Use str(Path) instead of os.path.abspath(Path)

modules/RWKV.py

@@ -25,10 +25,10 @@ class RWKVModel:
         tokenizer_path = Path(f"{path.parent}/20B_tokenizer.json")
 
         if shared.args.rwkv_strategy is None:
-            model = RWKV(model=os.path.abspath(path), strategy=f'{device} {dtype}')
+            model = RWKV(model=str(path), strategy=f'{device} {dtype}')
         else:
-            model = RWKV(model=os.path.abspath(path), strategy=shared.args.rwkv_strategy)
-        pipeline = PIPELINE(model, os.path.abspath(tokenizer_path))
+            model = RWKV(model=str(path), strategy=shared.args.rwkv_strategy)
+        pipeline = PIPELINE(model, str(tokenizer_path))
 
         result = self()
         result.pipeline = pipeline
@@ -61,7 +61,7 @@ class RWKVTokenizer:
     @classmethod
     def from_pretrained(self, path):
         tokenizer_path = path / "20B_tokenizer.json"
-        tokenizer = Tokenizer.from_file(os.path.abspath(tokenizer_path))
+        tokenizer = Tokenizer.from_file(str(tokenizer_path))
 
         result = self()
         result.tokenizer = tokenizer

modules/quantized_LLaMA.py

@@ -1,4 +1,3 @@
-import os
 import sys
 from pathlib import Path
 
@@ -7,7 +6,7 @@ import torch
 
 import modules.shared as shared
 
-sys.path.insert(0, os.path.abspath(Path("repositories/GPTQ-for-LLaMa")))
+sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
 from llama import load_quant
 
 
@@ -41,7 +40,7 @@ def load_quantized_LLaMA(model_name):
         print(f"Could not find {pt_model}, exiting...")
         exit()
 
-    model = load_quant(path_to_model, os.path.abspath(pt_path), bits)
+    model = load_quant(path_to_model, str(pt_path), bits)
 
     # Multi-GPU setup
     if shared.args.gpu_memory: