mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-09-20 00:07:06 +00:00
105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
import os
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import re
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import time
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import glob
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import torch
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import gradio as gr
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import transformers
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from transformers import AutoTokenizer
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from transformers import GPTJForCausalLM, AutoModelForCausalLM, AutoModelForSeq2SeqLM, OPTForCausalLM, T5Tokenizer, T5ForConditionalGeneration, GPTJModel, AutoModel
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#model_name = "bloomz-7b1-p3"
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#model_name = 'gpt-j-6B-float16'
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#model_name = "opt-6.7b"
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#model_name = 'opt-13b'
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#model_name = "gpt4chan_model_float16"
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model_name = 'galactica-6.7b'
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#model_name = 'gpt-neox-20b'
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#model_name = 'flan-t5'
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#model_name = 'OPT-13B-Erebus'
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loaded_preset = None
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def load_model(model_name):
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print(f"Loading {model_name}...")
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t0 = time.time()
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if os.path.exists(f"torch-dumps/{model_name}.pt"):
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print("Loading in .pt format...")
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model = torch.load(f"torch-dumps/{model_name}.pt").cuda()
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elif model_name in ['gpt-neox-20b', 'opt-13b', 'OPT-13B-Erebus']:
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model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", device_map='auto', load_in_8bit=True)
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elif model_name in ['gpt-j-6B']:
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model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
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elif model_name in ['flan-t5', 't5-large']:
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model = T5ForConditionalGeneration.from_pretrained(f"models/{model_name}").cuda()
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if model_name in ['gpt4chan_model_float16']:
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tokenizer = AutoTokenizer.from_pretrained("models/gpt-j-6B/")
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elif model_name in ['flan-t5']:
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tokenizer = T5Tokenizer.from_pretrained(f"models/{model_name}/")
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else:
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tokenizer = AutoTokenizer.from_pretrained(f"models/{model_name}/")
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print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
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return model, tokenizer
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def fix_gpt4chan(s):
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for i in range(10):
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s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
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s = re.sub("--- [0-9]*\n *\n---", "---", s)
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s = re.sub("--- [0-9]*\n\n\n---", "---", s)
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return s
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def fn(question, temperature, max_length, inference_settings, selected_model):
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global model, tokenizer, model_name, loaded_preset, preset
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if selected_model != model_name:
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model_name = selected_model
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model = None
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tokenier = None
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torch.cuda.empty_cache()
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model, tokenizer = load_model(model_name)
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if inference_settings != loaded_preset:
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with open(f'presets/{inference_settings}.txt', 'r') as infile:
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preset = infile.read()
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loaded_preset = inference_settings
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torch.cuda.empty_cache()
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input_text = question
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input_ids = tokenizer.encode(str(input_text), return_tensors='pt').cuda()
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output = eval(f"model.generate(input_ids, {preset}).cuda()")
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reply = tokenizer.decode(output[0], skip_special_tokens=True)
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if model_name.startswith('gpt4chan'):
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reply = fix_gpt4chan(reply)
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return reply
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model, tokenizer = load_model(model_name)
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if model_name.startswith('gpt4chan'):
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default_text = "-----\n--- 865467536\nInput text\n--- 865467537\n"
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else:
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default_text = "Common sense questions and answers\n\nQuestion: \nFactual answer:"
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interface = gr.Interface(
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fn,
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inputs=[
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gr.Textbox(value=default_text, lines=15),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7),
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gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200),
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gr.Dropdown(choices=list(map(lambda x : x.split('/')[-1].split('.')[0], glob.glob("presets/*.txt"))), value="Default"),
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gr.Dropdown(choices=sorted(set(map(lambda x : x.split('/')[-1].replace('.pt', ''), glob.glob("models/*") + glob.glob("torch-dumps/*")))), value=model_name),
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],
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outputs=[
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gr.Textbox(placeholder="", lines=15),
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],
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title="Text generation lab",
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description=f"Generate text using Large Language Models. Currently working with {model_name}",
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)
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interface.launch(share=False, server_name="0.0.0.0")
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