File size: 2,828 Bytes
40c895f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
import os
from threading import Event, Thread
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
StoppingCriteria,
StoppingCriteriaList,
TextIteratorStreamer,
)
from huggingface_hub import login
import gradio as gr
import torch
login(os.getenv("HF_TOKEN", None))
model_name = "richardr1126/spider-natsql-wizard-coder-8bit"
tok = AutoTokenizer.from_pretrained(model_name)
max_new_tokens = 1536
print(f"Starting to load the model {model_name}")
m = AutoModelForCausalLM.from_pretrained(
model_name,
device_map=0,
load_in_8bit=True,
)
m.config.pad_token_id = m.config.eos_token_id
m.generation_config.pad_token_id = m.config.eos_token_id
stop_tokens = [";", "###", "Result"]
stop_token_ids = tok.convert_tokens_to_ids(stop_tokens)
print(f"Successfully loaded the model {model_name} into memory")
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
for stop_id in stop_token_ids:
if input_ids[0][-1] == stop_id:
return True
return False
def bot(input_message: str, temperature=0.1, top_p=0.9, top_k=0, repetition_penalty=1.08):
stop = StopOnTokens()
messages = input_message
input_ids = tok(messages, return_tensors="pt").input_ids
input_ids = input_ids.to(m.device)
streamer = TextIteratorStreamer(tok, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
temperature=temperature,
do_sample=temperature > 0.0,
top_p=top_p,
top_k=top_k,
repetition_penalty=repetition_penalty,
streamer=streamer,
stopping_criteria=StoppingCriteriaList([stop]),
)
stream_complete = Event()
def generate_and_signal_complete():
m.generate(**generate_kwargs)
stream_complete.set()
t1 = Thread(target=generate_and_signal_complete)
t1.start()
partial_text = ""
for new_text in streamer:
partial_text += new_text
return partial_text
gradio_interface = gr.Interface(
fn=bot,
inputs=[
"text",
gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.1, step=0.1),
gr.Slider(label="Top-p (nucleus sampling)", minimum=0.0, maximum=1.0, value=0.9, step=0.01),
gr.Slider(label="Top-k", minimum=0, maximum=200, value=0, step=1),
gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.08, step=0.1)
],
outputs="text",
title="REST API with Gradio and Huggingface Spaces",
description="This is a demo of how to build an AI powered REST API with Gradio and Huggingface Spaces – for free! See the **Use via API** link at the bottom of this page.",
)
gradio_interface.launch()
|