Update getans.py

getans.py

@@ -1,20 +1,19 @@
-from mmap import mmap
-
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from huggingface_hub import login
-token1="hf_"
-token2="rPlNHzkJScHYmtGSaQPcaoKcjJGYQEpjLu"
-login(token=token1+token2)
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-mm="finetuning/output/pytorch_model.bin"
-tokenizer = AutoTokenizer.from_pretrained(mm)
-model = AutoModelForCausalLM.from_pretrained(mm)
-
-def get_response(prompt, max_new_tokens=50):
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, temperature=0.0001, do_sample=True)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)  # Use indexing instead of calling
-    ans=response.toString()
-    return ans
+from mmap import mmap
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from huggingface_hub import login
+token1="hf_"
+token2="vWSzjVPShXPbgmylDJduWwHinTfHNqZVpj"
+login(token=token1+token2,add_to_git_credential=True)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+mm="finetuning/output/pytorch_model.bin"
+tokenizer = AutoTokenizer.from_pretrained(mm)
+model = AutoModelForCausalLM.from_pretrained(mm)
+
+def get_response(prompt, max_new_tokens=50):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, temperature=0.0001, do_sample=True)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)  # Use indexing instead of calling
+    ans=response.toString()
+    return ans