diff --git a/ggml.c b/ggml.c
index 072078806..3dda7547f 100644
--- a/ggml.c
+++ b/ggml.c
@@ -4345,50 +4345,6 @@ void ggml_print_objects(const struct ggml_context * ctx) {
     GGML_PRINT("%s: --- end ---\n", __func__);
 }
 
-static void ggml_print_tensor(const struct ggml_tensor * tensor) {
-    GGML_PRINT("Tensor (null): %s | rank %d | shape (", ggml_type_name(tensor->type), tensor->n_dims);
-    for (int i=0; i<tensor->n_dims; ++i) {
-        GGML_PRINT("%lld ", tensor->ne[i]);
-    }
-    GGML_PRINT(") | strides (");
-    for (int i=0; i<tensor->n_dims; ++i) {
-        GGML_PRINT("%lld ", tensor->nb[i]);
-    }
-    GGML_PRINT(")\n");
-}
-
-static void ggml_print_tensor_values(const struct ggml_tensor * tensor, int starts[], int dim, int nelts) {
-    GGML_ASSERT(tensor->type == GGML_TYPE_F32);
-    GGML_PRINT("Printing values for tensor %s[", tensor->name);
-    for (int i=0; i<tensor->n_dims; ++i) {
-        GGML_ASSERT(starts[i] >= 0);
-        if (i == dim) {
-            if (starts[i] > 0)  {
-                GGML_PRINT("%d:%d", starts[i], starts[i]+nelts);
-            } else {
-                GGML_PRINT(":%d", starts[i]+nelts);
-            }
-        } else {
-            GGML_PRINT("%d", starts[i]);
-        }
-        if (i<tensor->n_dims-1) {
-            GGML_PRINT(",");
-        }
-    }
-    GGML_PRINT("]\n");
-    float *data_ptr = (float *) tensor->data;
-    int offset = 0;
-    for (int j = 0; j < tensor->n_dims; j++) {
-        offset += (starts[j] * tensor->nb[j]) / ggml_type_size(GGML_TYPE_F32);
-    }
-    data_ptr += offset;
-    for (int i = 0; i < nelts; i++) {
-        GGML_PRINT("%f ", *data_ptr);
-        data_ptr += tensor->nb[dim] / ggml_type_size(GGML_TYPE_F32);
-    }
-    GGML_PRINT("\n");
-}
-
 int64_t ggml_nelements(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
@@ -6442,7 +6398,6 @@ struct ggml_tensor * ggml_mul_mat(
 
     const int64_t ne[4] = { a->ne[1], b->ne[1], b->ne[2], b->ne[3] };
     struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MAX(a->n_dims, b->n_dims), ne);
-    //GGML_PRINT("ggml_mul_mat result shape : (%lld, %lld, %lld, %lld)\n", ne[0], ne[1], ne[2], ne[3]);
 
     result->op   = GGML_OP_MUL_MAT;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -11205,7 +11160,6 @@ static void ggml_compute_forward_norm_f32(
     }
 
     GGML_ASSERT(src0->nb[0] == sizeof(float));
-    // If the name starts with "layer_inputs", and we are on thread 0, print the tensor
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -12322,16 +12276,8 @@ static void ggml_compute_forward_view(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0) {
     // NOP
-    if (strncmp(src0->name, "cache_k", 7) == 0 && params->ith == 0) { 
-        /*
-        GGML_PRINT("\noutputs of cache_k for view%s\n", src0->name);
-        ggml_print_tensor(src0);
-        int starts[] = {4096 * };
-        ggml_print_tensor_values(src0, starts, 0, 10);
-        */
-    }
-    //UNUSED(params);
-    //UNUSED(src0);
+    UNUSED(params);
+    UNUSED(src0);
 }
 
 // ggml_compute_forward_permute
diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py
index 6bb139c9d..882b96bc6 100644
--- a/gguf-py/gguf/gguf.py
+++ b/gguf-py/gguf/gguf.py
@@ -234,7 +234,7 @@ class TensorNameMap:
             "transformer.word_embeddings", # falcon
             "model.embed_tokens",          # llama-hf
             "tok_embeddings",              # llama-pth
-            "language_model.embedding.word_embeddings", # adept
+            "language_model.embedding.word_embeddings", # persimmon
         ),
 
