diff --git a/src/llama.cpp b/src/llama.cpp
index 614483192..4fd2d4145 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -15769,44 +15769,64 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
         }
     } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
                ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M    || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M ||
-               ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS) {
+               ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS) { // TODO: Possibly spread this tree of FTYPES into its respective tensors categories.
         if (name.find("attn_v.weight") != std::string::npos) {
-            if      (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_IQ4_K;
-            else if (qs.model.hparams.n_gqa() >= 2 || qs.model.hparams.n_expert >= 2) new_type = GGML_TYPE_IQ3_K;
+            if      (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 2) new_type = GGML_TYPE_IQ4_K;
+            else if (qs.model.hparams.n_gqa() >= 2) new_type = GGML_TYPE_IQ3_K;
             else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
             ++qs.i_attention_wv;
         }
-        else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) {
-            new_type = GGML_TYPE_Q4_K;
+        else if (qs.model.hparams.n_expert >= 2 && name.find("attn_k.weight") != std::string::npos) {
+            new_type = GGML_TYPE_IQ4_KS;
+        }
+        else if (qs.model.hparams.n_expert >= 2 && name.find("attn_q.weight") != std::string::npos) {
+            new_type = GGML_TYPE_IQ3_XXS;
         }
         else if (name.find("attn_qkv.weight") != std::string::npos) {
             new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_XXS : GGML_TYPE_IQ2_K;
         }
         else if (name.find("ffn_down") != std::string::npos) {
-            if (qs.i_ffn_down < qs.n_ffn_down/8) {
-                new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+            if (qs.i_ffn_down < qs.n_ffn_down/4 || qs.i_ffn_down >= 7*qs.n_ffn_down/8) {
+                new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_XXS : GGML_TYPE_IQ2_S;
             }
             ++qs.i_ffn_down;
         }
         else if (name.find("attn_output.weight") != std::string::npos) {
-            if (qs.model.hparams.n_expert == 8) {
+            if (qs.model.hparams.n_expert >= 4) {
                 new_type = GGML_TYPE_Q5_K;
+            }
+            else if (qs.model.hparams.n_expert >= 2) {
+                new_type = GGML_TYPE_Q4_K;
             } else {
-                if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_K;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
+                if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_KS;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS) new_type = GGML_TYPE_IQ2_XS;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) new_type = GGML_TYPE_IQ2_S;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS) new_type = GGML_TYPE_IQ2_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_XXS;
             }
         }
     } else if (name.find("attn_v.weight") != std::string::npos) {
         if      (qs.params->attn_v_type < GGML_TYPE_COUNT) new_type = qs.params->attn_v_type;
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
-            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
+        else if (qs.model.type == MODEL_70B) {
+            // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
+            // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
+            // nearly negligible increase in model size by quantizing this tensor with more bits:
+            if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
+        }
+        else if (qs.model.hparams.n_expert >= 4) {
+            // for the 8-expert model, bumping this to Q8_0 trades just ~128MB
+            // TODO: explore better strategies
+            new_type = GGML_TYPE_Q8_0;
+        }
+        else if (qs.model.hparams.n_expert >= 2 || new_type != GGML_TYPE_IQ6_K) {
+            new_type = GGML_TYPE_Q6_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
+            new_type = qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_K) {
             new_type = qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ4_K : GGML_TYPE_IQ3_K;
         }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
-            new_type = GGML_TYPE_Q4_K;
-        }
         else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
             new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ3_K
                      : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
@@ -15840,19 +15860,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) {
             if (qs.model.hparams.n_vocab >= 127999 && (qs.model.type == MODEL_8B || qs.model.type == MODEL_70B))
