mistral ai model

playground/models/mistral.py

@@ -0,0 +1,10 @@
+from swarms.models import Mistral
+
+model = Mistral(
+    device="cuda", 
+    use_flash_attention=True
+)
+
+prompt = "My favourite condiment is"
+result = model.run(prompt)
+print(result)

swarms/models/__init__.py

@@ -6,4 +6,5 @@ from swarms.models.anthropic import Anthropic
 from swarms.models.petals import Petals
 # from swarms.models.openai import OpenAIChat
 #prompts
-from swarms.models.prompts.debate import *
+from swarms.models.prompts.debate import *
+from swarms.models.mistral import Mistral

swarms/models/base.py

@@ -2,6 +2,9 @@ from abc import ABC, abstractmethod
 
 class AbstractModel(ABC):
     #abstract base class for language models
+    def __init__():
+        pass
+    
     @abstractmethod
     def run(self, prompt):
         #generate text using language model

swarms/models/mistral.py

@@ -1,47 +1,29 @@
-# from exa import Inference
-
-
-# class Mistral:
-#     def __init__(
-#         self,
-#         temperature: float = 0.4,
-#         max_length: int = 500,
-#         quantize: bool = False,
-#     ):
-#         self.temperature = temperature
-#         self.max_length = max_length
-#         self.quantize = quantize
-
-#         self.model = Inference(
-#             model_id="from swarms.workers.worker import Worker",
-#             max_length=self.max_length,
-#             quantize=self.quantize
-#         )
-    
-#     def run(
-#         self,
-#         task: str
-#     ):
-#         try:
-#             output = self.model.run(task)
-#             return output
-#         except Exception as e:
-#             raise e
-        
-
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-class MistralWrapper:
+class Mistral:
+    """
+    Mistral
+
+    model = MistralWrapper(device="cuda", use_flash_attention=True, temperature=0.7, max_length=200)
+    task = "My favourite condiment is"
+    result = model.run(task)
+    print(result)
+    """
     def __init__(
-            self, 
+        self, 
-            model_name="mistralai/Mistral-7B-v0.1", 
+        model_name: str ="mistralai/Mistral-7B-v0.1", 
-            device="cuda", 
+        device: str ="cuda", 
-            use_flash_attention=False
+        use_flash_attention: bool = False,
-        ):
+        temperature: float = 1.0,
+        max_length: int = 100,
+        do_sample: bool = True
+    ):
         self.model_name = model_name
         self.device = device
         self.use_flash_attention = use_flash_attention
+        self.temperature = temperature
+        self.max_length = max_length
 
         # Check if the specified device is available
         if not torch.cuda.is_available() and device == "cuda":
@@ -60,18 +42,22 @@ class MistralWrapper:
         except Exception as e:
             raise ValueError(f"Error loading the Mistral model: {str(e)}")
 
-    def run(self, prompt, max_new_tokens=100, do_sample=True):
+    def run(
+        self, 
+        task: str
+    ):
+        """Run the model on a given task."""
+        
         try:
-            model_inputs = self.tokenizer([prompt], return_tensors="pt").to(self.device)
+            model_inputs = self.tokenizer([task], return_tensors="pt").to(self.device)
-            generated_ids = self.model.generate(**model_inputs, max_new_tokens=max_new_tokens, do_sample=do_sample)
+            generated_ids = self.model.generate(
+                **model_inputs, 
+                max_length=self.max_length, 
+                do_sample=self.do_sample, 
+                temperature=self.temperature,
+                max_new_tokens=self.max_length
+            )
             output_text = self.tokenizer.batch_decode(generated_ids)[0]
             return output_text
         except Exception as e:
-            raise ValueError(f"Error running the model: {str(e)}")
+            raise ValueError(f"Error running the model: {str(e)}")
-
-# Example usage:
-if __name__ == "__main__":
-    wrapper = MistralWrapper(device="cuda", use_flash_attention=True)
-    prompt = "My favourite condiment is"
-    result = wrapper.run(prompt)
-    print(result)