Spaces:
Sleeping
Sleeping
File size: 3,853 Bytes
07ffad3 42df98c 1b7e4b0 07ffad3 31d7c4a 07ffad3 eaca477 1b7e4b0 43ae797 07ffad3 31d7c4a ac746bc 31d7c4a 1b7e4b0 31d7c4a 1b7e4b0 31d7c4a 07ffad3 31d7c4a 1b7e4b0 42df98c 31d7c4a eaca477 31d7c4a 1b7e4b0 31d7c4a 07ffad3 cc1edc1 0118c8b 31d7c4a 42df98c 07ffad3 31d7c4a 07ffad3 31d7c4a 07ffad3 31d7c4a 07ffad3 31d7c4a 07ffad3 18b530b 07ffad3 ac746bc 07ffad3 18b530b ef4a283 995956f ef4a283 8b048b4 ef4a283 18b530b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
import gradio as gr
from datasets import load_dataset
import os
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
import torch
from threading import Thread
from sentence_transformers import SentenceTransformer
from datasets import load_dataset
import time
token = os.environ["HF_TOKEN"]
ST = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
dataset = load_dataset("AI-4-Health/embedded-dataset")
data = dataset["train"]
data = data.add_faiss_index("embeddings") # column name that has the embeddings of the dataset
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
# use quantization to lower GPU usage
bnb_config = BitsAndBytesConfig(
load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(model_id,token=token)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
quantization_config=bnb_config,
token=token
)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
SYS_PROMPT = """You are an assistant for answering questions.
You are given the extracted parts of a long document and a question. Provide a conversational answer.
If you don't know the answer, just say "I do not know." Don't make up an answer."""
def search(query: str, k: int = 3 ):
"""a function that embeds a new query and returns the most probable results"""
embedded_query = ST.encode(query) # embed new query
scores, retrieved_examples = data.get_nearest_examples( # retrieve results
"embeddings", embedded_query, # compare our new embedded query with the dataset embeddings
k=k # get only top k results
)
return scores, retrieved_examples
def format_prompt(prompt,retrieved_documents,k):
"""using the retrieved documents we will prompt the model to generate our responses"""
PROMPT = f"Question:{prompt}\nContext:"
for idx in range(k) :
PROMPT+= f"{retrieved_documents['text'][idx]}\n"
return PROMPT
@spaces.GPU(duration=150)
def talk(prompt,history):
k = 1 # number of retrieved documents
scores , retrieved_documents = search(prompt, k)
formatted_prompt = format_prompt(prompt,retrieved_documents,k)
formatted_prompt = formatted_prompt[:2000] # to avoid GPU OOM
messages = [{"role":"system","content":SYS_PROMPT},{"role":"user","content":formatted_prompt}]
# tell the model to generate
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
input_ids,
max_new_tokens=1024,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
streamer = TextIteratorStreamer(
tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
)
generate_kwargs = dict(
input_ids= input_ids,
streamer=streamer,
max_new_tokens=1024,
do_sample=True,
top_p=0.95,
temperature=0.75,
eos_token_id=terminators,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
print(outputs)
yield "".join(outputs)
TITLE = "# RAG"
DESCRIPTION = """
HPP Chatbot
"""
demo = gr.ChatInterface(
fn=talk,
chatbot=gr.Chatbot(
show_label=True,
show_share_button=True,
show_copy_button=True,
likeable=True,
layout="bubble",
bubble_full_width=False,
),
theme="Soft",
examples=[["what is HPP ?"]],
title=TITLE,
description=DESCRIPTION,
)
demo.launch(debug=True)
|