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Running
on
Zero
Running
on
Zero
import gradio as gr | |
from transformers import TextIteratorStreamer | |
from threading import Thread | |
from transformers import StoppingCriteria, StoppingCriteriaList | |
import torch | |
import spaces | |
import os | |
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 | |
model.to('cuda') | |
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=300., 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 | |
demo = 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(512, 8192, 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"), | |
] | |
) | |
demo.launch(share=True) | |