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app.py

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+import json
+import os
+from pprint import pprint
+import bitsandbytes as bnb
+import pandas as pd
+import torch
+import torch.nn as nn
+import transformers
+from datasets import load_dataset
+from huggingface_hub import notebook_login
+from peft import (
+    LoraConfig,
+    PeftConfig,
+    PeftModel,
+    get_peft_model,
+    prepare_model_for_kbit_training,
+)
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+PEFT_MODEL = 'deedax/falcon-7b-personal-assistant'
+
+config = PeftConfig.from_pretrained(PEFT_MODEL)
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit = True,
+    bnb_4bit_use_double_quant = True,
+    bnb_4bit_quant_type = 'nf4',
+    bnb_4bit_compute_dtype = torch.bfloat16,
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    return_dict = True,
+    quantization_config = bnb_config,
+    device_map = 'auto',
+    trust_remote_code = True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+tokenizer.pad_token = tokenizer.eos_token
+
+model = PeftModel.from_pretrained(model, PEFT_MODEL)
+model.config.use_cache = False
+
+DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+
+generation_config = model.generation_config
+generation_config.max_new_tokens = 200
+generation_config.temperature = 0.1
+generation_config.top_p = 0.3
+generation_config.num_return_sequences = 1
+generation_config.pad_token_id = tokenizer.eos_token_id
+generation_config.eos_token_id = tokenizer.eos_token_id
+
+def generate_response(question: str) -> str:
+    prompt = f'''
+    Below is a conversation between an interviewer and a candidate, You are Dahiru Ibrahim, the candidate. 
+    Your contact details are as follows
+    github:https://github.com/Daheer
+    youtube:https://www.youtube.com/@deedaxinc
+    linkedin:https://linkedin.com/in/daheer-deedax
+    huggingface:https://huggingface.co/deedax
+    email:suhayrid6@gmail.com
+    phone:+2348147116750
+    Provide very SHORT, CONCISE, DIRECT and ACCURATE answers to the interview questions. 
+    You do not respond as 'Interviewer' or pretend to be 'Interviewer'. You only respond ONCE as Candidate.
+    Interviewer: {question}
+    Candidate:
+    '''.strip()
+    encoding = tokenizer(prompt, return_tensors = 'pt').to(DEVICE)
+    with torch.inference_mode():
+        outputs = model.generate(
+            input_ids = encoding.input_ids,
+            attention_mask = encoding.attention_mask,
+            generation_config = generation_config,
+        )
+
+        response = tokenizer.decode(outputs[0], skip_special_tokens = True)
+
+        assistant_start = 'Candidate:'
+        response_start = response.find(assistant_start)
+        return response[response_start + len(assistant_start):].strip() 
+
+import streamlit as st
+import random
+
+st.title("💬 Deedax Chat (Falcon-7B-Instruct)")
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [{"role": "assistant", "content": "Ask me anything about Dahiru!"}]
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
+
+if prompt := st.chat_input():
+
+    st.session_state.messages = []
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    st.chat_message("user").write(prompt)
+    msg = {'role': 'message', 'content': str(generate_response(prompt))}
+    st.session_state.messages.append(msg)
+    st.chat_message("assistant").write(msg['content'])

requirements.txt

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+pip
+bitsandbytes
+torch==2.0.1
+git+https://github.com/huggingface/transformers.git@e03a9cc
+git+https://github.com/huggingface/peft.git@42a184f
+git+https://github.com/huggingface/accelerate.git@c9fbb71
+datasets==2.12.0
+loralib==0.1.1
+einops==0.6.1
+protobuf==3.20
+jsonschema==3.0.2
+transforms
+streamlit