diff --git a/src/llama-build-context.cpp b/src/llama-build-context.cpp
index 277da085..1783b1a7 100644
--- a/src/llama-build-context.cpp
+++ b/src/llama-build-context.cpp
@@ -1721,7 +1721,7 @@ ggml_cgraph * llm_build_context::build_llama() {
 
         // self-attention
         if (use_rope) {
-            cur = build_std_attention(gf, inpL, inp_pos, nullptr, this_KQ_mask, nullptr, kq_scale, hparams.f_attention_scale, this_n_swa, il);
+            cur = build_std_attention(gf, inpL, inp_pos, nullptr, this_KQ_mask, nullptr, nullptr, kq_scale, hparams.f_attention_scale, this_n_swa, il);
         }
         else {
 
@@ -1905,57 +1905,15 @@ ggml_cgraph * llm_build_context::build_mistral3() {
 
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
-    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-    //const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : 1.f;
-
-    // ====================================
-
-    //auto * inp_attn = build_attn_inp_kv();
+    //const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : 1.f;
 
     for (int il = 0; il < n_layer; ++il) {
         ggml_tensor * inpSA = inpL;
 
         auto rope_factors = build_rope_factors(il);
 
-        // self-attention
-        if (!inp_attn_scale) {
-            cur = build_std_attention(gf, inpL, inp_pos, rope_factors, KQ_mask, nullptr, kq_scale, hparams.f_attention_scale, 0, il);
-        }
-        else {
-
-            // norm
-            cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
-            cb(cur, "attn_norm", il);
-
-            auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur,
-                    model.layers[il].wqkv, model.layers[il].bqkv,
-                    model.layers[il].wqk, model.layers[il].bqk,
-                    model.layers[il].wq, model.layers[il].bq,
-                    model.layers[il].wk, model.layers[il].bk,
-                    model.layers[il].wv, model.layers[il].bv,
-                    nullptr, nullptr, hparams.f_attention_scale, il);
-
-            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors,
-                    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, rope_factors,
-                    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);
-
-            if (inp_attn_scale) {
-                Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
-                cb(Qcur, "Qcur_temp_scaled", il);
-            }
-
-            cur = llm_build_kv(ctx0, lctx, kv_self, gf,
-                    model.layers[il].wo, model.layers[il].bo,
-                    Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il, nullptr, 0);
-        }
+        cur = build_std_attention(gf, inpL, inp_pos, rope_factors, KQ_mask, nullptr, inp_attn_scale, kq_scale, hparams.f_attention_scale, 0, il);
 
         if (il == n_layer - 1 && inp_out_ids) {
             cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
@@ -3947,7 +3905,7 @@ ggml_cgraph * llm_build_context::build_qwen3moe() {
         //cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
         //cb(cur, "attn_norm", il);
 
-        cur = build_std_attention(gf, inpL, inp_pos, nullptr, KQ_mask, nullptr, 1.0f/sqrtf(float(n_embd_head)), 0.0f, 0, il);
+        cur = build_std_attention(gf, inpL, inp_pos, nullptr, KQ_mask, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), 0.0f, 0, il);
 
         if (il == n_layer - 1) {
             // skip computing output for unused tokens
@@ -6826,7 +6784,7 @@ ggml_cgraph * llm_build_context::build_glm4_moe() {
 
         // self-attention
         if (rope_cache == nullptr) {
-            cur = build_std_attention(gf, inpL, inp_pos, nullptr, KQ_mask, nullptr, kq_scale, 0.0f, 0, il);
+            cur = build_std_attention(gf, inpL, inp_pos, nullptr, KQ_mask, nullptr, nullptr, kq_scale, 0.0f, 0, il);
         } else {
             // Pre-attention norm
             cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
@@ -9329,7 +9287,7 @@ ggml_cgraph * llm_build_context::llama_build_graph(
 }
 
 ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tensor * input, ggml_tensor * inp_pos, ggml_tensor * rope_factors_in,
-        ggml_tensor * KQ_mask, ggml_tensor * sinks, float KQ_scale, float f_attn_scale, int n_swa, int il) {
+        ggml_tensor * KQ_mask, ggml_tensor * sinks, ggml_tensor * inp_attn_scale, float KQ_scale, float f_attn_scale, int n_swa, int il) {
     if (!model.layers[il].wqkv && !model.layers[il].wqk && cparams.flash_attn &&
          model.layers[il].wq->extra && model.layers[il].wk->extra && model.layers[il].wv->extra && model.layers[il].wo->extra) {
         if (kv_self.k_l[il]->extra && kv_self.v_l[il]->extra) {
@@ -9400,6 +9358,10 @@ ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tens
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Qcur, "Qcur", il_cb);
                 cb(Kcur, "Kcur", il_cb);
+                if (inp_attn_scale) {
+                    Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
+                    cb(Qcur, "Qcur_temp_scaled", il_cb);
+                }
                 ggml_build_forward_expand(gf, Qcur);
                 ggml_build_forward_expand(gf, Kcur);
                 ggml_build_forward_expand(gf, Vcur);
@@ -9528,6 +9490,11 @@ ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tens
     cb(Qcur, "Qcur", il);
     cb(Kcur, "Kcur", il);
 
+    if (inp_attn_scale) {
+        Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
+        cb(Qcur, "Qcur_temp_scaled", il);
+    }
+
     cur = llm_build_kv(ctx0, lctx, kv_self, gf,
             model.layers[il].wo, model.layers[il].bo,
             Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, KQ_scale, cb, il, sinks, n_swa);
diff --git a/src/llama-build-context.h b/src/llama-build-context.h
index 41597248..cbf12817 100644
--- a/src/llama-build-context.h
+++ b/src/llama-build-context.h
@@ -407,6 +407,6 @@ llm_expert_gating_func_type   gating_op,
     static ggml_cgraph * llama_build_graph(llama_context & lctx, const llama_batch & batch, bool worst_case);
 
     ggml_tensor * build_std_attention(ggml_cgraph * gf, ggml_tensor * cur, ggml_tensor * inp_pos, ggml_tensor * rope_factors,
-            ggml_tensor * KQ_mask, ggml_tensor * sinks, float KQ_scale, float f_attn_scale, int n_swa, int il);
+            ggml_tensor * KQ_mask, ggml_tensor * sinks, ggml_tensor * inp_attn_scale, float KQ_scale, float f_attn_scale, int n_swa, int il);
 
 };
diff --git a/src/llama.cpp b/src/llama.cpp
index 1a0480bb..2124b180 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1728,6 +1728,7 @@ static bool is_model_split_supported(const llama_model & model) {
         LLM_ARCH_LLAMA,
         LLM_ARCH_QWEN3MOE,
         LLM_ARCH_GLM4_MOE,
+        LLM_ARCH_MISTRAL3,
     };
     auto it =  k_supported.find(model.arch);
     return it != k_supported.end();