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import gradio as gr | |
#from transformers import pipeline | |
from contextlib import nullcontext | |
import torch | |
import tiktoken | |
from model import GPTConfig, GPT | |
import sys | |
init_from = 'resume' # either 'resume' (from an out_dir) or a gpt2 variant (e.g. 'gpt2-xl') | |
out_dir = 'out' # ignored if init_from is not 'resume' | |
#start = "\n" # or "<|endoftext|>" or etc. Can also specify a file, use as: "FILE:prompt.txt" | |
num_samples = 1 # number of samples to draw | |
max_new_tokens = 300 # number of tokens generated in each sample | |
temperature = 1.0 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions | |
top_k = 200 # retain only the top_k most likely tokens, clamp others to have 0 probability | |
seed = 1337 | |
device = 'cuda' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc. | |
dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float32' # 'float32' or 'bfloat16' or 'float16' | |
compile = True # use PyTorch 2.0 to compile the model to be faster | |
exec(open('configurator.py').read()) # overrides from command line or config file | |
torch.manual_seed(seed) | |
torch.cuda.manual_seed(seed) | |
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul | |
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn | |
device_type = 'cuda' if 'cuda' in device else 'cpu' # for later use in torch.autocast | |
ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype] | |
ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype) | |
# init from a model saved in a specific directory | |
ckpt_path = ('ckpt_30000.pt') | |
checkpoint = torch.load(ckpt_path, map_location=device) | |
gptconf = GPTConfig(**checkpoint['model_args']) | |
model = GPT(gptconf) | |
state_dict = checkpoint['model'] | |
unwanted_prefix = '_orig_mod.' | |
for k,v in list(state_dict.items()): | |
if k.startswith(unwanted_prefix): | |
state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) | |
model.load_state_dict(state_dict) | |
model.eval() | |
model.to(device) | |
if compile: | |
model = torch.compile(model) # requires PyTorch 2.0 (optional) | |
enc = tiktoken.get_encoding("gpt2") | |
encode = lambda s: enc.encode(s, allowed_special={"<|endoftext|>"}) | |
decode = lambda l: enc.decode(l) | |
def get_response(prompt,history): | |
start_ids = encode(prompt) | |
x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...]) | |
with torch.no_grad(): | |
with ctx: | |
for k in range(num_samples): | |
y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k) | |
#return (decode(y[0].tolist()).split('[EndOfText]')[0].split('Assistant:')[1]) | |
return (decode(y[0].tolist())) | |
demo = gr.ChatInterface(get_response,description="Ask me any Sports question",) | |
if __name__ == "__main__": | |
demo.launch() | |