Commit 448147d42 for llama.cpp
commit 448147d42a71d0147c6dc844f99f9ffe7419183a
Author: Pascal <admin@serveurperso.com>
Date: Wed Oct 7 16:30:00 2026 +0200
llama: share the nextn tensor flags between models (#30097)
* llama: share the nextn tensor flags between models
Follow-up of the TODO in glm5-next: move the trunk-only and MTP-only
detection that each model copied into a nextn_flags helper of
llama_model_base. It probes the first trunk layer and the first NextN
layer, and adds TENSOR_SKIP when MTP is not loaded. qwen4exp probes
hc_attn_norm since it has no attn_norm.
deepseek4, nemotron-h, qwen35, qwen35moe, qwen3next and qwen4exp now
also accept a trunk-only file, like the other models.
* llama: avoid capturing structured bindings in the nextn flags
Lambdas that capture structured bindings need C++20, and GCC 15
rejects them under -Werror, so the models read the trunk and MTP
flags into plain variables.
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 1f3b80b08..9f05a66e4 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -3461,6 +3461,27 @@ void llama_model_base::create_tensor_qkv(llama_layer & layer, int bid,
}
}
+llama_model_base::nextn_flags_t llama_model_base::nextn_flags(llama_model_loader & ml, llm_tensor trunk_probe) const {
+ int trunk = 0;
+ int mtp = 0;
+
+ // a file without the first trunk layer is MTP-only, a file without the first NextN layer is trunk-only
+ if (hparams.n_layer_nextn > 0) {
+ if (ml.get_weight(tn(trunk_probe, "weight", 0).str().c_str()) == nullptr) {
+ trunk = TENSOR_NOT_REQUIRED;
+ }
+ if (ml.get_weight(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", hparams.n_layer()).str().c_str()) == nullptr) {
+ mtp = TENSOR_NOT_REQUIRED;
+ }
+ }
+
+ if (!ml.load_mtp) {
+ mtp |= TENSOR_SKIP;
+ }
+
+ return { trunk, mtp };
+}
+
void llama_model_base::load_swa_pattern(llama_model_loader & ml, uint32_t n_pattern, bool dense_first) {
if (ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, false)) {
return;
diff --git a/src/llama-model.h b/src/llama-model.h
index a86cc5693..82124471f 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -849,6 +849,13 @@ struct llama_model_base : public llama_model {
int64_t n_embd_, int64_t n_embd_q_, int64_t n_embd_k_, int64_t n_embd_v_,
int flags);
+ // helper: tensor flags for a file that holds the trunk, the NextN layers, or both
+ struct nextn_flags_t {
+ int trunk; // TENSOR_NOT_REQUIRED when the file holds only the NextN layers
+ int mtp; // TENSOR_NOT_REQUIRED when the file holds only the trunk, TENSOR_SKIP when MTP is not loaded
+ };
+ nextn_flags_t nextn_flags(llama_model_loader & ml, llm_tensor trunk_probe = LLM_TENSOR_ATTN_NORM) const;
+
// helper: read the SWA pattern as one flag per layer, or as a period expanded by set_swa_pattern
void load_swa_pattern(llama_model_loader & ml, uint32_t n_pattern, bool dense_first = false);
diff --git a/src/models/bailingmoe3.cpp b/src/models/bailingmoe3.cpp
index b04b58449..2458b1c1a 100644
--- a/src/models/bailingmoe3.cpp
+++ b/src/models/bailingmoe3.cpp
@@ -64,15 +64,9 @@ void llama_model_bailingmoe3::load_arch_tensors(llama_model_loader & ml) {
const int64_t qk_head_dim = hparams.n_embd_head_k_mla();
const int64_t v_head_dim = hparams.n_embd_head_v_mla();
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
- const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
-
- if (!ml.load_mtp) {
- mtp_flags |= TENSOR_SKIP;
- }
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
for (int il = 0; il < n_layer; ++il) {
auto & layer = layers[il];
diff --git a/src/models/cohere2moe.cpp b/src/models/cohere2moe.cpp
index 3baf3950c..a379e2f60 100644
--- a/src/models/cohere2moe.cpp
+++ b/src/models/cohere2moe.cpp
@@ -40,18 +40,9 @@ void llama_model_cohere2moe::load_arch_hparams(llama_model_loader & ml) {
void llama_model_cohere2moe::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- // Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
- // tensors live in a separate file. Mark MTP tensors NOT_REQUIRED so the
- // trunk loads cleanly.
