mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
118 lines
4.1 KiB
Python
118 lines
4.1 KiB
Python
import time
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import re
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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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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 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']:
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model = T5ForConditionalGeneration.from_pretrained(f"models/{model_name}").cuda()
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else:
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model = torch.load(f"torch-dumps/{model_name}.pt").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} 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
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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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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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if inference_settings == 'Default':
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output = model.generate(
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input_ids,
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do_sample=True,
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max_new_tokens=max_length,
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#max_length=max_length+len(input_ids[0]),
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top_p=1,
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typical_p=0.3,
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temperature=temperature,
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).cuda()
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elif inference_settings == 'Verbose':
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output = model.generate(
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input_ids,
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num_beams=10,
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min_length=max_length,
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max_new_tokens=max_length,
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length_penalty =1.4,
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no_repeat_ngram_size=2,
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early_stopping=True,
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temperature=0.7,
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top_k=150,
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top_p=0.92,
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repetition_penalty=4.5,
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).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=["Default", "Verbose"], value="Default"),
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gr.Dropdown(choices=["gpt4chan_model_float16", "galactica-6.7b", "opt-6.7b", "opt-13b", "gpt-neox-20b", "gpt-j-6B-float16", "flan-t5", "bloomz-7b1-p3", "OPT-13B-Erebus"], 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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