diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h
index 92cf739e7a..e2eecc5970 100644
--- a/ggml/src/ggml-common.h
+++ b/ggml/src/ggml-common.h
@@ -389,8 +389,8 @@ typedef struct {
 } block_iq3_xxs;
 static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_half) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
 
-// 3.4375 bpw
 #define IQ3S_N_SCALE QK_K/64
+// 3.4375 bpw
 typedef struct {
     ggml_half d;
     uint8_t qs[QK_K/4];
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 8e8ce23124..872f60059e 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -25,15 +25,28 @@ struct tensor_type_option {
 enum class tensor_category {
     TOKEN_EMBD,
     ATTENTION_Q,
+    ATTENTION_Q_A,
+    ATTENTION_Q_B,
+    ATTENTION_K_B,
+    ATTENTION_V_B,
     ATTENTION_V,
     ATTENTION_K,
     ATTENTION_QKV,
     ATTENTION_KV_B,
+    ATTENTION_KV_A_MQA,
     ATTENTION_OUTPUT,
     FFN_UP,
     FFN_GATE,
     FFN_DOWN,
     OUTPUT,
+    FFN_UP_EXPS,
+    FFN_DOWN_EXPS,
+    FFN_GATE_EXPS,
+    FFN_UP_SHEXP,
+    FFN_DOWN_SHEXP,
+    FFN_GATE_SHEXP,
+    SSM_OUT,
+    SSM_BA,
     OTHER
 };
 
@@ -135,9 +148,57 @@ static tensor_category tensor_get_category(const std::string & tensor_name) {
     if (tensor_name.find("attn_q.weight") != std::string::npos) {
         return tensor_category::ATTENTION_Q;
     }
+    if (tensor_name.find("attn_kv_a_mqa.weight") != std::string::npos) {
+        return tensor_category::ATTENTION_KV_A_MQA;
+    }
+    if (tensor_name.find("attn_q_a.weight") != std::string::npos) {
+        return tensor_category::ATTENTION_Q_A;
+    }
+    if (tensor_name.find("attn_q_b.weight") != std::string::npos) {
+        return tensor_category::ATTENTION_Q_B;
+    }
+    if (tensor_name.find("attn_k_b.weight") != std::string::npos) {
+        return tensor_category::ATTENTION_K_B;
+    }
+    if (tensor_name.find("attn_v_b.weight") != std::string::npos) {
+        return tensor_category::ATTENTION_V_B;
+    }
     if (tensor_name.find("attn_output.weight") != std::string::npos) {
         return tensor_category::ATTENTION_OUTPUT;
     }
+    if (tensor_name.find("ffn_down_exps") != std::string::npos) {
+        return tensor_category::FFN_DOWN_EXPS;
+    }
+    if (tensor_name.find("ffn_gate_exps") != std::string::npos) {
+        return tensor_category::FFN_GATE_EXPS;
+    }
+    if (tensor_name.find("ffn_up_exps") != std::string::npos) {
+        return tensor_category::FFN_UP_EXPS;
+    }
+    if (tensor_name.find("ffn_down_shexp") != std::string::npos) {
+        return tensor_category::FFN_DOWN_SHEXP;
+    }
+    if (tensor_name.find("ffn_gate_shexp") != std::string::npos) {
+        return tensor_category::FFN_GATE_SHEXP;
+    }
+    if (tensor_name.find("ffn_up_shexp") != std::string::npos) {
+        return tensor_category::FFN_UP_SHEXP;
+    }
+    if (tensor_name.find("attn_output.weight") != std::string::npos) {
+        return tensor_category::ATTENTION_OUTPUT;
+    }
+    if (tensor_name.find("ssm_out.weight") != std::string::npos) {
+        return tensor_category::SSM_OUT;
+    }
+    if (tensor_name.find("ssm_ba.weight") != std::string::npos) {
+        return tensor_category::SSM_BA;
+    }
+    if (tensor_name.find("ssm_alpha.weight") != std::string::npos) {
+        return tensor_category::SSM_BA;
+    }
+    if (tensor_name.find("ssm_beta.weight") != std::string::npos) {
+        return tensor_category::SSM_BA;
+    }
     if (tensor_name.find("ffn_up") != std::string::npos) {
         return tensor_category::FFN_UP;
     }
@@ -153,7 +214,6 @@ static tensor_category tensor_get_category(const std::string & tensor_name) {
 // check if category is for attention-v-like tensors (more sensitive to quantization)
 static bool category_is_attn_v(tensor_category cat) {
     return cat == tensor_category::ATTENTION_V     ||
-           cat == tensor_category::ATTENTION_QKV   ||
            cat == tensor_category::ATTENTION_KV_B;
 }
 
@@ -165,14 +225,32 @@ struct quantize_state_impl {
     const llama_model                 & model;
     const llama_model_quantize_params * params;
 
-    int n_attention_wv = 0;
-    int n_ffn_down     = 0;
-    int n_ffn_gate     = 0;
-    int n_ffn_up       = 0;
-    int i_attention_wv = 0;
-    int i_ffn_down     = 0;
-    int i_ffn_gate     = 0;
-    int i_ffn_up       = 0;
+    int n_attention_wv   = 0;
+    int n_attn_qkv       = 0;
+    int n_ffn_down       = 0;
+    int n_ffn_gate       = 0;
+    int n_ffn_up         = 0;