- const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
- const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
-
- if (!ml.load_mtp) {
- mtp_flags |= TENSOR_SKIP;
- }
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp
index 47fc2f1ac..f1067a516 100644
--- a/src/models/deepseek2.cpp
+++ b/src/models/deepseek2.cpp
@@ -56,15 +56,9 @@ void llama_model_deepseek2::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
const int64_t n_expert_shared = hparams.n_expert_shared;
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
- const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
-
- if (!ml.load_mtp) {
- mtp_flags |= TENSOR_SKIP;
- }
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
const bool is_mla = hparams.is_mla();
diff --git a/src/models/deepseek32.cpp b/src/models/deepseek32.cpp
index ade9b9ed9..76f763c76 100644
--- a/src/models/deepseek32.cpp
+++ b/src/models/deepseek32.cpp
@@ -46,15 +46,9 @@ void llama_model_deepseek32::load_arch_hparams(llama_model_loader & ml) {
void llama_model_deepseek32::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
- const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
-
- if (!ml.load_mtp) {
- mtp_flags |= TENSOR_SKIP;
- }
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
const bool is_mla = hparams.is_mla();
if (!is_mla) {
diff --git a/src/models/deepseek4.cpp b/src/models/deepseek4.cpp
index 4edf0c2b4..0974b63b3 100644
--- a/src/models/deepseek4.cpp
+++ b/src/models/deepseek4.cpp
@@ -93,9 +93,9 @@ void llama_model_deepseek4::load_arch_tensors(llama_model_loader & ml) {
const int64_t hc_dim = hc_mult * n_embd;
const int64_t hc_mix_dim = (2 + hc_mult) * hc_mult;
- const bool mtp_only = (n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- const int mtp_flags = ml.load_mtp ? 0 : TENSOR_SKIP;
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp
index 32581f840..3e28ef271 100644
--- a/src/models/glm-dsa.cpp
+++ b/src/models/glm-dsa.cpp
@@ -75,19 +75,9 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
const int64_t n_expert_shared = hparams.n_expert_shared;
- // MTP-only: the GGUF carries only the NextN/MTP block(s) (user split target/draft).
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- // Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
- // tensors live in a separate file (or were stripped at conversion). Mark
- // MTP tensors NOT_REQUIRED so the trunk loads cleanly.
- const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
- const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
-
- if (!ml.load_mtp) {
- mtp_flags |= TENSOR_SKIP;
- }
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
const bool is_mla = hparams.is_mla();
if (!is_mla) {
diff --git a/src/models/glm4-moe.cpp b/src/models/glm4-moe.cpp
index d65cace1f..4b41b5958 100644
--- a/src/models/glm4-moe.cpp
+++ b/src/models/glm4-moe.cpp
@@ -29,15 +29,9 @@ void llama_model_glm4_moe::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
const int64_t n_expert_shared = hparams.n_expert_shared;
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
- const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
-
- if (!ml.load_mtp) {
- mtp_flags |= TENSOR_SKIP;
- }
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
GGML_ASSERT(hparams.n_expert > 0 && "n_expert must be > 0 for GLM4_MOE MoE layers");
GGML_ASSERT(hparams.n_expert_used() > 0 && "n_expert_used must be > 0 for GLM4_MOE MoE layers");
diff --git a/src/models/glm5-next.cpp b/src/models/glm5-next.cpp
index 4343e6ac6..48b07f1e2 100644
--- a/src/models/glm5-next.cpp
+++ b/src/models/glm5-next.cpp
@@ -64,16 +64,9 @@ void llama_model_glm5_next::load_arch_tensors(llama_model_loader & ml) {
const int64_t hc = hparams.dsv4_hc_mult;
const int64_t hc_mix_dim = (2 + hc)*hc;
- // TODO: consolidate the trunk_only/mtp_only detection and flags shared with glm4-moe and qwen35moe
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
- const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
-
- if (!ml.load_mtp) {
- mtp_flags |= TENSOR_SKIP;
- }
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
diff --git a/src/models/hy-v3.cpp b/src/models/hy-v3.cpp
index bb0e0a2b8..c4c55c3f9 100644
--- a/src/models/hy-v3.cpp
+++ b/src/models/hy-v3.cpp
@@ -22,18 +22,9 @@ void llama_model_hy_v3::load_arch_hparams(llama_model_loader & ml) {
void llama_model_hy_v3::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- // Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
- // tensors live in a separate file (e.g. user split target/draft). Mark
- // MTP tensors NOT_REQUIRED so the trunk loads cleanly.
