diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 122aba633..79ddb1b33 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -330,8 +330,6 @@ struct clip_ctx {
     float image_std[3];
     bool use_gelu = false;
     bool use_silu = false;
-    bool use_glu_mlp = false;
-    bool use_rms_norm = false;
     int32_t ftype = 1;
 
     gguf_context_ptr ctx_gguf;
@@ -847,7 +845,6 @@ static ggml_cgraph * clip_image_build_graph_qwen2_5_vl(clip_ctx * ctx, const cli
         inp = ggml_add(ctx0, inp, model.patch_bias);
     }
     struct ggml_tensor * embeddings     = inp;
-    struct ggml_tensor * pos_embed      = nullptr;
     struct ggml_tensor * window_mask    = nullptr;
     struct ggml_tensor * window_idx     = nullptr;
     struct ggml_tensor * inv_window_idx = nullptr;
@@ -858,17 +855,10 @@ static ggml_cgraph * clip_image_build_graph_qwen2_5_vl(clip_ctx * ctx, const cli
 
     // pre-layernorm
     if (model.pre_ln_w) {
-        if (ctx->use_rms_norm) {
-            embeddings = ggml_rms_norm(ctx0, embeddings, eps);
-            ggml_set_name(embeddings, "pre_ln");
+        embeddings = ggml_rms_norm(ctx0, embeddings, eps);
+        ggml_set_name(embeddings, "pre_ln");
 
-            embeddings = ggml_mul(ctx0, embeddings, model.pre_ln_w);
-        } else {
-            embeddings = ggml_norm(ctx0, embeddings, eps);
-            ggml_set_name(embeddings, "pre_ln");
-
-            embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b);
-        }
+        embeddings = ggml_mul(ctx0, embeddings, model.pre_ln_w);
     }
 
     std::vector<struct ggml_tensor *> embedding_stack;
@@ -991,17 +981,10 @@ static ggml_cgraph * clip_image_build_graph_qwen2_5_vl(clip_ctx * ctx, const cli
 
     // post-layernorm
     if (model.post_ln_w) {
-        if (ctx->use_rms_norm) {
-            embeddings = ggml_rms_norm(ctx0, embeddings, eps);
-            ggml_set_name(embeddings, "post_ln");
+        embeddings = ggml_rms_norm(ctx0, embeddings, eps);
+        ggml_set_name(embeddings, "post_ln");
 
-            embeddings = ggml_mul(ctx0, embeddings, model.post_ln_w);
-        } else {
-            embeddings = ggml_norm(ctx0, embeddings, eps);
-            ggml_set_name(embeddings, "post_ln");
-
-            embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
-        }
+        embeddings = ggml_mul(ctx0, embeddings, model.post_ln_w);
     }
 
     // final layer is a vision feature layer
@@ -1086,7 +1069,6 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
     const int n_head               = hparams.n_head;
     const int d_head               = hidden_size / n_head;
     const float eps                = hparams.eps;
-    const bool use_window_attn     = hparams.full_attn_layers.size() > 0;
     int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
 
     const int batch_size = imgs.entries.size();
@@ -1118,7 +1100,6 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
 
         auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
         inp = ggml_add(ctx0, inp, inp_1);
-
         inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3));  // [w, h, c, b] -> [c, w, h, b]
         inp = ggml_reshape_4d(
             ctx0, inp,
@@ -1140,11 +1121,8 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
         // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
         inp = ggml_add(ctx0, inp, model.patch_bias);
     }
-    struct ggml_tensor * embeddings     = inp;
-    struct ggml_tensor * pos_embed      = nullptr;
-    struct ggml_tensor * window_mask    = nullptr;
-    struct ggml_tensor * window_idx     = nullptr;
-    struct ggml_tensor * inv_window_idx = nullptr;
+    struct ggml_tensor * embeddings = inp;
+    struct ggml_tensor * pos_embed = nullptr;
 
     if (ctx->has_llava_projector) {
         // concat class_embeddings and patch_embeddings
@@ -1186,40 +1164,16 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
 