+    int n_ffn_down_exps  = 0;
+    int n_ffn_gate_exps  = 0;
+    int n_ffn_up_exps    = 0;
+    int n_ffn_down_shexp = 0;
+    int n_ffn_gate_shexp = 0;
+    int n_ffn_up_shexp   = 0;
+    int n_ssm_out        = 0;
+    int n_attn_q         = 0;
+    int i_attn_qkv       = 0;
+    int i_attention_wv   = 0;
+    int i_ffn_down       = 0;
+    int i_ffn_gate       = 0;
+    int i_ffn_up         = 0;
+    int i_ffn_down_exps  = 0;
+    int i_ffn_gate_exps  = 0;
+    int i_ffn_up_exps    = 0;
+    int i_ffn_down_shexp = 0;
+    int i_ffn_gate_shexp = 0;
+    int i_ffn_up_shexp   = 0;
+    int i_ssm_out        = 0;
+    int i_attn_q         = 0;
 
     int n_fallback    = 0;
 
@@ -402,6 +480,65 @@ static ggml_type tensor_type_fallback(quantize_state_impl & qs, const ggml_tenso
     return return_type;
 }
 
+// Returns the appropriate type for expert _exps tensors based on ftype
+static inline ggml_type get_exps_type_low_bpw_bump(llama_ftype ftype, ggml_type new_type) {
+    if      (ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S) new_type = GGML_TYPE_IQ3_XXS;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) new_type = GGML_TYPE_IQ2_S;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS) new_type = GGML_TYPE_IQ2_XS;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) new_type = GGML_TYPE_IQ1_M;
+    return new_type;
+}
+
+static inline ggml_type get_exps_type_low_bpw_squash(llama_ftype ftype, ggml_type new_type) {
+    if      (ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ2_XS;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S) new_type = GGML_TYPE_IQ2_XXS;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) new_type = GGML_TYPE_IQ2_XXS;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS) new_type = GGML_TYPE_IQ1_M;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ1_S;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) new_type = GGML_TYPE_IQ1_S;
+    return new_type;
+}
+
+static inline ggml_type get_exps_type_high_bpw_bump(llama_ftype ftype, ggml_type new_type, bool has_imatrix) {
+    if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K)   new_type = GGML_TYPE_Q3_K;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S) new_type = GGML_TYPE_Q5_K;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q6_K;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_Q6_K)   new_type = GGML_TYPE_Q8_0;
+    // Bump I-quants
+    else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) new_type = GGML_TYPE_Q5_K;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_Q4_K;
+    else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !has_imatrix) new_type = GGML_TYPE_Q6_K;
+
+    return new_type;
+}
+
+static inline ggml_type get_ffn_shexp_ggml_type(llama_ftype ftype, ggml_type new_type) {
+    if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S   ||
+        ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
+        ftype == LLAMA_FTYPE_MOSTLY_Q2_K   || ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
+        new_type = GGML_TYPE_Q4_K;
+    }
+    else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_0) new_type = GGML_TYPE_Q5_0;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_1) new_type = GGML_TYPE_Q5_1;
+    else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ||
+             ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) {
+        new_type = GGML_TYPE_Q5_K;
+    }
+    else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) {
+        new_type = GGML_TYPE_Q6_K;
+    }
+    else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q6_K   ||
+        ftype == LLAMA_FTYPE_MOSTLY_Q8_0) {
+        new_type = GGML_TYPE_Q8_0;
+    }
+    return new_type;
+}
+
 // internal standard logic for selecting the target tensor type based on tensor category, ftype, and model arch
 static ggml_type llama_tensor_get_type_impl(quantize_state_impl & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype, tensor_category category) {
     const std::string name = ggml_get_name(tensor);
@@ -453,6 +590,8 @@ static ggml_type llama_tensor_get_type_impl(quantize_state_impl & qs, ggml_type
                 new_type = GGML_TYPE_Q6_K;
             }
         }
+    } else if (category == tensor_category::SSM_BA) {
+        new_type = GGML_TYPE_F32;
     } else if (ftype == LLAMA_FTYPE_MOSTLY_MXFP4_MOE) {
         // MoE   tensors -> MXFP4
         // other tensors -> Q8_0
@@ -482,33 +621,136 @@ static ggml_type llama_tensor_get_type_impl(quantize_state_impl & qs, ggml_type
