diff --git a/gguf_namemap.py b/gguf_namemap.py
new file mode 100644
index 000000000..7546630ed
--- /dev/null
+++ b/gguf_namemap.py
@@ -0,0 +1,95 @@
+# Recommended mapping of model tensor names for storage in gguf
+
+def get_tensor_namemap( n_blocks : int):
+    tensor_map = {}
+    # Token embeddings
+    mapped_to = "token_embd"
+    tensor_map["gpt_neox.embed_in"] = mapped_to           # gptneox
+    tensor_map["transformer.wte"] = mapped_to             # gpt2 mpt
+    tensor_map["transformer.word_embeddings"] = mapped_to # falcon
+    tensor_map["model.embed_tokens"] = mapped_to          # llama-hf
+    tensor_map["tok_embeddings"] = mapped_to              # llama-pth
+    # Position embeddings
+    mapped_to = "pos_embd"
+    tensor_map["transformer.wpe"] = mapped_to # gpt2
+    # Output norm
+    mapped_to = "output_norm"
+    tensor_map["gpt_neox.final_layer_norm"] = mapped_to # gptneox
+    tensor_map["transformer.ln_f"] = mapped_to          # gpt2 falcon
+    tensor_map["transformer.norm_f"] = mapped_to        # mpt
+    tensor_map["model.norm"] = mapped_to                # llama-hf
+    tensor_map["norm"] = mapped_to                      # llama-pth
+    # Output
+    mapped_to = "output"
+    tensor_map["embed_out"] = mapped_to # gptneox
+    tensor_map["lm_head"] = mapped_to   # gpt2 mpt falcon llama-hf
+    tensor_map["output"] = mapped_to    # llama-pth
+    # Attention and fee-forward layer blocks
+    for i in range(0,n_blocks):
+        # Attention norm
+        mapped_to = "blk."+str(i)+".attn_norm"
+        tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to # gptneox
+        tensor_map["transformer.h."+str(i)+".ln_1"] = mapped_to              # gpt2
+        tensor_map["transformer.blocks."+str(i)+".norm_1"] = mapped_to       # mpt
+        tensor_map["transformer.h."+str(i)+".input_layernorm"] = mapped_to   # falcon7b
+        tensor_map["transformer.h."+str(i)+".ln_attn"] = mapped_to           # falcon40b
+        tensor_map["model.layers."+str(i)+".input_layernorm"] = mapped_to    # llama-hf
+        tensor_map["layers."+str(i)+".attention_norm"] = mapped_to           # llama-pth
+        # Attention norm 2
+        mapped_to = "blk."+str(i)+".attn_norm_2"
+        tensor_map["transformer.h."+str(i)+".ln_mlp"] = mapped_to # falcon40b
+        # Attention query-key-value
+        mapped_to = "blk."+str(i)+".attn_qkv"
+        tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"] = mapped_to     # gptneox
+        tensor_map["transformer.h."+str(i)+".attn.c_attn"] = mapped_to                     # gpt2
+        tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"] = mapped_to                  # mpt
+        tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to  # falcon
+        # Attention query
+        mapped_to = "blk."+str(i)+".attn_q"
+        tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to # llama-hf
+        tensor_map["layers."+str(i)+".attention.wq"] = mapped_to           # llama-pth
+        # Attention key
+        mapped_to = "blk."+str(i)+".attn_k"
+        tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to # llama-hf
+        tensor_map["layers."+str(i)+".attention.wk"] = mapped_to           # llama-pth
+        # Attention value
+        mapped_to = "blk."+str(i)+".attn_v"
+        tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to # llama-hf
+        tensor_map["layers."+str(i)+".attention.wv"] = mapped_to           # llama-pth
+        # Attention output
+        mapped_to = "blk."+str(i)+".attn_output"
+        tensor_map["gpt_neox.layers."+str(i)+".attention.dense"] = mapped_to    # gptneox
+        tensor_map["transformer.h."+str(i)+".attn.c_proj"] = mapped_to          # gpt2
+        tensor_map["transformer.blocks."+str(i)+".attn.out_proj"] = mapped_to   # mpt
+        tensor_map["transformer.h."+str(i)+".self_attention.dense"] = mapped_to # falcon
+        tensor_map["model.layers."+str(i)+".self_attn.o_proj"] = mapped_to      # llama-hf
+        tensor_map["layers."+str(i)+".attention.wo"] = mapped_to                # llama-pth
+        # Feed-forward norm
+        mapped_to = "blk."+str(i)+".ffn_norm"
+        tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to # gptneox
+        tensor_map["transformer.h."+str(i)+".ln_2"] = mapped_to                       # gpt2
+        tensor_map["transformer.blocks."+str(i)+".norm_2"] = mapped_to               # mpt
+        tensor_map["model.layers."+str(i)+".post_attention_layernorm"] = mapped_to    # llama-hf
+        tensor_map["layers."+str(i)+".ffn_norm"] = mapped_to                          # llama-pth
+        # Feed-forward up
+        mapped_to = "blk."+str(i)+".ffn_up"
+        tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # gptneox
+        tensor_map["transformer.h."+str(i)+".mlp.c_fc"] = mapped_to            # gpt2
+        tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"] = mapped_to    # mpt
+        tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"] = mapped_to   # falcon
+        tensor_map["model.layers."+str(i)+".mlp.up_proj"] = mapped_to          # llama-hf
+        tensor_map["layers."+str(i)+".feed_forward.w3"] = mapped_to            # llama-pth
+        # Feed-forward gate
+        mapped_to = "blk."+str(i)+".ffn_gate"
+        tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to # llama-hf
+        tensor_map["layers."+str(i)+".feed_forward.w1"] = mapped_to     # llama-pth
+        # Feed-forward down
+        mapped_to = "blk."+str(i)+".ffn_down"
+        tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # gptneox
+        tensor_map["transformer.h."+str(i)+".mlp.c_proj"] = mapped_to          # gpt2
+        tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"] = mapped_to  # mpt
+        tensor_map["transformer.h."+str(i)+".mlp.dense_4h_to_h"] = mapped_to   # falcon
+        tensor_map["model.layers."+str(i)+".mlp.down_proj"] = mapped_to        # llama-hf
+        tensor_map["layers."+str(i)+".feed_forward.w2"] = mapped_to            # llama-pth
+
+    return tensor_map