##################################### ## ##################################### from langchain_community.llms import HuggingFaceHub #from langchain_community.llms import HuggingFaceHub llm_zephyr_7b_beta = HuggingFaceHub( repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", task="text-generation", model_kwargs={ "max_new_tokens": 1000, "top_k": 2, "temperature": 0.1, "repetition_penalty": 1.03, }, ) import os from crewai import Agent, Task, Crew, Process from crewai_tools.tools import SerperDevTool #from crewai_tools import SerperDevTool search_tool = SerperDevTool() # Define your agents with roles and goals researcher = Agent( role='Senior Research Analyst', goal='Uncover cutting-edge developments in AI and data science', backstory="""You work at a leading tech think tank. Your expertise lies in identifying emerging trends. You have a knack for dissecting complex data and presenting actionable insights.""", verbose=True, allow_delegation=False, tools=[search_tool], max_execution_time=120, max_iter=50, # Optional max_rpm=25, # Optional llm=llm_zephyr_7b_beta # You can pass an optional llm attribute specifying what mode you wanna use. # It can be a local model through Ollama / LM Studio or a remote # model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/) # # import os # os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo' # # OR # # from langchain_openai import ChatOpenAI # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7) ) writer = Agent( role='Tech Content Strategist', goal='Craft compelling content on tech advancements', backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles. You transform complex concepts into compelling narratives.""", verbose=True, max_execution_time=120, max_iter=50, # Optional max_rpm=25, # Optional allow_delegation=False, llm=llm_zephyr_7b_beta ) # Create tasks for your agents task1 = Task( description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024. Identify key trends, breakthrough technologies, and potential industry impacts.""", expected_output="Full analysis report in bullet points", agent=researcher ) task2 = Task( description="""Using the insights provided, develop an engaging blog post that highlights the most significant AI advancements. Your post should be informative yet accessible, catering to a tech-savvy audience. Make it sound cool, avoid complex words so it doesn't sound like AI.""", expected_output="Full blog post of at least 4 paragraphs", agent=writer ) # Instantiate your crew with a sequential process crew = Crew( agents=[researcher, writer], tasks=[task1, task2], verbose=1, # You can set it to 1 or 2 to different logging levels ) # Get your crew to work! #result = crew.kickoff() #print("######################") #print(result) ################## ###### other models: # "Trelis/Llama-2-7b-chat-hf-sharded-bf16" # "bn22/Mistral-7B-Instruct-v0.1-sharded" # "HuggingFaceH4/zephyr-7b-beta" # function for loading 4-bit quantized model def load_model( ): model = HuggingFaceHub( repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_kwargs={"max_length": 1048, "temperature":0.2, "max_new_tokens":256, "top_p":0.95, "repetition_penalty":1.0}, ) return model ############### ##### ##### ##### #### from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware app = FastAPI() # middlewares to allow cross orgin communications app.add_middleware( CORSMiddleware, allow_origins=['*'], allow_credentials=True, allow_methods=['*'], allow_headers=['*'], ) @app.post("/generate/") def generate(user_input, history=[]): print("######################") result = crew.kickoff() print("######################") print(result) return result # load the model asynchronously on startup and save it into memory @app.on_event("startup") async def startup(): # Get your crew to work! # result = crew.kickoff() print("######################") # print(result)