- const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
- const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
-
- if (!ml.load_mtp) {
- mtp_flags |= TENSOR_SKIP;
- }
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
diff --git a/src/models/mimo2.cpp b/src/models/mimo2.cpp
index 866959c83..a70350191 100644
--- a/src/models/mimo2.cpp
+++ b/src/models/mimo2.cpp
@@ -25,15 +25,9 @@ void llama_model_mimo2::load_arch_hparams(llama_model_loader & ml) {
void llama_model_mimo2::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
- const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
-
- if (!ml.load_mtp) {
- mtp_flags |= TENSOR_SKIP;
- }
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
diff --git a/src/models/nemotron-h.cpp b/src/models/nemotron-h.cpp
index cd90e24d2..cf78ebfbe 100644
--- a/src/models/nemotron-h.cpp
+++ b/src/models/nemotron-h.cpp
@@ -49,9 +49,9 @@ void llama_model_nemotron_h::load_arch_hparams(llama_model_loader & ml) {
void llama_model_nemotron_h::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
- const bool mtp_only = hparams.n_layer_nextn > 0 && ml.get_weight("blk.0.attn_norm.weight") == nullptr;
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- const int mtp_flags = !ml.load_mtp ? TENSOR_SKIP : 0;
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
// mamba2 Mixer SSM params
// NOTE: int64_t for tensor dimensions
diff --git a/src/models/qwen35.cpp b/src/models/qwen35.cpp
index e484850da..350cea808 100644
--- a/src/models/qwen35.cpp
+++ b/src/models/qwen35.cpp
@@ -33,9 +33,9 @@ void llama_model_qwen35::load_arch_hparams(llama_model_loader & ml) {
void llama_model_qwen35::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = !ml.load_mtp ? TENSOR_SKIP : 0;
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp
index 6e28028a8..f9cc8a2c6 100644
--- a/src/models/qwen35moe.cpp
+++ b/src/models/qwen35moe.cpp
@@ -36,9 +36,9 @@ void llama_model_qwen35moe::load_arch_hparams(llama_model_loader & ml) {
void llama_model_qwen35moe::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = !ml.load_mtp ? TENSOR_SKIP : 0;
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp
index 2d56c38ca..620161ac7 100644
--- a/src/models/qwen3next.cpp
+++ b/src/models/qwen3next.cpp
@@ -35,9 +35,9 @@ void llama_model_qwen3next::load_arch_tensors(llama_model_loader & ml) {
throw std::runtime_error(arch_name() + " model cannot have zero experts");
}
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = !ml.load_mtp ? TENSOR_SKIP : 0;
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
diff --git a/src/models/qwen4exp.cpp b/src/models/qwen4exp.cpp
index d05f72fc3..88e2e8891 100644
--- a/src/models/qwen4exp.cpp
+++ b/src/models/qwen4exp.cpp
@@ -175,10 +175,9 @@ void llama_model_qwen4exp::load_arch_tensors(llama_model_loader & ml) {
const int64_t hc_dim = hc * n_embd;
const int64_t hc_lr = hparams.hc_low_rank;
- // an MTP-only file carries the MTP block, the embeddings and the LM head, but no trunk
- const bool mtp_only = n_layer_nextn > 0 && ml.get_weight(tn(LLM_TENSOR_HC_ATTN_NORM, "weight", 0).str().c_str()) == nullptr;
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- const int mtp_flags = ml.load_mtp ? 0 : TENSOR_SKIP;
+ const auto nf = nextn_flags(ml, LLM_TENSOR_HC_ATTN_NORM);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
diff --git a/src/models/step35.cpp b/src/models/step35.cpp
index f4660bf19..bfa80fab3 100644
--- a/src/models/step35.cpp
+++ b/src/models/step35.cpp
@@ -37,18 +37,9 @@ void llama_model_step35::load_arch_hparams(llama_model_loader & ml) {
void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
- const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
- // Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
- // tensors live in a separate file (e.g. user split target/draft). Mark
- // MTP tensors NOT_REQUIRED so the trunk loads cleanly.
- const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
- const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
- const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
- int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
-
- if (!ml.load_mtp) {
- mtp_flags |= TENSOR_SKIP;
- }
+ const auto nf = nextn_flags(ml);
+ const int trunk_flags = nf.trunk;
+ const int mtp_flags = nf.mtp;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);