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import transformers
from torch import cuda, bfloat16
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.document_loaders import HuggingFaceDatasetLoader
from langchain.vectorstores import Chroma
from langchain.llms import HuggingFacePipeline
from langchain.chains import RetrievalQA
from langchain.schema import AIMessage, HumanMessage
import gradio as gr
embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'
device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
embed_model = HuggingFaceEmbeddings(
model_name=embed_model_id,
model_kwargs={'device': device},
encode_kwargs={'device': device, 'batch_size': 4}
)
dataset_name = "beinghasnain16/company-policies"
page_content_column = "chunk"
hf_auth = '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'
loader = HuggingFaceDatasetLoader(dataset_name, page_content_column, use_auth_token=hf_auth)
data = loader.load()
vectordb = Chroma.from_documents(data, embed_model)
model_id = 'meta-llama/Llama-2-13b-chat-hf'
# model_id = 'microsoft/phi-1_5'
# model_id = 'meta-llama/Llama-2-7b-chat-hf'
device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
# set quantization configuration to load large model with less GPU memory
# this requires the `bitsandbytes` library
bnb_config = transformers.BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=bfloat16
)
# begin initializing HF items, need auth token for these
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()
print(f"Model loaded on {device}")
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_id,
use_auth_token=hf_auth
)
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
temperature=0.0, # 'randomness' of outputs, 0.0 is the min and 1.0 the max
max_new_tokens=512, # mex number of tokens to generate in the output
repetition_penalty=1.1, # without this output begins repeating
)
res = generate_text("Explain to me the difference between nuclear fission and fusion.")
print(res[0]["generated_text"])
llm = HuggingFacePipeline(pipeline=generate_text)
rag_pipeline = RetrievalQA.from_chain_type(
llm=llm, chain_type='stuff',
retriever=vectordb.as_retriever()
)
def predict(message, history):
history_langchain_format = []
for human, ai in history:
history_langchain_format.append(HumanMessage(content=human))
history_langchain_format.append(AIMessage(content=ai))
history_langchain_format.append(HumanMessage(content=message))
llm_response = rag_pipeline(message)
return llm_response['result']
gr.ChatInterface(predict).launch() |