--- a/src/models/qwen4exp.cpp
+++ b/src/models/qwen4exp.cpp
@@ -560,9 +565,13 @@ ggml_tensor * llama_model_qwen4exp::graph::build_attn_qsa(
         ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il));
         ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, v_idxs, il));
     }
 
-    ggml_tensor * kq_mask = inp->get_kq_mask();
+    // get_kq_mask() returns the F16-converted mask when flash attention is
+    // on, but ggml_fill/set_rows require F32. Mirror the MLA sparse path
+    // (llm_graph_context::build_attn): run the top-k mask surgery on the F32
+    // base mask and cast to the attention type only at the end.
+    ggml_tensor * kq_mask = inp->self_kq_mask;
 
     // prepare new kq mask - starts filled with -INFINITY
     ggml_tensor * kq_mask_all = ggml_fill(ctx0, kq_mask, -INFINITY);
 
@@ -592,9 +601,12 @@ ggml_tensor * llama_model_qwen4exp::graph::build_attn_qsa(
     ggml_tensor * q = q_cur;
     ggml_tensor * k = mctx_cur->get_k(ctx0, il);
     ggml_tensor * v = mctx_cur->get_v(ctx0, il);
 
-    ggml_tensor * cur = build_attn_mha(q, k, v, nullptr, kq_mask_top_k, nullptr, nullptr, kq_scale, il);
+    // flash attention consumes an F16 mask; softmax consumes F32 as-is
+    ggml_tensor * kq_mask_attn = inp->cparams.flash_attn ? ggml_cast(ctx0, kq_mask_top_k, GGML_TYPE_F16) : kq_mask_top_k;
+
+    ggml_tensor * cur = build_attn_mha(q, k, v, nullptr, kq_mask_attn, nullptr, nullptr, kq_scale, il);
     cb(cur, "kqv_out", il);
 
     return cur;
 }