diff --git a/src/llama-build-context.cpp b/src/llama-build-context.cpp
index 4615605c..8728507d 100644
--- a/src/llama-build-context.cpp
+++ b/src/llama-build-context.cpp
@@ -8178,121 +8178,150 @@ ggml_cgraph * llm_build_context::build_ernie4_5_moe() {
 
     GGML_ASSERT(hparams.n_moe_layer_step > 0 && "Ernie 4.5 MoE requires n_moe_layer_step > 0");
     for (int il = 0; il < n_layer; ++il) {
-        ggml_tensor * inpSA = inpL;
-        // norm
-        // Pre-attention norm
-        cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
-        cb(cur, "attn_norm", il);
+        //ggml_tensor * inpSA = inpL;
 
-        // self-attention
-        {
-            // Q, K, V projections
-            ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
-            cb(Qcur, "Qcur", il);
-            if (model.layers[il].bq) {
-                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
-                cb(Qcur, "Qcur", il);
-            }
+        cur = build_std_attention(gf, model.layers[il].attn_norm, inpL, inp_pos, nullptr, KQ_mask, nullptr, nullptr,
+                    1.0f/sqrtf(float(n_embd_head)), 0.0f, 0, il, true, false, true);
+
+        //// norm
+        //// Pre-attention norm
+        //cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
+        //cb(cur, "attn_norm", il);
+
+        //// self-attention
+        //{
+        //    // Q, K, V projections
+        //    ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+        //    cb(Qcur, "Qcur", il);
+        //    if (model.layers[il].bq) {
+        //        Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+        //        cb(Qcur, "Qcur", il);
+        //    }
 
 
-            ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
-            cb(Kcur, "Kcur", il);
-            if (model.layers[il].bk) {
-                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
-                cb(Kcur, "Kcur", il);
-            }
-            cb(Kcur, "Kcur", il);
+        //    ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+        //    cb(Kcur, "Kcur", il);
+        //    if (model.layers[il].bk) {
+        //        Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+        //        cb(Kcur, "Kcur", il);
+        //    }
+        //    cb(Kcur, "Kcur", il);
 
-            ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
-            cb(Vcur, "Vcur", il);
-            if (model.layers[il].bv) {
-                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
-                cb(Vcur, "Vcur", il);
-            }
+        //    ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+        //    cb(Vcur, "Vcur", il);
+        //    if (model.layers[il].bv) {
+        //        Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+        //        cb(Vcur, "Vcur", il);
+        //    }
 
 
-            // reshape for multi-head
-            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
-            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-            // Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+        //    // reshape for multi-head
+        //    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+        //    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+        //    // Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
-            // apply RoPE
-            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);
-            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(Qcur, "Qcur", il);
-            cb(Kcur, "Kcur", il);
-            cb(Vcur, "Vcur", il);
+        //    // apply RoPE
+        //    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);
+        //    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(Qcur, "Qcur", il);
+        //    cb(Kcur, "Kcur", il);
+        //    cb(Vcur, "Vcur", il);
 
-            cur = llm_build_kv(ctx0, lctx, kv_self, gf,
-                    model.layers[il].wo, NULL,
-                    Kcur, Vcur, Qcur, KQ_mask,
-                    n_tokens, kv_head, n_kv,
-                    1.0f / sqrtf(float(n_embd_head)), cb, il);
-        }
+        //    cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+        //            model.layers[il].wo, NULL,
+        //            Kcur, Vcur, Qcur, KQ_mask,
+        //            n_tokens, kv_head, n_kv,
+        //            1.0f / sqrtf(float(n_embd_head)), cb, il);
+        //}
 
         if (il == n_layer - 1 && inp_out_ids) {
             cur = ggml_get_rows(ctx0, cur, inp_out_ids);
-            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            //inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
 
-        // residual connection for attention output
-        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-        cb(ffn_inp, "ffn_inp", il);
+        //// residual connection for attention output
+        //ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        //cb(ffn_inp, "ffn_inp", il);
 
         // feed-forward network
         bool is_moe_layer = static_cast<uint32_t>(il) >= hparams.n_layer_dense_lead && (il + 1) % hparams.n_moe_layer_step == 0;
 
         if (!is_moe_layer) {
-            cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, ffn_inp,
-                    model.layers[il].ffn_up, NULL, NULL,
+            // dense FFN
+            cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
                     model.layers[il].ffn_gate, NULL, NULL,
                     model.layers[il].ffn_down, NULL, NULL,
                     NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il, gf, true);
             cb(cur, "ffn_out", il);
-        }
-        else {
-            // MoE branch
-            cur = llm_build_norm(ctx0, ffn_inp, hparams, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, cb, il);
-            cb(cur, "ffn_norm", il);
-
-            ggml_tensor * moe_out = llm_build_moe_ffn(ctx0, lctx, cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
+        } else {
+            cur = llm_build_std_moe_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
+                    model.layers[il].ffn_gate_inp,  model.layers[il].ffn_gate_inp_b,
+                    model.layers[il].ffn_up_exps,   model.layers[il].ffn_up_exps_b,
+                    model.layers[il].ffn_gate_exps, model.layers[il].ffn_gate_exps_b,
+                    model.layers[il].ffn_down_exps, model.layers[il].ffn_down_exps_b,
                     model.layers[il].ffn_exp_probs_b,
+                    model.layers[il].ffn_up_shexp,    nullptr, // we don't have shared expert biases?
+                    model.layers[il].ffn_gate_shexp,  nullptr,
+                    model.layers[il].ffn_down_shexp,  nullptr,
                     n_expert, n_expert_used,
-                    LLM_FFN_SILU, true,
-                    false, 0.0,
+                    LLM_FFN_SILU, true, false, 0.0f,
                     LLM_EXPERT_GATING_FUNC_SOFTMAX,
-                    cb, il, gf);
-            cb(moe_out, "ffn_moe_out", il);
-
-            // Shared expert (if present)
-            if (hparams.n_ff_shexp > 0) {
-                ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, nullptr, cur,
-                        model.layers[il].ffn_up_shexp, NULL, NULL,
-                        model.layers[il].ffn_gate_shexp, NULL, NULL,
-                        model.layers[il].ffn_down_shexp, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
-                cb(ffn_shexp, "ffn_shexp", il);
-
-                cur = ggml_add(ctx0, moe_out, ffn_shexp);
-            }
-            else {
-                cur = moe_out;
-            }
-            cb(cur, "ffn_out", il);
+                    LLM_FFN_SILU, cb, il, gf, true);
         }
 
