anthropic docs

docs/swarms/models/anthropic.md

@@ -70,17 +70,18 @@ class Anthropic:
 ```python
 # Import necessary modules and classes
 from swarms.models import Anthropic
-import torch
 
 # Initialize an instance of the Anthropic class
-anthropic_instance = Anthropic()
+model = Anthropic(
+    anthropic_api_key="sk-"
+)
 
-# Using the generate method
-completion_1 = anthropic_instance.generate("What is the capital of France?")
+# Using the run method
+completion_1 = model.run("What is the capital of France?")
 print(completion_1)
 
 # Using the __call__ method
-completion_2 = anthropic_instance("How far is the moon from the earth?", stop=["miles", "km"])
+completion_2 = model("How far is the moon from the earth?", stop=["miles", "km"])
 print(completion_2)
 ```
 

swarms/agents/idea_to_image_agent.py

@@ -1,7 +1,7 @@
 import os
 import logging
 from dataclasses import dataclass
-from dalle3 import Dalle
+from playground.models.dalle3 import Dalle
 from swarms.models import OpenAIChat
 
 

swarms/models/huggingface.py

@@ -23,7 +23,7 @@ class HuggingfaceLLM:
     ```
     from swarms.models import HuggingfaceLLM
 
-    model_id = "gpt2-small"
+    model_id = "NousResearch/Yarn-Mistral-7b-128k"
     inference = HuggingfaceLLM(model_id=model_id)
 
     task = "Once upon a time"
@@ -74,15 +74,20 @@ def __init__(
             bnb_config = BitsAndBytesConfig(**quantization_config)
 
         try:
-            self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+            self.tokenizer = AutoTokenizer.from_pretrained(self.model_id, *args, **kwargs)
             self.model = AutoModelForCausalLM.from_pretrained(
-                self.model_id, quantization_config=bnb_config
+                self.model_id, quantization_config=bnb_config, *args, **kwargs
             )
 
             self.model  # .to(self.device)
         except Exception as e:
-            self.logger.error(f"Failed to load the model or the tokenizer: {e}")
-            raise
+            # self.logger.error(f"Failed to load the model or the tokenizer: {e}")
+            # raise
+            print(colored(f"Failed to load the model and or the tokenizer: {e}", "red"))
+
+    def print_error(self, error: str):
+        """Print error"""
+        print(colored(f"Error: {error}", "red"))
 
     def load_model(self):
         """Load the model"""
@@ -157,7 +162,7 @@ def run(self, task: str):
             del inputs
             return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
         except Exception as e:
-            self.logger.error(f"Failed to generate the text: {e}")
+            print(colored(f"HuggingfaceLLM could not generate text because of error: {e}, try optimizing your arguments", "red"))
             raise
 
     async def run_async(self, task: str, *args, **kwargs) -> str:

tests/models/dalle3.py

@@ -6,7 +6,7 @@
 from PIL import Image
 from termcolor import colored
 
-from dalle3 import Dalle3
+from playground.models.dalle3 import Dalle3
 
 
 # Mocking the OpenAI client to avoid making actual API calls during testing