     // pre-layernorm
     if (model.pre_ln_w) {
-        if (ctx->use_rms_norm) {
-            embeddings = ggml_rms_norm(ctx0, embeddings, eps);
-            ggml_set_name(embeddings, "pre_ln");
+        embeddings = ggml_norm(ctx0, embeddings, eps);
+        ggml_set_name(embeddings, "pre_ln");
 
-            embeddings = ggml_mul(ctx0, embeddings, model.pre_ln_w);
-        } else {
-            embeddings = ggml_norm(ctx0, embeddings, eps);
-            ggml_set_name(embeddings, "pre_ln");
-
-            embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b);
-        }
+        embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b);
     }
 
     std::vector<struct ggml_tensor *> embedding_stack;
     const auto & vision_feature_layer = hparams.vision_feature_layer;
 
     // loop over layers
-
-    if (use_window_attn) {
-        inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4);
-        ggml_set_name(inv_window_idx, "inv_window_idx");
-        ggml_set_input(inv_window_idx);
-        // mask for window attention
-        window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, num_positions, num_positions);
-        ggml_set_name(window_mask, "window_mask");
-        ggml_set_input(window_mask);
-
-        // embeddings shape: [hidden_size, patches_w * patches_h, batch_size]
-        GGML_ASSERT(batch_size == 1);
-        embeddings = ggml_reshape_2d(ctx0, embeddings, hidden_size * 4, patches_w * patches_h * batch_size / 4);
-        embeddings = ggml_get_rows(ctx0, embeddings, inv_window_idx);
-        embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size, patches_w * patches_h, batch_size);
-    }
-
     for (int il = 0; il < ctx->max_feature_layer; il++) {
         struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
 
@@ -1232,12 +1186,9 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
         //const size_t nb_q_w = model.layers[il].q_w->nb[0];
 
         // layernorm1
-        if (ctx->use_rms_norm) {
-            cur = ggml_rms_norm(ctx0, cur, eps);
-            cur = ggml_mul(ctx0, cur, model.layers[il].ln_1_w);
-        }
-        else {
+        {
             cur = ggml_norm(ctx0, cur, eps);
+
             cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w),
                            model.layers[il].ln_1_b);
         }
@@ -1277,14 +1228,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
             V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);
 
             struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-            const bool inlist = std::find(hparams.full_attn_layers.begin(), hparams.full_attn_layers.end(), il) != hparams.full_attn_layers.end();
-            const bool full_attn = use_window_attn ? inlist : true;
-            if (full_attn) {
-                KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f);
-            } else {
-                KQ = ggml_soft_max_ext(ctx0, KQ, window_mask, 1.0f / sqrtf((float)d_head), 0.0f);
-            }
-
+            KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f);
             struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
             KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size);
             KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
@@ -1301,50 +1245,25 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
         embeddings = cur; // embeddings = residual, cur = hidden_states
 
         // layernorm2
-        if (ctx->use_rms_norm) {
-            cur = ggml_rms_norm(ctx0, cur, eps);
-            cur = ggml_mul(ctx0, cur, model.layers[il].ln_2_w);
-        } else {
+        {
             cur = ggml_norm(ctx0, cur, eps);
+
             cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b);
         }
 
-        // mlp
-        if (ctx->use_glu_mlp) {
-            // ffn_up
-            auto cur_up = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
-            cur_up = ggml_add(ctx0, cur_up, model.layers[il].ff_o_b);
+        cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
+        cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
 
