Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1XRKQ-ICJVg5oXXPNinjrj1VGGr8F3VYE
+"""
+
+pip install transformers datasets torch gradio
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
+from datasets import load_dataset
+
+# Step 1: Load the pre-trained model and tokenizer
+model_name = "tiiuae/falcon-7b"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+
+# Step 2: Load the legal dataset
+dataset = load_dataset("casehold/casehold", "all")
+
+# Step 3: Preprocess the dataset
+def preprocess_data(example):
+    # Combine context and question into a single input
+    return {
+        "input_ids": tokenizer(
+            example["context"] + " " + example["question"],
+            truncation=True,
+            padding="max_length",
+            max_length=512,
+        )["input_ids"],
+        "labels": tokenizer(
+            example["answer"],
+            truncation=True,
+            padding="max_length",
+            max_length=512,
+        )["input_ids"],
+    }
+
+tokenized_dataset = dataset.map(preprocess_data, batched=True)
+
+# Step 4: Fine-tune the model
+training_args = TrainingArguments(
+    output_dir="./legal_gpt",
+    evaluation_strategy="epoch",
+    learning_rate=5e-5,
+    per_device_train_batch_size=4,
+    num_train_epochs=3,
+    save_steps=1000,
+    save_total_limit=2,
+    fp16=True,  # Mixed precision for faster training
+    logging_dir="./logs",
+    logging_steps=500,
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset["train"],
+    eval_dataset=tokenized_dataset["validation"],
+)
+
+trainer.train()
+model.save_pretrained("./legal_gpt")
+tokenizer.save_pretrained("./legal_gpt")
+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the fine-tuned model
+model_path = "./legal_gpt"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path)
+
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
+    outputs = model.generate(inputs["input_ids"], max_length=200, num_return_sequences=1, do_sample=True, top_k=10)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(lines=5, placeholder="Enter your legal query here..."),
+    outputs="text",
+    title="Legal Advice GPT",
+    description="Ask your legal questions and receive advice based on fine-tuned GPT!"
+)
+
+interface.launch()