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!nvidia-smi
"""Install the libraries"""
!pip install -q langchain transformers accelerate bitsandbytes
"""Load the libraries"""
from langchain.chains import LLMChain, SequentialChain
from langchain.memory import ConversationBufferMemory
from langchain import HuggingFacePipeline
from langchain import PromptTemplate, LLMChain
from transformers import AutoModel
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import json
import textwrap
"""Download the Model - We are using NousResearch's Llama2 which is the same as Meta AI's Llama 2, the only difference being "**Not requiring authentication to download**"
"""
tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf",
device_map='auto',
torch_dtype=torch.float16,
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16)
"""Define Transformers Pipeline which will be fed into Langchain"""
from transformers import pipeline
pipe = pipeline("text-generation",
model=model,
tokenizer= tokenizer,
torch_dtype=torch.float16,
device_map="auto",
max_new_tokens = 4956,
do_sample=True,
top_k=30,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id
)
"""Define the Prompt format for Llama 2 - This might change if you have a different model"""
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<>\n", "\n<>\n\n"
DEFAULT_SYSTEM_PROMPT = """\
As the leader of a sizable team in a dynamic business, I'm tasked with improving our supply chain management process. Recently, we've been facing issues like increased costs, longer lead times, and decreased customer satisfaction, all of which we believe are interconnected. To address these challenges, I need your assistance in optimizing our supply chain management. Please provide insights, strategies, and best practices that can help us streamline our operations, reduce costs, improve efficiency, and ultimately enhance customer satisfaction. Additionally, consider the latest technologies and innovations that could be integrated into our supply chain to make it more agile and responsive to market demands.If you don't know the answer to a question, please don't share false information.Just say you don't know and you are sorry!"""
"""All the helper fucntions to generate prompt, prompt template, clean up output text"""
def get_prompt(instruction, new_system_prompt=DEFAULT_SYSTEM_PROMPT, citation=None):
SYSTEM_PROMPT = B_SYS + new_system_prompt + E_SYS
prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST
if citation:
prompt_template += f"\n\nCitation: {citation}" # Insert citation here
return prompt_template
def cut_off_text(text, prompt):
cutoff_phrase = prompt
index = text.find(cutoff_phrase)
if index != -1:
return text[:index]
else:
return text
def remove_substring(string, substring):
return string.replace(substring, "")
def generate(text, citation=None):
prompt = get_prompt(text, citation=citation)
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(**inputs,
max_length=4956,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
)
final_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
final_outputs = cut_off_text(final_outputs, '')
final_outputs = remove_substring(final_outputs, prompt)
return final_outputs
def parse_text(text):
wrapped_text = textwrap.fill(text, width=100)
print(wrapped_text + '\n\n')
"""Defining Langchain LLM"""
llm = HuggingFacePipeline(pipeline = pipe, model_kwargs = {'temperature':0.3,'max_length': 4956, 'top_k' :50})
system_prompt = "You are an advanced supply chain optimization expert"
instruction = "Use the data provided to you to optimize the supply chain:\n\n {text}"
template = get_prompt(instruction, system_prompt)
print(template)
prompt = PromptTemplate(template=template, input_variables=["text"])
llm_chain = LLMChain(prompt=prompt, llm=llm, verbose = False)
import pandas as pd
df_supplier = pd.read_csv('merged_data.csv')
print(df_supplier.head())
text = f"Based on the data provided how can you optimize my supply chain by prorviding me with the optmized solution as well as the techniques used. {df_supplier}"
response = llm_chain.run(text)
print(response)
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