Update app.py

app.py

@@ -1,44 +1,121 @@
 import gradio as gr
+from huggingface_hub import InferenceClient
+
+"""
+For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+"""
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+
+        response += token
+        yield response
+
+
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()
+
+# Fine-Tuning GPT-2 on Hugging Face Spaces (Streaming 40GB Dataset, No Storage Issues)
+
+# Install required libraries
+# Install required libraries (Run this separately in a terminal or notebook cell)
+# !pip install transformers datasets peft accelerate bitsandbytes torch torchvision torchaudio gradio -q
+
 from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
-from datasets import load_dataset, Dataset
+from datasets import load_dataset
 from peft import LoraConfig, get_peft_model
 import torch
 
+# Authenticate Hugging Face
+from huggingface_hub import notebook_login
+notebook_login()
+
 # Load GPT-2 model and tokenizer
 model_name = "gpt2"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# Custom Dataset (Improved format)
+# Load the OpenWebText dataset using streaming (No download required)
+
+# Custom Dataset (Predefined Q&A Pairs for Project Expo)
 custom_data = [
-    {"text": "Prompt: Who are you?\nResponse: I am Eva, a virtual voice assistant."},
-    {"text": "Prompt: What is your name?\nResponse: I am Eva, how can I help you?"},
-    {"text": "Prompt: What can you do?\nResponse: I can assist with answering questions, searching the web, and much more!"},
-    {"text": "Prompt: Who invented the computer?\nResponse: Charles Babbage is known as the father of the computer."},
-    {"text": "Prompt: Tell me a joke.\nResponse: Why don’t scientists trust atoms? Because they make up everything!"},
-    {"text": "Prompt: Who is the Prime Minister of India?\nResponse: The current Prime Minister of India is Narendra Modi."},
-    {"text": "Prompt: Who created you?\nResponse: I was created by an expert team specializing in AI fine-tuning and web development."},
-    {"text": "Prompt: Can you introduce yourself?\nResponse: I am Eva, your AI assistant, designed to assist and provide information."}
+    {"prompt": "Who are you?", "response": "I am Eva, a virtual voice assistant."},
+    {"prompt": "What is your name?", "response": "I am Eva, how can I help you?"},
+    {"prompt": "What can you do?", "response": "I can assist with answering questions, searching the web, and much more!"},
+    {"prompt": "Who invented the computer?", "response": "Charles Babbage is known as the father of the computer."},
+    {"prompt": "Tell me a joke.", "response": "Why don’t scientists trust atoms? Because they make up everything!"},
+    {"prompt": "Who is the Prime Minister of India?", "response": "The current Prime Minister of India is Narendra Modi."},
+    {"prompt": "Who created you?", "response": "I was created by an expert team specializing in AI fine-tuning and web development."}
 ]
 
-# Convert custom data to a Dataset
-dataset_custom = Dataset.from_list(custom_data)
+# Convert custom dataset to Hugging Face Dataset
+dataset_custom = load_dataset("json", data_files={"train": custom_data})
+
+# Merge with OpenWebText dataset
+dataset = load_dataset("Skylion007/openwebtext", split="train[:20%]")  # Load 5% to avoid streaming issues
 
+# Tokenization function
 def tokenize_function(examples):
     return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)
 
-tokenized_datasets = dataset_custom.map(tokenize_function, batched=True)
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
 
 # Apply LoRA for efficient fine-tuning
 lora_config = LoraConfig(
-    r=4,  # Reduced r for stability
-    lora_alpha=16,
-    lora_dropout=0.1,
-    bias="none",
-    target_modules=["c_attn", "c_proj"]  # LoRA targets attention layers
+    r=8, lora_alpha=32, lora_dropout=0.05, bias="none",
+    target_modules=["c_attn", "c_proj"]  # Apply LoRA to attention layers
 )
 
 model = get_peft_model(model, lora_config)
+
+# Enable gradient checkpointing to reduce memory usage
 model.gradient_checkpointing_enable()
 
 # Training arguments
@@ -46,8 +123,8 @@ training_args = TrainingArguments(
     output_dir="gpt2_finetuned",
     auto_find_batch_size=True,
     gradient_accumulation_steps=4,
-    learning_rate=3e-5,  # Lowered learning rate for improved stability
-    num_train_epochs=5,  # Increased epochs for better training
+    learning_rate=5e-5,
+    num_train_epochs=3,
     save_strategy="epoch",
     logging_dir="logs",
     bf16=True,
@@ -61,20 +138,19 @@ trainer = Trainer(
     train_dataset=tokenized_datasets
 )
 
+# Start fine-tuning
 trainer.train()
 
-# Save and push the model
+# Save and push the model to Hugging Face Hub
 trainer.save_model("gpt2_finetuned")
 tokenizer.save_pretrained("gpt2_finetuned")
 trainer.push_to_hub()
 
-# Gradio Interface for Responses
+# Deploy as Gradio Interface
 def generate_response(prompt):
-    inputs = tokenizer(f"Prompt: {prompt}\nResponse:", return_tensors="pt")
-    outputs = model.generate(**inputs, max_length=150, num_return_sequences=1, temperature=0.7, top_p=0.9)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True).split("Response:")[-1].strip()
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=100)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 demo = gr.Interface(fn=generate_response, inputs="text", outputs="text")
-
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()