diff --git a/gpt4all-backend/scripts/convert_mpt_hf_to_ggml.py b/gpt4all-backend/scripts/convert_mpt_hf_to_ggml.py
index 981432fc..50a68554 100644
--- a/gpt4all-backend/scripts/convert_mpt_hf_to_ggml.py
+++ b/gpt4all-backend/scripts/convert_mpt_hf_to_ggml.py
@@ -76,24 +76,32 @@ fout = open(fname_out, "wb")
 vocab = tokenizer.vocab
 
 hparams["multiple_of"] = 1
-fout.write(struct.pack("i", 0x67676d6d)) # magic: ggml in hex
-fout.write(struct.pack("i", hparams["vocab_size"]))
-fout.write(struct.pack("i", hparams["max_seq_len"]))
-fout.write(struct.pack("i", hparams["d_model"]))
-fout.write(struct.pack("i", hparams["n_heads"]))
-fout.write(struct.pack("i", hparams["n_layers"]))
-# n_rot (unused)
-fout.write(struct.pack("i", 0))
-fout.write(struct.pack("i", ftype))
+fout.write(struct.pack("I", 0x67676d6d)) # magic: ggml in hex
+fout.write(struct.pack("I", model.config.vocab_size))
+fout.write(struct.pack("I", model.config.max_seq_len))
+fout.write(struct.pack("I", model.config.n_layers))
+fout.write(struct.pack("I", model.config.n_heads))
+fout.write(struct.pack("I", model.config.d_model))
+fout.write(struct.pack("f", model.config.attn_config['alibi_bias_max']))
+clip_qkv = model.config.attn_config['clip_qkv']
+fout.write(struct.pack("f",  clip_qkv if clip_qkv is not None else 0))
+fout.write(struct.pack("I", ftype))
 
 # # Is this correct??
 # dot_token = tokenizer.encode(".")[0]
 # write tokens to ggml file 
-fout.write(struct.pack("i", hparams["vocab_size"]))
+dot_token = tokenizer.encode('.')[0]
+fout.write(struct.pack("I", model.config.vocab_size))
 
-for i in range(hparams["vocab_size"]):
-    text = tokenizer.decode([i]).encode('utf-8')
-    fout.write(struct.pack("i", len(text)))
+for i in range(model.config.vocab_size):
+    text = tokenizer.decode([dot_token, i]).encode('utf-8')
+    # remove the first byte (it's always '.')
+    text = text[1:]
+    enclen = len(text)
+    if i in tokenizer.all_special_ids:
+        print(f"special token: {text}")
+        enclen = enclen | 1<<31
+    fout.write(struct.pack("I", enclen))
     fout.write(text)
     
 list_vars = model.state_dict()
@@ -101,73 +109,35 @@ for name in list_vars.keys():
     data = list_vars[name].squeeze().numpy()
     print("Processing variable: " + name + " with shape: ", data.shape)
 
-    # we don't need these
-    if name.endswith("attn.masked_bias") or name.endswith(".attn.bias"):
-        print("  Skipping variable: " + name)
-        continue
+    n_dims = len(data.shape);
 
-    if "Wqkv.weight" in name:
-        # chunk qkv
-        query, key, value = np.split(data, 3, axis=0)
-
-        new_name = name.split("Wqkv.weight")[0]
-
-        for (data, name) in [(query, new_name + "q_proj.weight"), (key, new_name + "k_proj.weight"), (value, new_name + "v_proj.weight")]:
-            print(f"Processing variable: {name} with shape: {data.shape}")
-            n_dims = len(data.shape);
-
-            # ftype == 0 -> float32, ftype == 1 -> float16
-            ftype_cur = 0;
-            if ftype != 0:
-                print("  Converting to float16")
-                data = data.astype(np.float16)
-                ftype_cur = 1
-            else:
-                if data.dtype != np.float32:
-                    print("  Converting to float32")
-                    data = data.astype(np.float32)
-                    ftype_cur = 0
-
-            # header
-            str = name.encode('utf-8')
-            fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
-            for i in range(n_dims):
-                fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
-            fout.write(str);
-
-            # data
-            data.tofile(fout)
-
-    else:
-
-        n_dims = len(data.shape);
-
-        # ftype == 0 -> float32, ftype == 1 -> float16
-        ftype_cur = 0;
-        if ftype != 0:
-            if name[-7:] == ".weight" and n_dims == 2:
-                print("  Converting to float16")
-                data = data.astype(np.float16)
-                ftype_cur = 1
-            else:
-                print("  Converting to float32")
-                data = data.astype(np.float32)
-                ftype_cur = 0
+    # ftype == 0 -> float32, ftype == 1 -> float16
+    ftype_cur = 0;
+    if ftype != 0:
+        # Keep token embeddings in fp32
+        if name[-7:] == ".weight" and n_dims == 2 and ".wte" not in name:
+            print("  Converting to float16")
+            data = data.astype(np.float16)
+            ftype_cur = 1
         else:
-            if data.dtype != np.float32:
-                print("  Converting to float32")
-                data = data.astype(np.float32)
-                ftype_cur = 0
+            print("  Converting to float32")
+            data = data.astype(np.float32)
+            ftype_cur = 0
+    else:
+        if data.dtype != np.float32:
+            print("  Converting to float32")
+            data = data.astype(np.float32)
+            ftype_cur = 0
 
-        # header
-        str = name.encode('utf-8')
-        fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
-        for i in range(n_dims):
-            fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
-        fout.write(str);
+    # header
+    str = name.encode('utf-8')
+    fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
+    for i in range(n_dims):
+        fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
+    fout.write(str);
 
-        # data
-        data.tofile(fout)
+    # data
+    data.tofile(fout)
 
 fout.close()