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import os | |
from threading import Thread | |
from typing import Iterator, List, Tuple | |
import torch | |
from fastapi import FastAPI, HTTPException | |
from pydantic import BaseModel | |
from transformers import AutoTokenizer, AutoModelForCausalLM | |
from peft import PeftModel | |
import gradio as gr | |
from gradio import Blocks | |
from transformers import TextIteratorStreamer | |
# Load the base model and tokenizer | |
base_model = AutoModelForCausalLM.from_pretrained( | |
'meta-llama/Llama-2-7b-chat-hf', | |
trust_remote_code=True, | |
device_map="auto", | |
torch_dtype=torch.float16, | |
) | |
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-chat-hf') | |
# Load the finetuned model | |
model = PeftModel.from_pretrained(base_model, 'FinGPT/fingpt-forecaster_dow30_llama2-7b_lora') | |
model = model.eval() | |
# Define constants | |
MAX_MAX_NEW_TOKENS = 2048 | |
DEFAULT_MAX_NEW_TOKENS = 1024 | |
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) | |
# FastAPI setup | |
app = FastAPI() | |
class ChatRequest(BaseModel): | |
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 | |
async def chat(request: ChatRequest): | |
try: | |
response = await generate_response( | |
request.message, | |
request.chat_history, | |
request.system_prompt, | |
request.max_new_tokens, | |
request.temperature, | |
request.top_p, | |
request.top_k, | |
request.repetition_penalty | |
) | |
return {"response": response} | |
except Exception as e: | |
raise HTTPException(status_code=500, detail=str(e)) | |
async def generate_response( | |
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, | |
) -> 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:] | |
input_ids = input_ids.to(model.device) | |
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) | |
generate_kwargs = { | |
"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) | |
return "".join(outputs) | |
# Gradio setup | |
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]: | |
return generate_response( | |
message, | |
chat_history, | |
system_prompt, | |
max_new_tokens, | |
temperature, | |
top_p, | |
top_k, | |
repetition_penalty | |
) | |
chat_interface = gr.ChatInterface( | |
fn=generate, | |
additional_inputs=[ | |
gr.Textbox(label="System prompt", lines=6), | |
gr.Slider( | |
label="Max new tokens", | |
minimum=1, | |
maximum=MAX_MAX_NEW_TOKENS, | |
step=1, | |
value=DEFAULT_MAX_NEW_TOKENS, | |
), | |
gr.Slider( | |
label="Temperature", | |
minimum=0.1, | |
maximum=4.0, | |
step=0.1, | |
value=0.6, | |
), | |
gr.Slider( | |
label="Top-p (nucleus sampling)", | |
minimum=0.05, | |
maximum=1.0, | |
step=0.05, | |
value=0.9, | |
), | |
gr.Slider( | |
label="Top-k", | |
minimum=1, | |
maximum=1000, | |
step=1, | |
value=50, | |
), | |
gr.Slider( | |
label="Repetition penalty", | |
minimum=1.0, | |
maximum=2.0, | |
step=0.05, | |
value=1.2, | |
), | |
], | |
stop_btn=None, | |
examples=[ | |
["Hello there! How are you doing?"], | |
["Can you explain briefly to me what is the Python programming language?"], | |
["Explain the plot of Cinderella in a sentence."], | |
["How many hours does it take a man to eat a Helicopter?"], | |
["Write a 100-word article on 'Benefits of Open-Source in AI research'"], | |
], | |
) | |
with Blocks(css="style.css") as demo: | |
gr.Markdown("# Llama-2 7B Chat") | |
gr.Markdown(""" | |
This Space demonstrates the Llama-2 7B Chat model by Meta, fine-tuned for chat instructions. | |
Feel free to chat with the model here or use the API to integrate it into your applications. | |
""") | |
chat_interface.render() | |
gr.Markdown("---") | |
gr.Markdown("This demo is governed by the original [license](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf/blob/main/LICENSE.txt).") | |
if __name__ == "__main__": | |
demo.queue(max_size=20).launch() | |