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#! /bin/bash
# -*- coding: utf-8 -*-
"""Gradio.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1NhPAjcqhdmjOyMrg7j8IHqzGlJARGIjs
"""
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
new_model="Mervyn999/mistral-7b-distilabel-mini-DPO"
model = AutoModelForCausalLM.from_pretrained(new_model,
# low_cpu_mem_usage=True,
torch_dtype=torch.float16,
load_in_4bit=True,
# device_map="cuda"
)
tokenizer = AutoTokenizer.from_pretrained(new_model)
# model = model.to('cuda:0')
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
stop_ids = [29, 0]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
def predict(message, history):
history_transformer_format = history + [[message, ""]]
stop = StopOnTokens()
#Wrap the prompt using the right chat template
messages = "".join([f"### Instruction:\n{item[0]}\n\n### Response:\n{item[1]}" #curr_system_message +
for item in history_transformer_format])
model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=1024,
do_sample=True,
top_p=0.95,
top_k=1000,
temperature=1.0,
num_beams=1,
stopping_criteria=StoppingCriteriaList([stop])
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
partial_message = ""
for new_token in streamer:
if new_token != '<':
partial_message += new_token
yield partial_message
title = "Jaewon's finetuned LLM"
gr.close_all()
# gr.ChatInterface(predict).queue().launch(debug=True)
gr.ChatInterface(
predict,
chatbot=gr.Chatbot(height=300),
textbox=gr.Textbox(placeholder="Send a message", container=False, scale=7),
title="Chat with Mistral-7B DPO tuned",
theme="soft",
retry_btn=None,
undo_btn="Delete Previous",
clear_btn="Clear",
).queue().launch()
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