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Refactor several function calls and the API

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oobabooga 2023-04-06 01:22:15 -03:00 • committed by GitHub
parent 378d21e80c
commit 3f3e42e26c
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8 changed files with 147 additions and 118 deletions
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35
extensions/api/script.py
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@ -40,24 +40,27 @@ class Handler(BaseHTTPRequestHandler):
prompt_lines.pop(0)
prompt = '\n'.join(prompt_lines)
generate_params = {
'max_new_tokens': int(body.get('max_length', 200)),
'do_sample': bool(body.get('do_sample', True)),
'temperature': float(body.get('temperature', 0.5)),
'top_p': float(body.get('top_p', 1)),
'typical_p': float(body.get('typical', 1)),
'repetition_penalty': float(body.get('rep_pen', 1.1)),
'encoder_repetition_penalty': 1,
'top_k': int(body.get('top_k', 0)),
'min_length': int(body.get('min_length', 0)),
'no_repeat_ngram_size': int(body.get('no_repeat_ngram_size',0)),
'num_beams': int(body.get('num_beams',1)),
'penalty_alpha': float(body.get('penalty_alpha', 0)),
'length_penalty': float(body.get('length_penalty', 1)),
'early_stopping': bool(body.get('early_stopping', False)),
'seed': int(body.get('seed', -1)),
}
generator = generate_reply(
question = prompt,
max_new_tokens = int(body.get('max_length', 200)),
do_sample=bool(body.get('do_sample', True)),
temperature=float(body.get('temperature', 0.5)),
top_p=float(body.get('top_p', 1)),
typical_p=float(body.get('typical', 1)),
repetition_penalty=float(body.get('rep_pen', 1.1)),
encoder_repetition_penalty=1,
top_k=int(body.get('top_k', 0)),
min_length=int(body.get('min_length', 0)),
no_repeat_ngram_size=int(body.get('no_repeat_ngram_size',0)),
num_beams=int(body.get('num_beams',1)),
penalty_alpha=float(body.get('penalty_alpha', 0)),
length_penalty=float(body.get('length_penalty', 1)),
early_stopping=bool(body.get('early_stopping', False)),
seed=int(body.get('seed', -1)),
prompt,
generate_params,
stopping_strings=body.get('stopping_strings', []),
)

5
extensions/send_pictures/script.py
View file

@ -2,12 +2,11 @@ import base64
from io import BytesIO
import gradio as gr
import modules.chat as chat
import modules.shared as shared
import torch
from PIL import Image
from transformers import BlipForConditionalGeneration, BlipProcessor
from modules import chat, shared
# If 'state' is True, will hijack the next chat generation with
# custom input text given by 'value' in the format [text, visible_text]
input_hijack = {