-            auto cur_gate = ggml_mul_mat(ctx0, model.layers[il].ff_g_w, cur);
-            cur_gate = ggml_add(ctx0, cur_gate, model.layers[il].ff_g_b);
-            if (ctx->use_gelu) {
-                cur_gate = ggml_gelu_inplace(ctx0, cur_gate);
-            } else if (ctx->use_silu) {
-                cur_gate = ggml_silu_inplace(ctx0, cur_gate);
-            } else {
-                cur_gate = ggml_gelu_quick_inplace(ctx0, cur_gate);
-            }
-            cur = ggml_mul(ctx0, cur_gate, cur_up);
-
-            // ffn_down
-            cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
-            cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
+        if (ctx->use_gelu) {
+            cur = ggml_gelu_inplace(ctx0, cur);
+        } else if (ctx->use_silu) {
+            cur = ggml_silu_inplace(ctx0, cur);
+        } else {
+            cur = ggml_gelu_quick_inplace(ctx0, cur);
         }
-        else {
-            cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
-            cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
 
-            if (ctx->use_gelu) {
-                cur = ggml_gelu_inplace(ctx0, cur);
-            } else if (ctx->use_silu) {
-                cur = ggml_silu_inplace(ctx0, cur);
-            } else {
-                cur = ggml_gelu_quick_inplace(ctx0, cur);
-            }
-
-            cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
-            cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
-        }
+        cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
+        cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
 
         // residual 2
         cur = ggml_add(ctx0, embeddings, cur);
@@ -1354,17 +1273,10 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
 
     // post-layernorm
     if (model.post_ln_w) {
-        if (ctx->use_rms_norm) {
-            embeddings = ggml_rms_norm(ctx0, embeddings, eps);
-            ggml_set_name(embeddings, "post_ln");
+        embeddings = ggml_norm(ctx0, embeddings, eps);
+        ggml_set_name(embeddings, "post_ln");
 
-            embeddings = ggml_mul(ctx0, embeddings, model.post_ln_w);
-        } else {
-            embeddings = ggml_norm(ctx0, embeddings, eps);
-            ggml_set_name(embeddings, "post_ln");
-
-            embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
-        }
+        embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
     }
 
     // final layer is a vision feature layer
@@ -1678,18 +1590,6 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
         embeddings = ggml_add(ctx0, embeddings, model.mm_1_b);
     }
 
-    if (use_window_attn) {
-        window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4);
-        ggml_set_name(window_idx, "window_idx");
-        ggml_set_input(window_idx);
-
-        // embeddings shape: [hidden_size, patches_w * patches_h, batch_size]
-        GGML_ASSERT(batch_size == 1);
-        embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4);
-        embeddings = ggml_get_rows(ctx0, embeddings, window_idx);
-        embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4, batch_size);
-    }
-
     // build the graph
     ggml_build_forward_expand(gf, embeddings);
 
@@ -1810,8 +1710,6 @@ struct clip_model_loader {
 
             get_bool(KEY_USE_GELU, ctx_clip.use_gelu, false);
             get_bool(KEY_USE_SILU, ctx_clip.use_silu, false);
-            get_bool(KEY_USE_GLU_MLP, ctx_clip.use_glu_mlp, false);
-            get_bool(KEY_USE_RMS_NORM, ctx_clip.use_rms_norm, false);
 
             get_u32(string_format(KEY_N_EMBD,         "vision"), hparams.hidden_size);
             get_u32(string_format(KEY_N_HEAD,         "vision"), hparams.n_head);
diff --git a/examples/llava/qwen2_vl_surgery.py b/examples/llava/qwen2_vl_surgery.py
index 2345c10d1..f80f25f92 100644
--- a/examples/llava/qwen2_vl_surgery.py
+++ b/examples/llava/qwen2_vl_surgery.py
@@ -152,8 +152,6 @@ def main(args):
         raise ValueError()
 
     if args.model_type == "qwen2.5vl":
-        fout.add_bool("clip.use_glu_mlp", True)  # gate linear unit MLP layer in vision model
-        fout.add_bool("clip.use_rms_norm", True)
         fout.add_array("clip.vision.fullatt_block_indexes", vcfg.fullatt_block_indexes)
         fout.add_uint32("clip.vision.window_size", vcfg.window_size)
         fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.hidden_size)