Update app.py

app.py

@@ -3,7 +3,7 @@ from huggingface_hub import InferenceClient
 import transformers
 from transformers import AutoTokenizer,GenerationConfig
 import torch
-from peft import PeftModel, LoraConfig
+from peft import PeftModel
 
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
@@ -12,23 +12,24 @@ For more information on `huggingface_hub` Inference API support, please check th
 from llama_rope_scaled_monkey_patch import replace_llama_rope_with_scaled_rope
 replace_llama_rope_with_scaled_rope()
 base_model = "Neko-Institute-of-Science/LLaMA-65B-HF"
-lora_weights = LoraConfig(
-    auto_mapping=None,
-    base_model_name_or_path="Neko-Institute-of-Science/LLaMA-65B-HF",
-    bias=None,
-    fan_in_fan_out=False,
-    inference_mode=True,
-    init_lora_weights=True,
-    layers_pattern=None,
-    layers_to_transform=None,
-    lora_alpha=16,
-    lora_dropout=0.05,
-    modules_to_save=None,
-    peft_type="LORA",
-    revision=None,
-    target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"],
-    task_type="CAUSAL_LM",
-)
+lora_weights = "./"
+#lora_weights = LoraConfig(
+#    auto_mapping=None,
+#    base_model_name_or_path="Neko-Institute-of-Science/LLaMA-65B-HF",
+#   bias=None,
+#    fan_in_fan_out=False,
+#    inference_mode=True,
+#    init_lora_weights=True,
+#    layers_pattern=None,
+#    layers_to_transform=None,
+#    lora_alpha=16,
+#    lora_dropout=0.05,
+#    modules_to_save=None,
+#    peft_type="LORA",
+#    revision=None,
+#    target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"],
+#    task_type="CAUSAL_LM",
+#)
 
 cache_dir = "/data"
 model = transformers.AutoModelForCausalLM.from_pretrained(