diff --git a/requirements/requirements-convert_legacy_llama.txt b/requirements/requirements-convert_legacy_llama.txt
index 28221fad0ce9..1f46a105b0f9 100644
--- a/requirements/requirements-convert_legacy_llama.txt
+++ b/requirements/requirements-convert_legacy_llama.txt
@@ -1,7 +1,9 @@
 numpy~=1.26.4
 sentencepiece>=0.1.98,<0.3.0
 
-transformers==4.57.6
+# GLM-5.2 and other recent models require transformers 5.x; the TokenizersBackend class
+# referenced in their tokenizer_config.json does not exist in 4.x
+transformers>=5.0.0
 
 gguf>=0.1.0
 protobuf>=4.21.0,<5.0.0
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index cf92ce4bb8b7..9f0967f96d1d 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -847,7 +847,11 @@ static void init_quantize_state_counters(quantize_state_impl & qs, std::vector<t
             qs.has_tied_embeddings = false;
         }
     }
-    qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)qs.model.hparams.n_layer();
+    // Use n_layer_all, not n_layer(), because models with nextn/MTP blocks (e.g. GLM-5.2,
+    // BailingMoE2) store ffn_down/gate/up_exps tensors for those extra blocks in the GGUF
+    // file. The MoE path in layer_info() parses the block index from the tensor name, so
+    // those tensors would exceed the bounds set by n_layer() = n_layer_all - n_layer_nextn.
+    qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)qs.model.hparams.n_layer_all;
 }
 
 //
diff --git a/src/models/deepseek32.cpp b/src/models/deepseek32.cpp
index 9a20e2ce9077..8a5b8074d148 100644
--- a/src/models/deepseek32.cpp
+++ b/src/models/deepseek32.cpp
@@ -219,8 +219,8 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_
 
             ggml_tensor * top_k = nullptr;
 
-            // lightning indexer
-            {
+            // lightning indexer - skipped when tensors are absent (e.g. GLM-5.2 only has them on certain blocks)
+            if (model.layers[il].indexer_attn_q_b) {
                 ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr);
                 cb(indexer_q, "indexer_q", il);
 
diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp
index 11d91312defc..dd70f5374387 100644
--- a/src/models/glm-dsa.cpp
+++ b/src/models/glm-dsa.cpp
@@ -100,12 +100,13 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
 
         layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
 
-        // DSA indexer
-        layer.indexer_k_norm   = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "weight", i), {hparams.indexer_head_size}, flags);
-        layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "bias",   i), {hparams.indexer_head_size}, flags);
-        layer.indexer_proj     = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ,     "weight", i), {n_embd, hparams.indexer_n_head}, flags);
-        layer.indexer_attn_k   = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K,   "weight", i), {n_embd, hparams.indexer_head_size}, flags);
-        layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags);
+        // DSA indexer - only present on a subset of blocks (dense-lead blocks + every 4th MoE block),
+        // so mark all as TENSOR_NOT_REQUIRED; the graph skips the indexer path when these are null.
+        layer.indexer_k_norm   = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "weight", i), {hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);
+        layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "bias",   i), {hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);
+        layer.indexer_proj     = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ,     "weight", i), {n_embd, hparams.indexer_n_head}, flags | TENSOR_NOT_REQUIRED);
+        layer.indexer_attn_k   = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K,   "weight", i), {n_embd, hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);
+        layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);
         if (i < (int) hparams.n_layer_dense_lead) {
             layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, flags);
             layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);