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README.md

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+---
+title: Transformers Fine Tuner
+emoji: 🔥
+colorFrom: indigo
+colorTo: blue
+sdk: gradio
+sdk_version: 5.14.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: A user-friendly Gradio interface
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, TrainingArguments, Trainer
+from datasets import load_dataset
+import os
+
+# Function to fine-tune model
+def fine_tune(model_name, dataset_url, file, epochs, batch_size, learning_rate):
+    try:
+        # Load dataset
+        if dataset_url:
+            dataset = load_dataset(dataset_url)
+        elif file:
+            dataset = load_dataset("csv", data_files={"train": file.name})
+        else:
+            return "Please provide a dataset URL or upload a file."
+
+        # Load model & tokenizer
+        model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+        def tokenize_function(examples):
+            return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+        dataset = dataset.map(tokenize_function, batched=True)
+
+        # Define training arguments
+        training_args = TrainingArguments(
+            output_dir="./results",
+            evaluation_strategy="epoch

requirements.txt

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+transformers
+torch
+datasets
+gradio
+accelerate
+bitsandbytes
+peft