         # Position embeddings
@@ -247,7 +247,7 @@ class TensorNameMap:
             "embed_out", # gptneox
             "lm_head",   # gpt2 mpt falcon llama-hf baichuan
             "output",    # llama-pth
-            "word_embeddings_for_head", # adept
+            "word_embeddings_for_head", # persimmon
         ),
 
         # Output norm
@@ -256,7 +256,7 @@ class TensorNameMap:
             "transformer.ln_f",          # gpt2 falcon
             "model.norm",                # llama-hf baichuan
             "norm",                      # llama-pth
-            "language_model.encoder.final_layernorm", # adept
+            "language_model.encoder.final_layernorm", # persimmon
         ),
 
         # Rope frequencies
@@ -275,7 +275,7 @@ class TensorNameMap:
             "transformer.h.{bid}.ln_mlp",            # falcon40b
             "model.layers.{bid}.input_layernorm",    # llama-hf
             "layers.{bid}.attention_norm",           # llama-pth
-            "language_model.encoder.layers.{bid}.input_layernorm", # adept
+            "language_model.encoder.layers.{bid}.input_layernorm", # persimmon
         ),
 
         # Attention norm 2
@@ -289,7 +289,7 @@ class TensorNameMap:
             "transformer.h.{bid}.attn.c_attn",                    # gpt2
             "transformer.blocks.{bid}.attn.Wqkv",                 # mpt
             "transformer.h.{bid}.self_attention.query_key_value", # falcon
-            "language_model.encoder.layers.{bid}.self_attention.query_key_value", # adept
+            "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
         ),
 
         # Attention query
@@ -318,7 +318,7 @@ class TensorNameMap:
             "transformer.h.{bid}.self_attention.dense", # falcon
             "model.layers.{bid}.self_attn.o_proj",      # llama-hf
             "layers.{bid}.attention.wo",                # llama-pth
-            "language_model.encoder.layers.{bid}.self_attention.dense" # adept
+            "language_model.encoder.layers.{bid}.self_attention.dense" # persimmon
         ),
 
         # Rotary embeddings
@@ -334,7 +334,7 @@ class TensorNameMap:
             "transformer.blocks.{bid}.norm_2",                # mpt
             "model.layers.{bid}.post_attention_layernorm",    # llama-hf
             "layers.{bid}.ffn_norm",                          # llama-pth
-            "language_model.encoder.layers.{bid}.post_attention_layernorm", # adept
+            "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon
         ),
 
         # Feed-forward up
@@ -345,7 +345,7 @@ class TensorNameMap:
             "transformer.h.{bid}.mlp.dense_h_to_4h",   # falcon
             "model.layers.{bid}.mlp.up_proj",          # llama-hf
             "layers.{bid}.feed_forward.w3",            # llama-pth
-            "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # adept
+            "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
         ),
 
         # Feed-forward gate
@@ -362,7 +362,7 @@ class TensorNameMap:
             "transformer.h.{bid}.mlp.dense_4h_to_h",   # falcon
             "model.layers.{bid}.mlp.down_proj",        # llama-hf
             "layers.{bid}.feed_forward.w2",            # llama-pth
-            "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # adept
+            "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon
         ),
 
         MODEL_TENSOR.ATTN_Q_NORM: (
@@ -374,7 +374,7 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.ROPE_FREQS: (
-            "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # adept
+            "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon
         )
     }
 
diff --git a/llama.cpp b/llama.cpp
index 20feae50d..cebb5b6ca 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -7088,7 +7088,6 @@ struct llama_context * llama_new_context_with_model(
             llama_free(ctx);
             return nullptr;
         }
-        LLAMA_LOG_INFO("Kv self cache: %7.2f MB\n", ggml_nbytes(ctx->kv_self.k) / 1024.0 / 1024.0);
 
         {
             const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v);