clean up

Former-commit-id: 5677d821e0376f1e1fcf9e4df889f5dede7b1c1a

swarms/utils/llm.py

@@ -13,7 +13,8 @@ class LLM:
                  openai_api_key: Optional[str] = None,
                  hf_repo_id: Optional[str] = None,
                  hf_api_token: Optional[str] = None,
-                 model_kwargs: Optional[dict] = None):
+                 temperature: Optional[float] = 0.5,
+                 max_length: Optional[int] = 64):
 
         # Check if keys are in the environment variables
         openai_api_key = openai_api_key or os.getenv('OPENAI_API_KEY')
@@ -22,18 +23,23 @@ class LLM:
         self.openai_api_key = openai_api_key
         self.hf_repo_id = hf_repo_id
         self.hf_api_token = hf_api_token
-        self.model_kwargs = model_kwargs if model_kwargs else {}
+        self.temperature = temperature
+        self.max_length = max_length
 
         # If the HuggingFace API token is provided, set it in environment variables
         if self.hf_api_token:
             os.environ["HUGGINGFACEHUB_API_TOKEN"] = self.hf_api_token
 
-        # Create the LLM object based on the provided keys
+        #initialize the LLM project
+        self.initialize_llm()
+    
+    def initialize_llm(self):
+        model_kwargs = {"temperature": self.temperature, "max_length": self.max_length}
         try:
             if self.hf_repo_id and self.hf_api_token:
-                self.llm = HuggingFaceHub(repo_id=self.hf_repo_id, model_kwargs=self.model_kwargs)
+                self.llm = HuggingFaceHub(repo_id=self.hf_repo_id, model_kwargs=model_kwargs)
             elif self.openai_api_key:
-                self.llm = ChatOpenAI(api_key=self.openai_api_key, model_kwargs=self.model_kwargs)
+                self.llm = ChatOpenAI(api_key=self.openai_api_key, model_kwargs=model_kwargs)
             else:
                 raise ValueError("Please provide either OpenAI API key or both HuggingFace repository ID and API token.")
         except Exception as e:
@@ -43,7 +49,6 @@ class LLM:
     def run(self, prompt: str) -> str:
         template = """Question: {question}
         Answer: Let's think step by step."""
-        
         try:
             prompt_template = PromptTemplate(template=template, input_variables=["question"])
             llm_chain = LLMChain(prompt=prompt_template, llm=self.llm)
@@ -62,3 +67,8 @@ class LLM:
 # llm_instance = LLM(hf_repo_id="google/flan-t5-xl", hf_api_token="your_hf_api_token")
 # result = llm_instance.run("Who won the FIFA World Cup in 1998?")
 # print(result)
+
+
+# make super easy to chaneg parameters, in class, use cpu and 
+#add qlora, 8bit inference
+# look into adding deepspeed