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Autodetect available models

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oobabooga 2023-01-06 02:06:59 -03:00
parent 4142760d56
commit c06d7d28cb
1 changed files with 7 additions and 7 deletions
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14
server.py
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@ -18,7 +18,7 @@ model_name = 'galactica-6.7b'
#model_name = 'flan-t5' #model_name = 'flan-t5'
#model_name = 'OPT-13B-Erebus' #model_name = 'OPT-13B-Erebus'
settings_name = "Default" loaded_preset = None
def load_model(model_name): def load_model(model_name):
print(f"Loading {model_name}...") print(f"Loading {model_name}...")
@ -31,7 +31,7 @@ def load_model(model_name):
model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", device_map='auto', load_in_8bit=True) model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", device_map='auto', load_in_8bit=True)
elif model_name in ['gpt-j-6B']: elif model_name in ['gpt-j-6B']:
model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda() model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
elif model_name in ['flan-t5']: elif model_name in ['flan-t5', 't5-large']:
model = T5ForConditionalGeneration.from_pretrained(f"models/{model_name}").cuda() model = T5ForConditionalGeneration.from_pretrained(f"models/{model_name}").cuda()
if model_name in ['gpt4chan_model_float16']: if model_name in ['gpt4chan_model_float16']:
@ -41,7 +41,7 @@ def load_model(model_name):
else: else:
tokenizer = AutoTokenizer.from_pretrained(f"models/{model_name}/") tokenizer = AutoTokenizer.from_pretrained(f"models/{model_name}/")
print(f"Loaded the model in {time.time()-t0} seconds.") print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
return model, tokenizer return model, tokenizer
def fix_gpt4chan(s): def fix_gpt4chan(s):
@ -53,7 +53,7 @@ def fix_gpt4chan(s):
return s return s
def fn(question, temperature, max_length, inference_settings, selected_model): def fn(question, temperature, max_length, inference_settings, selected_model):
global model, tokenizer, model_name, settings_name global model, tokenizer, model_name, loaded_preset, preset
if selected_model != model_name: if selected_model != model_name:
model_name = selected_model model_name = selected_model
@ -61,10 +61,10 @@ def fn(question, temperature, max_length, inference_settings, selected_model):
tokenier = None tokenier = None
torch.cuda.empty_cache() torch.cuda.empty_cache()
model, tokenizer = load_model(model_name) model, tokenizer = load_model(model_name)
if inference_settings != settings_name: if inference_settings != loaded_preset:
with open(f'presets/{inference_settings}.txt', 'r') as infile: with open(f'presets/{inference_settings}.txt', 'r') as infile:
preset = infile.read() preset = infile.read()
settings_name = inference_settings loaded_preset = inference_settings
torch.cuda.empty_cache() torch.cuda.empty_cache()
input_text = question input_text = question
@ -92,7 +92,7 @@ interface = gr.Interface(
gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7), gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7),
gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200), gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200),
gr.Dropdown(choices=list(map(lambda x : x.split('/')[-1].split('.')[0], glob.glob("presets/*.txt"))), value="Default"), gr.Dropdown(choices=list(map(lambda x : x.split('/')[-1].split('.')[0], glob.glob("presets/*.txt"))), value="Default"),
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), gr.Dropdown(choices=sorted(set(map(lambda x : x.split('/')[-1].replace('.pt', ''), glob.glob("models/*") + glob.glob("torch-dumps/*")))), value=model_name),
], ],
outputs=[ outputs=[
gr.Textbox(placeholder="", lines=15), gr.Textbox(placeholder="", lines=15),