                 new_type = GGML_TYPE_Q6_K;
-        } 
-        if (qs.model.type == MODEL_70B) {
-            // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
-            // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
-            // nearly negligible increase in model size by quantizing this tensor with more bits:
-            if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
-        }
-        if (qs.model.hparams.n_expert == 8) {
-            // for the 8-expert model, bumping this to Q8_0 trades just ~128MB
-            // TODO: explore better strategies
-            new_type = GGML_TYPE_Q8_0;
         }
-        else if (qs.model.hparams.n_gqa() >= 4) {
+        else if (qs.model.hparams.n_gqa() >= 4) { // TODO: Possibly reintegrate this into FTYPE conditionality
             if      (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
             else if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_IQ3_S  ) new_type = GGML_TYPE_Q4_K;
             else if (new_type == GGML_TYPE_Q4_K || new_type == GGML_TYPE_IQ4_XS) new_type = GGML_TYPE_Q5_K;
@@ -15862,23 +15871,29 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
         ++qs.i_attention_wv;
     } else if (name.find("attn_k.weight") != std::string::npos) {
         if (qs.params->attn_k_type < GGML_TYPE_COUNT) new_type = qs.params->attn_k_type;
-        else if (qs.model.hparams.n_expert == 8) {
+        else if (qs.model.hparams.n_expert >= 4) {
             // for the 8-expert model, bumping this to Q8_0 trades just ~128MB
             // TODO: explore better strategies
             new_type = GGML_TYPE_Q8_0;
         }
+        else if (qs.model.hparams.n_expert >= 2 || new_type != GGML_TYPE_IQ6_K) {
+            new_type = GGML_TYPE_Q6_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
+            if (qs.model.hparams.n_gqa() >= 2) new_type =  GGML_TYPE_Q3_K;
+        }
         else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
-            new_type = GGML_TYPE_IQ3_XXS;
+            if (qs.model.hparams.n_gqa() <= 2) new_type = GGML_TYPE_IQ3_XXS;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
-            new_type = GGML_TYPE_IQ2_S;
+            if (qs.model.hparams.n_gqa() <= 2) new_type = GGML_TYPE_IQ2_S;
         }
     } else if (name.find("attn_q.weight") != std::string::npos) {
         if (qs.params->attn_q_type < GGML_TYPE_COUNT) new_type = qs.params->attn_q_type;
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && qs.model.hparams.n_expert <= 2) {
             new_type = GGML_TYPE_IQ3_XXS;
         }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && qs.model.hparams.n_expert <= 2) {
             new_type = GGML_TYPE_IQ2_S;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) {
@@ -15891,7 +15906,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
         if (qs.params->ffn_down_type < GGML_TYPE_COUNT) new_type = qs.params->ffn_down_type;
         else if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
-            if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
+            if (i_layer < n_layer/8 || i_layer >= 7*n_layer/8) new_type = GGML_TYPE_Q3_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
             new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
@@ -15902,7 +15917,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
                      : GGML_TYPE_Q3_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M && (i_layer < n_layer/8 ||
-                    (qs.model.hparams.n_expert == 8 && use_more_bits(i_layer, n_layer)))) {
+                    (qs.model.hparams.n_expert >= 2 && use_more_bits(i_layer, n_layer)))) {
             new_type = GGML_TYPE_IQ4_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
@@ -15938,7 +15953,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
     } else if (name.find("attn_output.weight") != std::string::npos) {
         if (qs.params->attn_output_type < GGML_TYPE_COUNT) new_type = qs.params->attn_output_type;
         else if (arch != LLM_ARCH_FALCON) {
-            if (qs.model.hparams.n_expert >= 8) {
+            if (qs.model.hparams.n_expert >= 2) {
                 if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
                     ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL  ||
                     ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S   ||
@@ -15961,6 +15976,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
     }
     else if (name.find("attn_qkv.weight") != std::string::npos) {
         if (qs.params->attn_qkv_type < GGML_TYPE_COUNT) new_type = qs.params->attn_qkv_type;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
+            new_type = GGML_TYPE_Q3_K;
+        }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
             new_type = GGML_TYPE_Q4_K;
         }