|
|
|
|
|
|
|
|
import os |
|
|
from threading import Thread |
|
|
from typing import Iterator |
|
|
|
|
|
import gradio as gr |
|
|
import spaces |
|
|
import torch |
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
|
|
|
|
|
MAX_MAX_NEW_TOKENS = 2048 |
|
|
DEFAULT_MAX_NEW_TOKENS = 1024 |
|
|
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) |
|
|
|
|
|
DESCRIPTION = """\ |
|
|
# Llama-2 7B Chat |
|
|
This is your personal space to chat. |
|
|
You can ask anything from strategic questions regarding the game or just chat as you like. |
|
|
""" |
|
|
|
|
|
'''LICENSE = """ |
|
|
<p/> |
|
|
|
|
|
--- |
|
|
As a derivate work of [Llama-2-13b-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat) by Meta, |
|
|
this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/llama-2-13b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/llama-2-13b-chat/blob/main/USE_POLICY.md). |
|
|
""" |
|
|
|
|
|
if not torch.cuda.is_available(): |
|
|
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" |
|
|
|
|
|
|
|
|
if torch.cuda.is_available(): |
|
|
model_id = "meta-llama/Llama-2-13b-chat-hf" |
|
|
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", load_in_4bit=True) |
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id) |
|
|
tokenizer.use_default_system_prompt = False |
|
|
''' |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if torch.cuda.is_available(): |
|
|
model_id = "meta-llama/Llama-2-7b-chat-hf" |
|
|
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") |
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id) |
|
|
tokenizer.use_default_system_prompt = False |
|
|
|
|
|
@spaces.GPU |
|
|
def generate( |
|
|
message: str, |
|
|
chat_history: list[tuple[str, str]], |
|
|
system_prompt: str, |
|
|
max_new_tokens: int = 1024, |
|
|
temperature: float = 0.6, |
|
|
top_p: float = 0.9, |
|
|
top_k: int = 50, |
|
|
repetition_penalty: float = 1.2, |
|
|
) -> Iterator[str]: |
|
|
conversation = [] |
|
|
if system_prompt: |
|
|
conversation.append({"role": "system", "content": system_prompt}) |
|
|
for user, assistant in chat_history: |
|
|
conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) |
|
|
conversation.append({"role": "user", "content": message}) |
|
|
|
|
|
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt") |
|
|
if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: |
|
|
input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] |
|
|
gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") |
|
|
input_ids = input_ids.to(model.device) |
|
|
|
|
|
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=max_new_tokens, |
|
|
do_sample=True, |
|
|
top_p=top_p, |
|
|
top_k=top_k, |
|
|
temperature=temperature, |
|
|
num_beams=1, |
|
|
repetition_penalty=repetition_penalty, |
|
|
) |
|
|
t = Thread(target=model.generate, kwargs=generate_kwargs) |
|
|
t.start() |
|
|
|
|
|
outputs = [] |
|
|
for text in streamer: |
|
|
outputs.append(text) |
|
|
yield "".join(outputs) |
|
|
|
|
|
|
|
|
chat_interface = gr.ChatInterface( |
|
|
fn=generate, |
|
|
examples=[ |
|
|
["How much should I invest in order to win?"], |
|
|
["What happened in the last round?"], |
|
|
["What is my probability to win if I do not invest anything?"], |
|
|
["What is my probability to win if I do not share anything?"], |
|
|
["Can you explain the rules very briefly again?"], |
|
|
], |
|
|
) |
|
|
|
|
|
with gr.Blocks(css="style.css") as demo: |
|
|
gr.Markdown(DESCRIPTION) |
|
|
|
|
|
chat_interface.render() |
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
|
|
|
demo.queue(max_size=20) |
|
|
demo.launch(share=True, debug=True) |
|
|
|