     } 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) {
         if (category_is_attn_v(category)) {
-            if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
-            else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+            if (use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) {
+                new_type = GGML_TYPE_Q6_K;
+            }
+            else {
+                new_type = GGML_TYPE_Q4_K;
+            }
             ++qs.i_attention_wv;
         }
-        else if (qs.model.hparams.n_expert == 8 && category == tensor_category::ATTENTION_K) {
+        else if (category == tensor_category::ATTENTION_QKV) {
+            if (qs.model.hparams.n_expert >= 8) {
+                if (use_more_bits(qs.i_attn_qkv, qs.n_attn_qkv)) {
+                    new_type = GGML_TYPE_Q6_K;
+                }
+                else {
+                    new_type = GGML_TYPE_Q4_K;
+                }
+            }
+            else if (use_more_bits(qs.i_attn_qkv, qs.n_attn_qkv)) {
+                new_type = GGML_TYPE_Q4_K;
+            }
+            ++qs.i_attn_qkv;
+        }
+        else if (category == tensor_category::SSM_OUT) {
+            if (use_more_bits(qs.i_ssm_out, qs.n_ssm_out)) {
+                new_type = GGML_TYPE_Q4_K;
+            }
+            ++qs.i_ssm_out;
+        }
+        else if (category == tensor_category::ATTENTION_K) {
             new_type = GGML_TYPE_Q4_K;
         }
+        else if (category == tensor_category::ATTENTION_Q) {
+            if (qs.model.hparams.n_expert >= 8 || use_more_bits(qs.i_attn_q, qs.n_attn_q)){
+                new_type = GGML_TYPE_Q4_K;
+            }
+        }
+        else if (qs.model.hparams.n_expert >= 8 && category == tensor_category::ATTENTION_KV_A_MQA) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (qs.model.hparams.n_expert >= 8 && category == tensor_category::ATTENTION_KV_B) {
+            if (qs.i_attention_wv < qs.n_attention_wv/8) {
+                new_type = GGML_TYPE_Q4_K;
+            }
+            else if (use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) {
+                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 (category == tensor_category::ATTENTION_Q_A) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (category == tensor_category::ATTENTION_Q_B) {
+            new_type = (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+        }
         else if (category == tensor_category::FFN_DOWN) {
-            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/16) {
+                new_type = GGML_TYPE_Q6_K;
+            }
+            else 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;
             }
             ++qs.i_ffn_down;
         }
+        else if (category == tensor_category::FFN_GATE) {
+            if (qs.i_ffn_gate < qs.n_ffn_gate/16) {
+                new_type = GGML_TYPE_Q4_K;
+            }
+            else if (qs.i_ffn_gate < qs.n_ffn_gate/8) {
+                new_type = (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+            }
+            ++qs.i_ffn_gate;
+        }
+        else if (category == tensor_category::FFN_UP) {
+            if (qs.i_ffn_up < qs.n_ffn_up/16) {
+                new_type = GGML_TYPE_Q4_K;
+            }
+            else if (qs.i_ffn_up < qs.n_ffn_up/8) {
+                new_type = (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+            }
+            ++qs.i_ffn_up;
+        }
+        else if (category == tensor_category::FFN_DOWN_EXPS) {
+            if (qs.i_ffn_down_exps < qs.n_ffn_down_exps/8 || qs.i_ffn_down_exps > 7*qs.n_ffn_down_exps/8) {
+                new_type = get_exps_type_low_bpw_bump(ftype, new_type);
+            }
+            ++qs.i_ffn_down_exps;
+        }
+        else if (category == tensor_category::FFN_DOWN_SHEXP) {
+            if (use_more_bits(qs.i_ffn_down_shexp, qs.n_ffn_down_shexp)) {
+                new_type = GGML_TYPE_Q8_0;
+            }
+            else {
+                new_type = GGML_TYPE_Q4_K;
+            }
+            ++qs.i_ffn_down_shexp;
+        }
+        else if (category == tensor_category::FFN_GATE_SHEXP) {
+            if (use_more_bits(qs.i_ffn_gate_shexp, qs.n_ffn_gate_shexp)) {
+                new_type = GGML_TYPE_Q8_0;
+            }
+            else {
+                new_type = GGML_TYPE_Q4_K;
+            }
+            ++qs.i_ffn_gate_shexp;
+        }
+        else if (category == tensor_category::FFN_UP_SHEXP) {
