app.py

import gradio as gr
from transformers import TextIteratorStreamer
from threading import Thread
from transformers import StoppingCriteria, StoppingCriteriaList
import torch
import spaces
import os

model_name1 = "microsoft/Phi-3-medium-4k-instruct"
from transformers import AutoModelForCausalLM, AutoTokenizer
model1 = AutoModelForCausalLM.from_pretrained(model_name1, device_map='cuda', torch_dtype=torch.float16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name1)

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

@spaces.GPU(duration=20, queue=False)
def predict1(message, history, temperature, max_tokens, top_p, top_k):
    history_transformer_format = history + [[message, ""]]
    stop = StopOnTokens()
    messages = "".join(["".join(["\n<|end|>\n<|user|>\n"+item[0], "\n<|end|>\n<|assistant|>\n"+item[1]]) 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=max_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        stopping_criteria=StoppingCriteriaList([stop])
    )
    t = Thread(target=model1.generate, kwargs=generate_kwargs)
    t.start()
    partial_message = ""
    for new_token in streamer:
        if new_token != '<':
            partial_message += new_token
        yield partial_message

model_name = "microsoft/Phi-3-medium-128k-instruct"
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(model_name, device_map='cuda', torch_dtype=torch.float16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name)

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

@spaces.GPU(duration=40, queue=False)
def predict(message, history, temperature, max_tokens, top_p, top_k):
    history_transformer_format = history + [[message, ""]]
    stop = StopOnTokens()
    messages = "".join(["".join(["\n<|end|>\n<|user|>\n"+item[0], "\n<|end|>\n<|assistant|>\n"+item[1]]) 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=max_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        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

with gr.Blocks() as min:  
    gr.ChatInterface(
        fn=predict1,
        title="Phi-3-medium-4k-instruct",
        additional_inputs=[
            gr.Slider(0.1, 0.9, value=0.7, label="Temperature"),
            gr.Slider(512, 4096, value=4096, label="Max Tokens"),
            gr.Slider(0.1, 0.9, value=0.7, label="top_p"),
            gr.Slider(10, 90, value=40, label="top_k"),
        ]
    )
    
    
with gr.Blocks() as max:
    gr.ChatInterface(
        fn=predict,
        title="Phi-3-medium-128k-instruct",
        additional_inputs=[
            gr.Slider(0.1, 0.9, value=0.7, label="Temperature"),
            gr.Slider(64000, 128000, value=100000, label="Max Tokens"),
            gr.Slider(0.1, 0.9, value=0.7, label="top_p"),
            gr.Slider(10, 90, value=40, label="top_k"),
        ]
    )

with gr.Blocks(title="Phi 3 Medium DEMO") as demo:
    gr.Markdown("# Phi3 Medium all in one")
    gr.TabbedInterface([max, min], ['Phi3 medium 128k','Phi3 medium 4k'])
        
demo.launch(share=True)