test.py

from torch import cuda, bfloat16
import transformers
import torch
from transformers import StoppingCriteria, StoppingCriteriaList
from langchain.llms import HuggingFacePipeline
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from transformers import pipeline

model_id = 'meta-llama/Llama-2-7b-chat-hf'
device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'

bnb_config = transformers.BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type='nf4',
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=bfloat16
)

hf_auth = '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'
model_config = transformers.AutoConfig.from_pretrained(
    model_id,
    use_auth_token=hf_auth
)

model = transformers.AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    config=model_config,
    quantization_config=bnb_config,
    device_map='auto',
    use_auth_token=hf_auth
)

model.eval()

tokenizer = transformers.AutoTokenizer.from_pretrained(
    model_id,
    use_auth_token=hf_auth
)

stop_list = ['\nHuman:', '\n```\n']

stop_token_ids = [tokenizer(x)['input_ids'] for x in stop_list]
stop_token_ids = [torch.LongTensor(x).to(device) for x in stop_token_ids]

class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        for stop_ids in stop_token_ids:
            if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():
                return True
        return False

stopping_criteria = StoppingCriteriaList([StopOnTokens()])

generate_text = transformers.pipeline(
    model=model, 
    tokenizer=tokenizer,
    return_full_text=True,  # langchain expects the full text
    task='text-generation',
    # we pass model parameters here too
    stopping_criteria=stopping_criteria,  # without this model rambles during chat
    temperature=0.1,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max
    max_new_tokens=512,  # max number of tokens to generate in the output
    repetition_penalty=1.1  # without this output begins repeating
)

llm = HuggingFacePipeline(pipeline=generate_text)

web_links = ["https://stanford-cs324.github.io/winter2022/lectures/introduction/"] 
loader = WebBaseLoader(web_links)
documents = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)
all_splits = text_splitter.split_documents(documents)

model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {"device": "cuda"}
embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
vectorstore = FAISS.from_documents(all_splits, embeddings)

chain = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)

chat_history = []

query = "What is a language model?"
result = chain({"question": query, "chat_history": chat_history})

print(result['answer'])