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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 | |
import subprocess | |
# Install flash attention | |
subprocess.run( | |
"pip install flash-attn --no-build-isolation", | |
env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"}, | |
shell=True, | |
) | |
theme = gr.themes.Base( | |
font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'], | |
) | |
model_name = "microsoft/Phi-3-medium-4k-instruct" | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
model = AutoModelForCausalLM.from_pretrained(model_name, device_map='cuda', torch_dtype=torch.float16, _attn_implementation="flash_attention_2", 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 | |
def predict1(message, history, temperature1, max_tokens1, repetition_penalty1, top_p1): | |
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_tokens1, | |
do_sample=True, | |
top_p=top_p1, | |
repetition_penalty=repetition_penalty1, | |
temperature=temperature1, | |
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 | |
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, _attn_implementation="flash_attention_2", 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 | |
def predict(message, history, temperature, max_tokens, repetition_penalty, top_p): | |
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, | |
repetition_penalty=repetition_penalty, | |
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 | |
max_tokens1 = gr.Slider( | |
minimum=512, | |
maximum=4096, | |
value=4000, | |
step=32, | |
interactive=True, | |
label="Maximum number of new tokens to generate", | |
) | |
repetition_penalty1 = gr.Slider( | |
minimum=0.01, | |
maximum=5.0, | |
value=1, | |
step=0.01, | |
interactive=True, | |
label="Repetition penalty", | |
) | |
temperature1 = gr.Slider( | |
minimum=0.0, | |
maximum=1.0, | |
value=0.7, | |
step=0.05, | |
visible=True, | |
interactive=True, | |
label="Temperature", | |
) | |
top_p1 = gr.Slider( | |
minimum=0.01, | |
maximum=0.99, | |
value=0.9, | |
step=0.01, | |
visible=True, | |
interactive=True, | |
label="Top P", | |
) | |
chatbot1 = gr.Chatbot( | |
label="Phi3-medium-4k", | |
show_copy_button=True, | |
likeable=True, | |
layout="panel" | |
) | |
output=gr.Textbox(label="Prompt") | |
with gr.Blocks() as min: | |
gr.ChatInterface( | |
fn=predict1, | |
chatbot=chatbot1, | |
additional_inputs=[ | |
temperature1, | |
max_tokens1, | |
repetition_penalty1, | |
top_p1, | |
], | |
) | |
max_tokens = gr.Slider( | |
minimum=64000, | |
maximum=128000, | |
value=100000, | |
step=1000, | |
interactive=True, | |
label="Maximum number of new tokens to generate", | |
) | |
repetition_penalty = gr.Slider( | |
minimum=0.01, | |
maximum=5.0, | |
value=1, | |
step=0.01, | |
interactive=True, | |
label="Repetition penalty", | |
) | |
temperature = gr.Slider( | |
minimum=0.0, | |
maximum=1.0, | |
value=0.7, | |
step=0.05, | |
visible=True, | |
interactive=True, | |
label="Temperature", | |
) | |
top_p = gr.Slider( | |
minimum=0.01, | |
maximum=0.99, | |
value=0.9, | |
step=0.01, | |
visible=True, | |
interactive=True, | |
label="Top P", | |
) | |
chatbot = gr.Chatbot( | |
label="Phi3-medium-128k", | |
show_copy_button=True, | |
likeable=True, | |
layout="panel" | |
) | |
output=gr.Textbox(label="Prompt") | |
with gr.Blocks() as max: | |
gr.ChatInterface( | |
fn=predict, | |
chatbot=chatbot, | |
additional_inputs=[ | |
temperature, | |
max_tokens, | |
repetition_penalty, | |
top_p, | |
], | |
) | |
with gr.Blocks(title="Phi 3 Medium DEMO", theme=theme) as demo: | |
gr.Markdown("# Phi3 Medium all in one") | |
gr.TabbedInterface([max, min], ['Phi3 medium 128k','Phi3 medium 4k']) | |
demo.launch(share=True) |