Merge pull request #15 from haseeb-heaven/feat/support-gemma-groq

Feat/support gemma groq

README.md

@@ -298,6 +298,7 @@ If you're interested in contributing to **Code-Interpreter**, we'd love to have
 - **v2.0.1** - Added AnthropicAI Claude-2, Instant models.
 
 🔥 **v2.1** - Added AnhtorpicAI Claude-3 models powerful _Opus,Sonnet,Haiku_ models.
+- **v2.1.1** - Added **Groq-AI** Model _Gemma-7B_ with **700 Tokens/Sec**.
 
 ## 📜 **License**
 

configs/groq-gemma.config

@@ -0,0 +1,17 @@
+# The temperature parameter controls the randomness of the model's output. Lower values make the output more deterministic.
+temperature = 0.1
+
+# The maximum number of new tokens that the model can generate.
+max_tokens = 8192
+
+# The start separator for the generated code.
+start_sep = ```
+
+# The end separator for the generated code.
+end_sep = ```
+
+# If True, the first line of the generated text will be skipped.
+skip_first_line = True
+
+# The model used for generating the code.
+HF_MODEL = groq-gemma

interpreter.py

@@ -24,7 +24,7 @@
 from libs.utility_manager import UtilityManager
 
 # The main version of the interpreter.
-INTERPRETER_VERSION = "2.1"
+INTERPRETER_VERSION = "2.1.1"
 
 def main():
         parser = argparse.ArgumentParser(description='Code - Interpreter')

libs/interpreter_lib.py

@@ -53,7 +53,7 @@ def initialize(self):
         self.DISPLAY_CODE = self.args.display_code
         self.INTERPRETER_MODEL = self.args.model if self.args.model else None
         self.logger.info(f"Interpreter args model selected is '{self.args.model}")
-        self.logger.info(f"Interpreter model selected is '{self.INTERPRETER_MODEL}")
+        self.logger.info(f"Interpreter model selected is '{self.INTERPRETER_MODEL}'")
         self.system_message = ""
         self.INTERPRETER_MODE = 'code'
 
@@ -91,7 +91,7 @@ def initialize_client(self):
         load_dotenv()
         self.logger.info("Initializing Client")
 
-        self.logger.info(f"Interpreter model selected is '{self.INTERPRETER_MODEL}")
+        self.logger.info(f"Interpreter model selected is '{self.INTERPRETER_MODEL}'")
         if self.INTERPRETER_MODEL is None or self.INTERPRETER_MODEL == "":
             self.logger.info("HF_MODEL is not provided, using default model.")
             config_file_name = f"configs/gpt-3.5-turbo.config" # Setting default model to GPT 3.5 Turbo.
@@ -225,7 +225,8 @@ def execute_last_code(self,os_name):
 
     def generate_content(self,message, chat_history: list[tuple[str, str]], temperature=0.1, max_tokens=1024,config_values=None,image_file=None):
         self.logger.info(f"Generating content with args: message={message}, chat_history={chat_history}, temperature={temperature}, max_tokens={max_tokens}, config_values={config_values}, image_file={image_file}")
-
+        self.logger.info(f"Interpreter model selected is '{self.INTERPRETER_MODEL}'")
+
         # Use the values from the config file if they are provided
         if config_values:
             temperature = float(config_values.get('temperature', temperature))
@@ -300,6 +301,9 @@ def generate_content(self,message, chat_history: list[tuple[str, str]], temperat
             elif 'groq-mixtral' in self.INTERPRETER_MODEL:
                 self.logger.info("Model is Groq/Mixtral.")
                 self.INTERPRETER_MODEL = "groq/mixtral-8x7b-32768"
+            elif 'groq-gemma' in self.INTERPRETER_MODEL:
+                self.logger.info("Model is Groq/Gemma.")
+                self.INTERPRETER_MODEL = "groq/gemma-7b-it"
 
             response = litellm.completion(self.INTERPRETER_MODEL, messages=messages,temperature=temperature,max_tokens=max_tokens)
             self.logger.info("Response received from completion function.")
@@ -697,7 +701,7 @@ def interpreter_main(self,version):
 
                 # Check if prompt contains any file uploaded by user.
                 extracted_file_name = self.utility_manager.extract_file_name(prompt)
-                self.logger.info(f"Input prompt extracted_name: '{extracted_file_name}'")
+                self.logger.info(f"Input prompt file name: '{extracted_file_name}'")
 
                 if extracted_file_name is not None:
                     full_path = self.utility_manager.get_full_file_path(extracted_file_name)