[tool cleanup]

new_features_examples/lumo_example.py

@@ -12,6 +12,7 @@ class Lumo:
     """
     A class for generating text using the Lumo model with 4-bit quantization.
     """
+
     def __init__(self):
         """
         Initializes the Lumo model with 4-bit quantization and a tokenizer.
@@ -21,7 +22,7 @@ class Lumo:
             load_in_4bit=True,
             bnb_4bit_quant_type="nf4",
             bnb_4bit_compute_dtype=torch.float16,
-            llm_int8_enable_fp32_cpu_offload=True
+            llm_int8_enable_fp32_cpu_offload=True,
         )
 
         self.model = LlamaForCausalLM.from_pretrained(
@@ -29,9 +30,11 @@ class Lumo:
             device_map="auto",
             quantization_config=bnb_config,
             use_cache=False,
-            attn_implementation="sdpa"
+            attn_implementation="sdpa",
+        )
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            "lumolabs-ai/Lumo-70B-Instruct"
         )
-        self.tokenizer = AutoTokenizer.from_pretrained("lumolabs-ai/Lumo-70B-Instruct")
 
     def run(self, task: str) -> str:
         """
@@ -43,11 +46,13 @@ class Lumo:
         Returns:
             str: The generated text.
         """
-        inputs = self.tokenizer(task, return_tensors="pt").to(self.model.device)
+        inputs = self.tokenizer(task, return_tensors="pt").to(
+            self.model.device
+        )
         outputs = self.model.generate(**inputs, max_new_tokens=100)
-        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-
+        return self.tokenizer.decode(
+            outputs[0], skip_special_tokens=True
+        )
 
 
 Agent(

swarms/tools/prebuilt/__init__.py

@@ -1,7 +1,5 @@
 from swarms.tools.prebuilt.math_eval import math_eval
-from swarms.tools.prebuilt.code_executor import CodeExecutor
 
 __all__ = [
     "math_eval",
-    "CodeExecutor",
 ]