+            if (use_more_bits(qs.i_ffn_up_shexp, qs.n_ffn_up_shexp)) {
+                new_type = GGML_TYPE_Q8_0;
+            }
+            else {
+                new_type = GGML_TYPE_Q4_K;
+            }
+            ++qs.i_ffn_up_shexp;
+        }
         else if (category == tensor_category::ATTENTION_OUTPUT) {
-            if (qs.model.hparams.n_expert == 8) {
-                new_type = GGML_TYPE_Q5_K;
+            if (qs.model.hparams.n_expert >= 8) {
+                new_type = (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
             } else {
                 if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS;
                 else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
             }
         }
-    } else if (category_is_attn_v(category)) {
-        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 (category == tensor_category::SSM_OUT) {
+        if (use_more_bits(qs.i_ssm_out, qs.n_ssm_out)) {
+            new_type = GGML_TYPE_Q8_0;
         }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
-            new_type = GGML_TYPE_Q4_K;
+        ++qs.i_ssm_out;
+    } else if (category_is_attn_v(category)) {
+        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
+            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
             new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
@@ -517,49 +759,96 @@ static ggml_type llama_tensor_get_type_impl(quantize_state_impl & qs, ggml_type
             new_type = GGML_TYPE_Q4_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
-            new_type = GGML_TYPE_Q4_K;
+            new_type = GGML_TYPE_Q5_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
-            new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+            if (use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) {
+                new_type = GGML_TYPE_Q5_K;
+            } else {
+                new_type = GGML_TYPE_Q4_K;
+            }
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
         else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 4) {
-            new_type = GGML_TYPE_Q5_K;
+            new_type = GGML_TYPE_Q6_K;
         }
         else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
                 use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q5_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q6_K && use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q8_0;
         if (qs.model.type == LLM_TYPE_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) {
+        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;
         }
         ++qs.i_attention_wv;
+    } else if (category == tensor_category::ATTENTION_Q) {
+        if (use_more_bits(qs.i_attn_q, qs.n_attn_q)) {
+            if (ftype == LLAMA_FTYPE_MOSTLY_Q6_K || ftype == LLAMA_FTYPE_MOSTLY_Q8_0) {
+                new_type = GGML_TYPE_Q8_0;
+            }
+            else {
+                new_type = GGML_TYPE_Q6_K;
+            }
+        }
+        ++qs.i_attn_q;
     } else if (category == tensor_category::ATTENTION_K) {
-        if (qs.model.hparams.n_expert == 8) {
+        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 (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
-            new_type = GGML_TYPE_IQ3_XXS;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q6_K) {
+            new_type = GGML_TYPE_Q8_0;
         }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
-            new_type = GGML_TYPE_IQ2_S;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) {
+            new_type = GGML_TYPE_Q6_K;
         }
-    } else if (category == tensor_category::ATTENTION_Q) {
-        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
-            new_type = GGML_TYPE_IQ3_XXS;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
+            new_type = GGML_TYPE_Q5_K;
         }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
-            new_type = GGML_TYPE_IQ2_S;
+    } else if (category == tensor_category::FFN_DOWN_SHEXP) {
+        if (use_more_bits(qs.i_ffn_down_shexp, qs.n_ffn_down_shexp)) {
+            new_type = GGML_TYPE_Q8_0;
         }
+        else {
+            new_type = get_ffn_shexp_ggml_type(ftype, new_type);
+        }
+        ++qs.i_ffn_down_shexp;
+    } else if (category == tensor_category::FFN_GATE_SHEXP) {
+        if (use_more_bits(qs.i_ffn_gate_shexp, qs.n_ffn_gate_shexp)) {
+            new_type = GGML_TYPE_Q8_0;
