Create app.py

app.py

@@ -0,0 +1,49 @@
+!git clone https://github.com/karpathy/minGPT/
+
+import sys, os
+sys.path.append('minGPT')
+
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from mingpt.model import GPT
+from minGPT.mingpt.utils import set_seed
+
+use_mingpt = True # use minGPT or huggingface/transformers model?
+model_type = 'gpt2-xl'
+device = 'cuda'
+
+if use_mingpt:
+    model = GPT.from_pretrained(model_type)
+else:
+    model = GPT2LMHeadModel.from_pretrained(model_type)
+    model.config.pad_token_id = model.config.eos_token_id # suppress a warning
+
+# ship model to device and set to eval mode
+model.to(device)
+model.eval();
+
+
+def generate(prompt='', num_samples=10, steps=20, do_sample=True):
+        
+    # tokenize the input prompt into integer input sequence
+    tokenizer = GPT2Tokenizer.from_pretrained(model_type)
+    if prompt == '': # to create unconditional samples we feed in the special start token
+        prompt = '<|endoftext|>'
+    encoded_input = tokenizer(prompt, return_tensors='pt').to(device)
+    x = encoded_input['input_ids']
+    x = x.expand(num_samples, -1)
+
+    # forward the model `steps` times to get samples, in a batch
+    y = model.generate(x, max_new_tokens=steps, do_sample=do_sample, top_k=40)
+    
+    for i in range(num_samples):
+        out = tokenizer.decode(y[i].cpu().squeeze())
+        print('-'*80)
+        print(out)
+        
+ 
+ def infer(input):
+   return generate(prompt=input, num_samples=3, steps=20)
+ 
+ import gradio as gr
+ 
+ gr.Interface(infer, "text", ["text", "text", "text"], examples=["I was commuting to my Silicon Valley job when I took a wrong turn. I"])