Update inference.py

inference.py

@@ -1,14 +1,14 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel
-
-BASE_MODEL = "deepseek-ai/deepseek-coder-1.3b-base"
-LORA_REPO  = "your-username/deepseek-lora-monthly"
-
-tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
-base = AutoModelForCausalLM.from_pretrained(BASE_MODEL, trust_remote_code=True)
-model = PeftModel.from_pretrained(base, LORA_REPO)
-
-def generate_response(prompt:str)->str:
-    inputs = tokenizer(prompt, return_tensors="pt")
-    out = model.generate(**inputs, max_new_tokens=200)
-    return tokenizer.decode(out[0], skip_special_tokens=True)
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+BASE_MODEL = "deepseek-ai/deepseek-coder-1.3b-base"
+LORA_REPO  = "https://huggingface.co/VaibhavHD/deepseek-lora-monthly"
+
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+base = AutoModelForCausalLM.from_pretrained(BASE_MODEL, trust_remote_code=True)
+model = PeftModel.from_pretrained(base, LORA_REPO)
+
+def generate_response(prompt:str)->str:
+    inputs = tokenizer(prompt, return_tensors="pt")
+    out = model.generate(**inputs, max_new_tokens=200)
+    return tokenizer.decode(out[0], skip_special_tokens=True)