+        }
+        else {
+            new_type = get_ffn_shexp_ggml_type(ftype, new_type);
+        }
+        ++qs.i_ffn_gate_shexp;
+    } else if (category == tensor_category::FFN_UP_SHEXP) {
+        if (use_more_bits(qs.i_ffn_up_shexp, qs.n_ffn_up_shexp)) {
+            new_type = GGML_TYPE_Q8_0;
+        }
+        else {
+            new_type = get_ffn_shexp_ggml_type(ftype, new_type);
+        }
+        ++qs.i_ffn_up_shexp;
+    } else if (category == tensor_category::FFN_DOWN_EXPS) {
+        if (use_more_bits(qs.i_ffn_down_exps, qs.n_ffn_down_exps)) {
+            if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || ftype == LLAMA_FTYPE_MOSTLY_Q5_0) && qs.has_imatrix) {
+                // Guard against craziness in the first few ffn_down layers that can happen even with imatrix for Q4_0/Q5_0.
+                // We only do it when an imatrix is provided because a) we want to make sure that one can always get the
+                // same quantization as before imatrix stuff, and b) Q4_1/Q5_1 do go crazy on ffn_down without an imatrix.
+                new_type = ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q5_1;
+            } else {
+                new_type = get_exps_type_high_bpw_bump(ftype, new_type, qs.has_imatrix);
+            }
+        }
+        ++qs.i_ffn_down_exps;
     } else if (category == tensor_category::FFN_DOWN) {
         auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str());
         int i_layer = info.first, n_layer = info.second;
@@ -576,7 +865,7 @@ static ggml_type llama_tensor_get_type_impl(quantize_state_impl & qs, ggml_type
                      : 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 >= 8 && use_more_bits(i_layer, n_layer)))) {
             new_type = GGML_TYPE_Q4_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
@@ -607,30 +896,52 @@ static ggml_type llama_tensor_get_type_impl(quantize_state_impl & qs, ggml_type
         ++qs.i_ffn_down;
     } else if (category == tensor_category::ATTENTION_OUTPUT) {
         if (arch != LLM_ARCH_FALCON) {
-            if (qs.model.hparams.n_expert == 8) {
-                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  ||
-                    ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) {
+            if (qs.model.hparams.n_expert >= 8) {
+                if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K    || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || 
+                    ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S ||
+                    ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S  || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
+                    new_type = GGML_TYPE_Q4_K;
+                }
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || 
+                         ftype == LLAMA_FTYPE_MOSTLY_IQ3_S  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  ||
+                         ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) {
                     new_type = GGML_TYPE_Q5_K;
                 }
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) {
+                    new_type = GGML_TYPE_Q6_K;
+                }
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q6_K) {
+                    new_type = GGML_TYPE_Q8_0;
+                }
             } else {
                 if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   ) new_type = GGML_TYPE_Q3_K;
                 else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS ) new_type = GGML_TYPE_IQ3_S;
                 else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q4_K;
                 else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  ) new_type = GGML_TYPE_Q4_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M ) new_type = GGML_TYPE_Q5_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S ) new_type = GGML_TYPE_Q4_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M ) new_type = GGML_TYPE_Q6_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S ) new_type = GGML_TYPE_Q5_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q6_K   ) new_type = GGML_TYPE_Q8_0;
             }
         } else {
             if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
         }
     }
     else if (category == tensor_category::ATTENTION_QKV) {
-        if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
+        if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
             new_type = GGML_TYPE_Q4_K;
