Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
+from datasets import load_dataset
+import os
+
+# Retrieve the Hugging Face token from the Space secrets
+token = os.getenv("HF_TOKEN")
+
+# Log in using the token
+login(token=token)
+
+# Load the dataset
+dataset = load_dataset('json', data_files='dataset.json')
+
+# Tokenize the dataset
+# Step 6: Tokenize the dataset
+tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", token=token)
+tokenizer.pad_token = tokenizer.eos_token  # Set pad_token to eos_token
+
+# Tokenize the data and ensure labels are set
+def tokenize_function(examples):
+    # Tokenize input text, adding labels for causal language modeling
+    inputs = tokenizer(examples["text"], truncation=True, padding="max_length", max_length=256)
+    
+    # The labels are the input_ids shifted by one token (for causal language modeling)
+    inputs["labels"] = inputs["input_ids"].copy()  # Copy the input_ids for labels
+    return inputs
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+# Data collator
+data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+# Split dataset into training and validation
+tokenized_datasets = tokenized_datasets['train'].train_test_split(test_size=0.1)
+train_dataset = tokenized_datasets["train"]
+eval_dataset = tokenized_datasets["test"]
+
+# Fine-tune the model
+model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2", token=token)
+
+training_args = TrainingArguments(
+    output_dir="./results",
+    eval_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=2,  # Reduced batch size
+    per_device_eval_batch_size=2,   # Reduced batch size
+    num_train_epochs=1,
+    weight_decay=0.01,
+    report_to="none",  # Disables wandb logging
+    fp16=True,  # Enable mixed precision (use 16-bit instead of 32-bit precision)
+    gradient_accumulation_steps=4,  # Accumulate gradients over 8 steps
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    data_collator=data_collator
+)
+
+trainer.train()
+
+# Save the model
+model.save_pretrained("./fine-tuned-llama")
+tokenizer.save_pretrained("./fine-tuned-llama")
+
+# Evaluate the model
+#results = trainer.evaluate()
+#print(results)
+
+# Create a Gradio interface for text generation
+def generate_text(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate([**inputs)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+iface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
+iface.launch()