diff --git a/ggml-quants.c b/ggml-quants.c
index e801e0c28..6b88c140a 100644
--- a/ggml-quants.c
+++ b/ggml-quants.c
@@ -11968,7 +11968,7 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
     const int      * kmap_q2xs       = iq2_data[gindex].map;
     const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours;
 
-    GGML_ASSERT(quant_weights   && "missing quantization weights");
+    //GGML_ASSERT(quant_weights   && "missing quantization weights");
     GGML_ASSERT(kgrid_q2xs      && "forgot to call ggml_quantize_init()?");
     GGML_ASSERT(kmap_q2xs       && "forgot to call ggml_quantize_init()?");
     GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
@@ -12006,8 +12006,12 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
 
         for (int ib = 0; ib < QK_K/block_size; ++ib) {
             const float * xb = xbl + block_size*ib;
-            const float * qw = quant_weights + QK_K*ibl + block_size*ib;
-            for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*ibl + block_size*ib;
+                for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            } else {
+                for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i];
+            }
             float max = fabsf(xb[0]);
             for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
             if (!max) {
diff --git a/ggml.c b/ggml.c
index be8691349..e4f33ec33 100644
--- a/ggml.c
+++ b/ggml.c
@@ -20353,8 +20353,8 @@ bool ggml_quantize_requires_imatrix(enum ggml_type type) {
     return
         type == GGML_TYPE_IQ2_XXS ||
         type == GGML_TYPE_IQ2_XS  ||
-        type == GGML_TYPE_IQ1_S   ||
-        type == GGML_TYPE_IQ1_M;
+        type == GGML_TYPE_IQ1_S;//   ||
+        //type == GGML_TYPE_IQ1_M;
 }
 
 size_t ggml_quantize_chunk(
diff --git a/llama.cpp b/llama.cpp
index 0c1c5645a..edeec193e 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -12919,6 +12919,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             if (!params->pure && ggml_is_quantized(default_type)) {
                 new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
             }
+            else if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
+                new_type = params->token_embedding_type;
+            }
+            else if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
+                new_type = params->output_tensor_type;
+            }
 
             // If we've decided to quantize to the same type the tensor is already
             // in then there's nothing to do.
@@ -12951,7 +12957,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                  new_type == GGML_TYPE_IQ2_XS  ||
                  new_type == GGML_TYPE_IQ2_S   ||
                  new_type == GGML_TYPE_IQ1_S   ||
-                 new_type == GGML_TYPE_IQ1_M   ||
+                (new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight"))  ||
                 (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) {
                 LLAMA_LOG_ERROR("\n\n============================================================\n");
                 LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);