         }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS ||
+                 ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  ||
+                 ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S  ||
+                 ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S) {
+            new_type = GGML_TYPE_Q5_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q6_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q6_K  ) new_type = GGML_TYPE_Q8_0;
     }
     else if (category == tensor_category::FFN_GATE) {
         auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str());
@@ -648,6 +959,63 @@ static ggml_type llama_tensor_get_type_impl(quantize_state_impl & qs, ggml_type
         }
         ++qs.i_ffn_up;
     }
+    else if (category == tensor_category::ATTENTION_KV_A_MQA) {
+        new_type = GGML_TYPE_Q8_0;
+    }
+    else if (category == tensor_category::ATTENTION_K_B || category == tensor_category::ATTENTION_V_B) {
+        if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   || ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S ||
+            ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ||
+            ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S) {
+            new_type = GGML_TYPE_Q5_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
+                 ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+            new_type = GGML_TYPE_IQ4_XS;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) {
+            new_type = GGML_TYPE_Q6_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q6_K) {
+            new_type = GGML_TYPE_Q8_0;
+        }
+    }
+    else if (category == tensor_category::ATTENTION_Q_B) {
+        if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   || ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S ||
+            ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ||
+            ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
+                 ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+            new_type = GGML_TYPE_IQ4_XS;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) {
+            new_type = GGML_TYPE_Q5_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) {
+            new_type = GGML_TYPE_Q6_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q6_K) {
+            new_type = GGML_TYPE_Q8_0;
+        }
+    }
+    else if (category == tensor_category::ATTENTION_Q_A) {
+        if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   || ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S ||
+            ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ||
+            ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S) {
+            new_type = GGML_TYPE_Q5_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
+                 ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+            new_type = GGML_TYPE_IQ4_XS;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) {
+            new_type = GGML_TYPE_Q6_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q6_K) {
+            new_type = GGML_TYPE_Q8_0;
+        }
+    }
 
     return new_type;
 }
@@ -983,6 +1351,33 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
             if (category_is_attn_v(cat)) {
                 ++qs.n_attention_wv;
             }
+            else if (cat == tensor_category::ATTENTION_QKV) {
+                ++qs.n_attn_qkv;
+            }
+            else if (cat == tensor_category::FFN_GATE_EXPS) {
+                ++qs.n_ffn_gate_exps;
+            }
+            else if (cat == tensor_category::FFN_GATE_SHEXP) {
+                ++qs.n_ffn_gate_shexp;
+            }
+            else if (cat == tensor_category::FFN_DOWN_EXPS) {
+                ++qs.n_ffn_down_exps;
+            }
+            else if (cat == tensor_category::FFN_DOWN_SHEXP) {
+                ++qs.n_ffn_down_shexp;
+            }
+            else if (cat == tensor_category::FFN_UP_EXPS) {
+                ++qs.n_ffn_up_exps;
+            }
+            else if (cat == tensor_category::FFN_UP_SHEXP) {
+                ++qs.n_ffn_up_shexp;
+            }
+            else if (cat == tensor_category::SSM_OUT) {
+                ++qs.n_ssm_out;
+            }
+            else if (cat == tensor_category::ATTENTION_Q) {
+                ++qs.n_attn_q;
+            }
             if (cat == tensor_category::OUTPUT) {
                 qs.has_tied_embeddings = false;
             }