 +2208,9 @@ uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR || model.arch == LLM_ARCH_QWEN35 || model.arch == LLM_ARCH_QWEN35MOE) {
return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
}
+ if (model.arch == LLM_ARCH_HRM_TEXT) {
+ return std::max<uint32_t>(n_tokens * 80, 64u * model.n_tensors());
+ }
uint32_t res = std::max<uint32_t>(1024u, 8u*model.n_tensors());
for (const auto & lora : model.loras) {
res += lora->get_n_nodes();
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index e2d051edc..812598f69 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -164,6 +164,12 @@ struct llama_hparams {
float f_embedding_scale = 0.0f;
float f_attention_scale = 0.0f;
+ // HRM-Text recurrence metadata. n_layer remains the expanded KV-cache slot count.
+ uint32_t n_hrm_layer_per_stack = 0;
+ uint32_t n_hrm_h_cycles = 0;
+ uint32_t n_hrm_l_cycles = 0;
+ bool hrm_prefix_lm = false;
+
// grok-2
float f_attn_out_scale = 0.0f;
uint32_t attn_temp_length = 0;
diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp
index 528e4c9c0..8a6e009c6 100644
--- a/src/llama-model-saver.cpp
+++ b/src/llama-model-saver.cpp
@@ -245,6 +245,10 @@ void llama_model_saver::add_kv_from_model() {
add_kv(LLM_KV_TOKEN_SHIFT_COUNT, hparams.token_shift_count);
add_kv(LLM_KV_INTERLEAVE_MOE_LAYER_STEP, hparams.n_moe_layer_step);
// add_kv(LLM_KV_FULL_ATTENTION_INTERVAL, ???);
+ add_kv(LLM_KV_HRM_LAYERS_PER_STACK, hparams.n_hrm_layer_per_stack);
+ add_kv(LLM_KV_HRM_H_CYCLES, hparams.n_hrm_h_cycles);
+ add_kv(LLM_KV_HRM_L_CYCLES, hparams.n_hrm_l_cycles);
+ add_kv(LLM_KV_HRM_PREFIX_LM, hparams.hrm_prefix_lm);
add_kv(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, true);
add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, true);
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 8bf20a716..a3cc996aa 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -96,6 +96,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
return new llama_model_qwen2moe(params);
case LLM_ARCH_QWEN3:
return new llama_model_qwen3(params);
+ case LLM_ARCH_HRM_TEXT:
+ return new llama_model_hrm_text(params);
case LLM_ARCH_QWEN3MOE:
return new llama_model_qwen3moe(params);
case LLM_ARCH_QWEN3VL:
@@ -2339,6 +2341,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_PANGU_EMBED:
case LLM_ARCH_AFMOE:
case LLM_ARCH_QWEN3NEXT:
+ case LLM_ARCH_HRM_TEXT:
case LLM_ARCH_MIMO2:
case LLM_ARCH_STEP35:
return LLAMA_ROPE_TYPE_NEOX;
diff --git a/src/models/hrm-text.cpp b/src/models/hrm-text.cpp
new file mode 100644
index 000000000..e0a3e9f59
--- /dev/null
+++ b/src/models/hrm-text.cpp
@@ -0,0 +1,183 @@
+#include "models.h"
+
+#include <cmath>
+#include <vector>
+
+void llama_model_hrm_text::load_arch_hparams(llama_model_loader & ml) {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale);
+ ml.get_key(LLM_KV_HRM_LAYERS_PER_STACK, hparams.n_hrm_layer_per_stack);
+ ml.get_key(LLM_KV_HRM_H_CYCLES, hparams.n_hrm_h_cycles);
+ ml.get_key(LLM_KV_HRM_L_CYCLES, hparams.n_hrm_l_cycles);
+ ml.get_key(LLM_KV_HRM_PREFIX_LM, hparams.hrm_prefix_lm, false);
+
+ switch (hparams.n_embd) {
+ case 1536: type = LLM_TYPE_1B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+}
+
+void llama_model_hrm_text::load_arch_tensors(llama_model_loader &) {
+ LLAMA_LOAD_LOCALS;
+
+ const int64_t n_stack = hparams.n_hrm_layer_per_stack;
+ const int64_t n_cycle_slots = n_stack * (hparams.n_hrm_l_cycles + 1);
+
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+ hrm_z_l_init = create_tensor(tn(LLM_TENSOR_HRM_Z_L_INIT), {n_embd}, 0);
+
+ std::vector<bool> loaded_physical(2 * n_stack, false);
+
+ for (int il = 0; il < n_layer; ++il) {
+ auto & layer = layers[il];
+
+ const int64_t layer_in_stack = il % n_stack;
+ const int64_t phase = (il % n_cycle_slots) / n_stack;
+ const bool is_h_stack = phase == int64_t(hparams.n_hrm_l_cycles);
+ const int physical_bid = int((is_h_stack ? n_stack : 0) + layer_in_stack);
+
+ const int flags = loaded_physical[physical_bid] ? TENSOR_DUPLICATED : 0;
+ loaded_physical[physical_bid] = true;
+
+ create_tensor_qkv(layer, physical_bid,
+ n_embd,
+ n_embd_head_k * n_head,
+ n_embd_k_gqa,
+ n_embd_v_gqa,
+ flags);
+
+ layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", physical_bid), {n_embd, n_embd_head_k * n_head}, flags);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", physical_bid), {n_embd_head_k * n_head, n_embd}, flags);
+
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", physical_bid), {n_embd, n_ff}, flags);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", physical_bid), {n_ff, n_embd}, flags);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", physical_bid), {n_embd, n_ff}, flags);
