import os from typing import Optional from threading import Thread import torch import gradio as gr from langchain.llms.base import LLM from langchain.prompts import PromptTemplate from langchain_community.vectorstores import Pinecone from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationalRetrievalChain from langchain_community.embeddings import HuggingFaceBgeEmbeddings from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer, pipeline os.environ["TOKENIZERS_PARALLELISM"] = "false" def initialize_model_and_tokenizer(model_name="mistralai/Mistral-7B-Instruct-v0.2"): quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, trust_remote_code=True, torch_dtype=torch.float16, device_map='auto', quantization_config=quantization_config ) model.eval() tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.pad_token = tokenizer.eos_token return model, tokenizer def init_chain(model, tokenizer, db, embed, temp, max_new_tokens, top_p, top_k, r_penalty): class CustomLLM(LLM): """Streamer Object""" streamer: Optional[TextIteratorStreamer] = None def _call(self, prompt, stop=None, run_manager=None) -> str: self.streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, Timeout=5) inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].to('cuda') generate_kwargs = dict( temperature=float(temp), max_new_tokens=int(max_new_tokens), top_p=float(top_p), top_k=int(top_k), repetition_penalty=float(r_penalty), do_sample=True ) kwargs = dict(input_ids=input_ids, streamer=self.streamer, **generate_kwargs) thread = Thread(target=model.generate, kwargs=kwargs) thread.start() return "" @property def _llm_type(self) -> str: return "custom" llm = CustomLLM() memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) questionprompt = PromptTemplate.from_template( """[INST] Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Instead of they/them refer to Dmytro Kisil as he/him. CONTEXT: {context} CHAT HISTORY: {chat_history} QUESTION: {question} Helpful Answer: [/INST] """ ) llm_chain = ConversationalRetrievalChain.from_llm( llm=llm, retriever=db.as_retriever(search_kwargs={"k": 5}), memory=memory, condense_question_prompt=questionprompt, ) return llm_chain, llm index_name = "resume-demo" queries = [["Which masters degree Dmytro Kisil has?"], ["Which amount of salary does Dmytro Kisil is looking for?"], ["How long does Dmytro Kisil looking for a job?"], ["Why Dmytro Kisil moved to the Netherlands?"], ["When Dmytro Kisil left Ukraine?"], ["Where Dmytro Kisil live now?"], ["How many years of working experience in total does Dmytro Kisil have?"], ["How fast Dmytro Kisil can start working for my company?"]] embed = HuggingFaceBgeEmbeddings(model_name='BAAI/bge-small-en-v1.5') db = Pinecone.from_existing_index(index_name, embed) model, tokenizer = initialize_model_and_tokenizer(model_name="mistralai/Mistral-7B-Instruct-v0.2") with gr.Blocks() as demo: with gr.Column(): gr.HTML("""

Ask about my work experience!

Start typing your question or choose one of the examples below to start from
""") with gr.Row(): gr.Markdown("[Resume]('https://drive.google.com/file/d/1OejkWuQKcjP73_uH6sfnj9u4hfmXQ-Oy/view?usp=sharing')") gr.Markdown("[LinkedIn]('https://www.linkedin.com/in/dmytro-kisil/')") gr.Markdown("[HuggingFace profile]('https://huggingface.co./Oysiyl')") with gr.Column(): chatbot = gr.Chatbot() with gr.Row(): msg = gr.Textbox(scale=9) submit_b = gr.Button("Submit", scale=1) with gr.Row(): retry_b = gr.Button("Retry") undo_b = gr.Button("Undo") clear_b = gr.Button("Clear") examples = gr.Examples(queries, msg) with gr.Accordion("Additional options", open=False): temp = gr.Slider( label="Temperature", value=0.0001, minimum=0.0001, maximum=1.00, step=0.0001, interactive=True, info="Higher values produce more diverse outputs", ) max_new_tokens = gr.Slider( label="Max new tokens", value=1024, minimum=64, maximum=8192, step=64, interactive=True, info="The maximum number of new tokens", ) top_p = gr.Slider( label="Top-p (nucleus sampling)", value=0.95, minimum=0.00, maximum=1.00, step=0.01, interactive=True, info="Higher values sample more low-probability tokens", ) top_k = gr.Slider( label="Top-k", value=40, minimum=0, maximum=100, step=1, interactive=True, info="select from top 0 tokens (because zero, relies on top_p)", ) r_penalty = gr.Slider( label="Repetition penalty", value=1.15, minimum=1.0, maximum=2.0, step=0.01, interactive=True, info="Penalize repeated tokens", ) def user(user_message, history): return "", history + [[user_message, None]] def undo(history): return history[:-1].copy() def retry(user_message, history): try: prev_user_message = history[-1][0] except: prev_user_message = "" return prev_user_message, history + [[prev_user_message, None]] def bot(history, temp, max_new_tokens, top_p, top_k, r_penalty): llm_chain, llm = init_chain(model, tokenizer, db, embed, temp, max_new_tokens, top_p, top_k, r_penalty) llm_chain.run(question=history[-1][0]) history[-1][1] = "" for character in llm.streamer: history[-1][1] += character yield history llm_chain, llm = init_chain(model, tokenizer, db, embed, temp, max_new_tokens, top_p, top_k, r_penalty) msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, [chatbot, temp, max_new_tokens, top_p, top_k, r_penalty], chatbot) submit_b.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, [chatbot, temp, max_new_tokens, top_p, top_k, r_penalty], chatbot) retry_b.click(retry, [msg, chatbot], [msg, chatbot], queue=False).then(bot, [chatbot, temp, max_new_tokens, top_p, top_k, r_penalty], chatbot) clear_b.click(lambda: None, None, chatbot, queue=False) undo_b.click(undo, chatbot, chatbot, queue=False) demo.queue() demo.launch()