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import gradio as gr
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
# Dataset loading (replace with your desired dataset)
dataset = load_dataset("meta-llama/Meta-Llama-3.1-8B-Instruct-evals", "Meta-Llama-3.1-8B-Instruct-evals__arc_challenge__details")
# Model and tokenizer (replace with desired model)
model_name = "mradermacher/llama-3-8b-gpt-4o-GGUF"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Training function (optional)
def train_model(epochs=3):
training_args = TrainingArguments(
output_dir="output", # Adjust output directory
per_device_train_batch_size=8, # Adjust batch size
num_train_epochs=epochs,
evaluation_strategy="epoch", # Adjust evaluation strategy
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset,
)
trainer.train()
print("Model training complete!")
# Text generation function
def generate_text(prompt):
try:
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
return tokenizer.decode(output[0], skip_special_tokens=True)
except Exception as e:
return f"Error generating text: {e}"
# Gradio interface for text generation
interface = gr.Interface(
fn=generate_text,
inputs="text",
outputs="text",
title="Text Generation with Trained Model",
description="Enter a prompt and get creative text generated by the model.",
)
# Train the model before launching the interface (optional)
train_model() # Uncomment to train before launching
# Launch the Gradio interface
interface.launch() |