+ }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_hrm_text::build_arch_graph(const llm_graph_params & params) const {
+ return std::make_unique<graph>(*this, params);
+}
+
+llama_model_hrm_text::graph::graph(const llama_model & model_, const llm_graph_params & params) : llm_graph_context(params) {
+ const auto & model = static_cast<const llama_model_hrm_text &>(model_);
+
+ GGML_ASSERT(model.tok_embd != nullptr);
+ GGML_ASSERT(model.output != nullptr);
+ GGML_ASSERT(model.hrm_z_l_init != nullptr);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+ GGML_ASSERT(n_embd_head == n_rot);
+
+ const int64_t n_stack = hparams.n_hrm_layer_per_stack;
+ const int64_t h_cycles = hparams.n_hrm_h_cycles;
+ const int64_t l_cycles = hparams.n_hrm_l_cycles;
+
+ ggml_tensor * inp_pos = build_inp_pos();
+ auto * inp_attn = build_attn_inp_kv();
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+ ggml_tensor * hidden_high = build_inp_embd(model.tok_embd);
+ ggml_tensor * hidden_low = ggml_repeat(ctx0, model.hrm_z_l_init, hidden_high);
+ cb(hidden_low, "hrm_z_l_init", -1);
+
+ const float kq_scale = 1.0f / std::sqrt(float(n_embd_head));
+
+ auto build_stack = [&](ggml_tensor * stack_inp, int slot_offset) -> ggml_tensor * {
+ ggml_tensor * stack_cur = stack_inp;
+
+ for (int layer_idx = 0; layer_idx < n_stack; ++layer_idx) {
+ const int il = slot_offset + layer_idx;
+ const auto & layer = model.layers[il];
+
+ ggml_tensor * inpSA = stack_cur;
+ ggml_tensor * cur = build_norm(stack_cur, nullptr, nullptr, LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ {
+ ggml_tensor * attn_inp = cur;
+ auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur, n_embd_head, n_head, n_head_kv, il);
+
+ ggml_tensor * gate = build_lora_mm(layer.wqkv_gate, attn_inp, layer.wqkv_gate_s);
+ cb(gate, "attn_gate_proj", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ cb(Qcur, "Qcur_rope", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ cb(Kcur, "Kcur_rope", il);
+
+ cur = build_attn(inp_attn,
+ nullptr, nullptr, nullptr,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+ cb(cur, "attn_out", il);
+
+ gate = ggml_sigmoid(ctx0, gate);
+ cb(gate, "attn_gate_sig", il);
+
+ cur = ggml_mul(ctx0, cur, gate);
+ cb(cur, "attn_gated", il);
+
+ cur = build_lora_mm(layer.wo, cur, layer.wo_s);
+ cb(cur, "attn_o_proj", il);
+ }
+
+ ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ cur = build_norm(ffn_inp, nullptr, nullptr, LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = build_ffn(cur,
+ layer.ffn_up, nullptr, layer.ffn_up_s,
+ layer.ffn_gate, nullptr, layer.ffn_gate_s,
+ layer.ffn_down, nullptr, layer.ffn_down_s,
+ nullptr,
+ LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = build_cvec(cur, il);
+ cb(cur, "hrm_layer_out", il);
+
+ stack_cur = cur;
+ }
+
+ stack_cur = build_norm(stack_cur, nullptr, nullptr, LLM_NORM_RMS, slot_offset);
+ cb(stack_cur, "stack_final_norm", slot_offset);
+ return stack_cur;
+ };
+
+ for (int h = 0; h < h_cycles; ++h) {
+ for (int l = 0; l < l_cycles; ++l) {
+ const int slot_offset = int((h * (l_cycles + 1) + l) * n_stack);
+ hidden_low = build_stack(ggml_add(ctx0, hidden_low, hidden_high), slot_offset);
+ }
+
+ const int slot_offset = int((h * (l_cycles + 1) + l_cycles) * n_stack);
+ hidden_high = build_stack(ggml_add(ctx0, hidden_high, hidden_low), slot_offset);
+ }
+
+ ggml_tensor * cur = hidden_high;
+
+ if (inp_out_ids) {
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ }
+
+ res->t_embd = cur;
+
+ cur = build_lora_mm(model.output, cur, model.output_s);
+ cb(cur, "result_output", -1);
+
+ res->t_logits = cur;
+ ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/models.h b/src/models/models.h
index 7e551eb96..7da6b7f7f 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -515,6 +515,20 @@ struct llama_model_qwen3 : public llama_model_base {
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
+struct llama_model_hrm_text : public llama_model_base {
+ llama_model_hrm_text(const struct llama_model_params & params) : llama_model_base(params) {}
+ void load_arch_hparams(llama_model_loader & ml) override;
+ void load_arch_tensors(llama_model_loader & ml) override;
+
+ ggml_tensor * hrm_z_l_init = nullptr;
+
+ struct graph : public llm_graph_context {
+ graph(const llama_model & model, const llm_graph_params & params);
+ };
+
+ std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
struct llama_model_qwen3moe : public llama_model_base {
llama_model_qwen3moe(const struct llama_model_params & params) : llama_model_base(params) {}
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