Update handler.py

handler.py

@@ -1,37 +1,50 @@
-from typing import Any, Dict, List
- 
+from typing import Any, Dict
 import torch
 import transformers
 from transformers import AutoModelForCausalLM, AutoTokenizer
- 
-dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
- 
- 
+
+# Set dtype based on device capability
+dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] == 8 else torch.float16
+
 class EndpointHandler:
     def __init__(self, path="vkamra/llama_finetune_clockit"):
+        # Load tokenizer
         tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
-        model = AutoModelForCausalLM.from_pretrained(
-            path,
-            return_dict=True,
-            device_map="auto",
-            load_in_8bit=True,
-            torch_dtype=dtype,
-            trust_remote_code=True,
-        )
- 
+        tokenizer.padding_side = "left"  # For proper padding alignment
+
+        # Load model with fallback for non-8bit environments
+        if torch.cuda.is_available():
+            model = AutoModelForCausalLM.from_pretrained(
+                path,
+                return_dict=True,
+                device_map="auto",
+                load_in_8bit=True,
+                torch_dtype=dtype,
+                trust_remote_code=True,
+            )
+        else:
+            model = AutoModelForCausalLM.from_pretrained(
+                path,
+                return_dict=True,
+                torch_dtype=torch.float32,  # Full precision for CPU
+                trust_remote_code=True,
+            )
+
+        # Configure generation settings
         generation_config = model.generation_config
         generation_config.max_new_tokens = 60
-        generation_config.temperature = 0
+        generation_config.temperature = 0.7
         generation_config.num_return_sequences = 1
         generation_config.pad_token_id = tokenizer.eos_token_id
         generation_config.eos_token_id = tokenizer.eos_token_id
         self.generation_config = generation_config
- 
+
+        # Initialize pipeline
         self.pipeline = transformers.pipeline(
             "text-generation", model=model, tokenizer=tokenizer
         )
- 
+
     def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
         prompt = data.pop("inputs", data)
         result = self.pipeline(prompt, generation_config=self.generation_config)
-        return result
+        return result