-        cur = ggml_add(ctx0, cur, ffn_inp);
-        cb(cur, "ffn_out", il);
+        //if (!is_moe_layer) {
+        //    cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, ffn_inp,
+        //            model.layers[il].ffn_up, NULL, NULL,
+        //            model.layers[il].ffn_gate, NULL, NULL,
+        //            model.layers[il].ffn_down, NULL, NULL,
+        //            NULL,
+        //            LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+        //    cb(cur, "ffn_out", il);
+        //}
+        //else {
+        //    // MoE branch
+        //    cur = llm_build_norm(ctx0, ffn_inp, hparams, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, cb, il);
+        //    cb(cur, "ffn_norm", il);
+
+        //    ggml_tensor * moe_out = llm_build_moe_ffn(ctx0, lctx, cur,
+        //            model.layers[il].ffn_gate_inp,
+        //            model.layers[il].ffn_up_exps,
+        //            model.layers[il].ffn_gate_exps,
+        //            model.layers[il].ffn_down_exps,
+        //            model.layers[il].ffn_exp_probs_b,
+        //            n_expert, n_expert_used,
+        //            LLM_FFN_SILU, true,
+        //            false, 0.0,
+        //            LLM_EXPERT_GATING_FUNC_SOFTMAX,
+        //            cb, il, gf);
+        //    cb(moe_out, "ffn_moe_out", il);
+
+        //    // Shared expert (if present)
+        //    if (hparams.n_ff_shexp > 0) {
+        //        ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, nullptr, cur,
+        //                model.layers[il].ffn_up_shexp, NULL, NULL,
+        //                model.layers[il].ffn_gate_shexp, NULL, NULL,
+        //                model.layers[il].ffn_down_shexp, NULL, NULL,
+        //                NULL,
+        //                LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+        //        cb(ffn_shexp, "ffn_shexp", il);
+
+        //        cur = ggml_add(ctx0, moe_out, ffn_shexp);
+        //    }
+        //    else {
+        //        cur = moe_out;
+        //    }
+        //    cb(cur, "ffn_out", il);
+        //}
+
+        //cur = ggml_add(ctx0, cur, ffn_inp);
+        //cb(cur, "ffn_out", il);
 
         cur = lctx.cvec.apply_to(ctx0, cur, il);
         cb(cur, "l_out", il);
@@ -8301,15 +8330,18 @@ ggml_cgraph * llm_build_context::build_ernie4_5_moe() {
         inpL = cur;
     }
 
-    cur = inpL;
-
-    cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
-
-    cb(cur, "result_norm", -1);
-    // lm_head
-    cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
-
+    cur = build_output(lctx, ctx0, inpL, model.output, model.output_norm, cb);
     cb(cur, "result_output", -1);
+
+    //cur = inpL;
+
+    //cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
+
+    //cb(cur, "result_norm", -1);
+    //// lm_head
+    //cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+
+    //cb(cur, "result_output", -1);
     ggml_build_forward_expand(gf, cur);
     return gf;
 }
diff --git a/src/llama-load-tensors.cpp b/src/llama-load-tensors.cpp
index bc7e70e7..72f058c3 100644
--- a/src/llama-load-tensors.cpp
+++ b/src/llama-load-tensors.cpp
@@ -2490,10 +2490,9 @@ bool create_tensors_helper::create_ernie45_tensors(const LLM_TN & tn) {
 
     for (int i = 0; i < n_layer; ++i) {
         auto& layer = model.layers[i];
-        ggml_context* ctx_layer = ctx_for_layer(i);
         ggml_context* ctx_split = ctx_for_layer_split(i);
 
-        layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+        layer.attn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
 
         layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
         layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
@@ -2501,18 +2500,18 @@ bool create_tensors_helper::create_ernie45_tensors(const LLM_TN & tn) {
         layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
 
         // optional bias tensors
-        layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED);
-        layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), { n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
-        layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), { n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
-        layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED);
+        layer.bq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "bias", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED);
+        layer.bk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "bias", i), { n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
+        layer.bv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "bias", i), { n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
+        layer.bo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-        layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
+        layer.ffn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
 
         if (model.arch == LLM_ARCH_ERNIE4_5_MOE && static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) { // MoE layers
             int n_ff_exp = hparams.n_ff_exp;
 
-            layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
-            layer.ffn_exp_probs_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, llama_model_loader::TENSOR_NOT_REQUIRED);
+            layer.ffn_gate_inp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
+            layer.ffn_exp_probs_b = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, llama_model_loader::TENSOR_NOT_REQUIRED);
             layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd,   n_ff_exp, n_expert }, llama_model_loader::TENSOR_NOT_REQUIRED);
             layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
             layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd,   n_ff_exp, n_expert }, 0);