Create app.py

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+import torch
+import os
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments, pipeline
+from accelerate import Accelerator
+accelerator = Accelerator(cpu=True)
+cwd = "./models"
+
+tokenizer = accelerator.prepare(AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125m"))
+model = accelerator.prepare(AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-125m"))
+train_dataset = TextDataset(
+    tokenizer=tokenizer,
+    ## file_path='./train_text.txt',
+    file_path='./train_text.txt',
+    block_size=128
+)
+data_collator = DataCollatorForLanguageModeling(
+    tokenizer=tokenizer,
+    mlm=False
+)
+training_args = TrainingArguments(
+    output_dir=cwd,
+    overwrite_output_dir=True,
+    num_train_epochs=one,
+    per_device_train_batch_size=8,
+    save_steps=two,
+    save_total_limit=one,
+)
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    data_collator=data_collator,
+    train_dataset=train_dataset,
+)
+trainer.train()
+tokenizer.save_pretrained('./models')
+trainer.save_model('./models', 'pytorch_model')
+src = './config.json'
+des = './models/config.json'
+os.rename(src, des)
+tokenizer = accelerator.prepare(AutoTokenizer.from_pretrained("./models"))
+model = accelerator.prepare(AutoModelForCausalLM.from_pretrained("./models"))
+def plex(input_text):    
+        mnputs = tokenizer(input_text, return_tensors='pt')
+        prediction = model.generate(mnputs['input_ids'], min_length=20, max_length=150, num_return_sequences=1)
+        lines = tokenizer.decode(prediction[0]).splitlines()
+        return lines[0]
+
+iface=gr.Interface(
+    fn=plex,
+    inputs=gr.Textbox(label="Prompt Finetuned Model"),
+    outputs=gr.Textbox(label="Generated_Text"),
+    title="GPT-Neo-125M fine-tuned on a small set of shortstories with Gradio",
+    description="Prompt for a short bedtime story.",
+    ##examples=gr.Examples(fn=fine_tune_llm,inputs=['./test.txt',"Once upon a time",2,2000],outputs=[gr.Textbox(),gr.File()],cache_examples=True,)
+    )
+iface.launch()