Spaces:
Running
Running
File size: 3,693 Bytes
07f3d5b 5f01803 07f3d5b 5f01803 07f3d5b 5f01803 07f3d5b 8e90038 21443c3 ba85e88 77389b9 8e90038 77389b9 8e90038 07f3d5b 4a90639 4bb8e69 90152c0 4bb8e69 4a90639 6ee4f1f 4bb8e69 07f3d5b 4bb8e69 07f3d5b 4bb8e69 07f3d5b 4bb8e69 07f3d5b 4bb8e69 07f3d5b d0186a4 07f3d5b |
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 94 95 96 97 98 99 100 101 102 |
import gradio as gr
import requests
import json
import os
APIKEY = os.environ.get("APIKEY")
APISECRET = os.environ.get("APISECRET")
def predict(text, seed, out_seq_length, min_gen_length, sampling_strategy,
num_beams, length_penalty, no_repeat_ngram_size,
temperature, topk, topp):
global APIKEY
global APISECRET
url = 'https://wudao.aminer.cn/os/api/api/v2/completions_130B'
payload = json.dumps({
"apikey": APIKEY,
"apisecret": APISECRET ,
"language": "zh-CN",
"prompt": text,
"length_penalty": length_penalty,
"temperature": temperature,
"top_k": topk,
"top_p": topp,
"min_gen_length": min_gen_length,
"sampling_strategy": sampling_strategy,
"num_beams": num_beams,
"max_tokens": out_seq_length
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.json())
answer = response.json()['result']['output']['raw']
if isinstance(answer, list):
answer = answer[0]
answer = answer.replace('[</s>]', '')
return answer
if __name__ == "__main__":
with gr.Blocks() as demo:
gr.Markdown(
"""
# GLM-130B
An Open Bilingual Pre-Trained Model
""")
with gr.Row():
with gr.Column():
model_input = gr.Textbox(lines=7, placeholder='Input something in English or Chinese', label='Input')
with gr.Row():
gen = gr.Button("Generate")
clr = gr.Button("Clear")
outputs = gr.Textbox(lines=7, label='Output')
gr.Markdown(
"""
Generation Parameter
""")
with gr.Row():
with gr.Column():
seed = gr.Slider(maximum=100000, value=1234, step=1, label='Seed')
out_seq_length = gr.Slider(maximum=256, value=128, minimum=32, step=1, label='Output Sequence Length')
with gr.Column():
min_gen_length = gr.Slider(maximum=64, value=0, step=1, label='Min Generate Length')
sampling_strategy = gr.Radio(choices=['BeamSearchStrategy', 'BaseStrategy'], value='BeamSearchStrategy', label='Search Strategy')
with gr.Row():
with gr.Column():
# beam search
gr.Markdown(
"""
Beam Search Parameter
""")
num_beams = gr.Slider(maximum=4, value=1, minimum=1, step=1, label='Number of Beams')
length_penalty = gr.Slider(maximum=1, value=0.8, minimum=0, label='Length Penalty')
no_repeat_ngram_size = gr.Slider(maximum=5, value=3, minimum=1, step=1, label='No Repeat Ngram Size')
with gr.Column():
# base search
gr.Markdown(
"""
Base Search Parameter
""")
temperature = gr.Slider(maximum=1, value=1, minimum=0, label='Temperature')
topk = gr.Slider(maximum=8, value=1, minimum=0, step=1, label='Top K')
topp = gr.Slider(maximum=1, value=0, minimum=0, label='Top P')
inputs = [model_input, seed, out_seq_length, min_gen_length, sampling_strategy, num_beams, length_penalty, no_repeat_ngram_size, temperature, topk, topp]
gen.click(fn=predict, inputs=inputs, outputs=outputs)
clr.click(fn=lambda value: gr.update(value=""), inputs=clr, outputs=model_input)
demo.launch() |