app.py

import gradio as gr
import spaces
import torch

from datasets import load_dataset
from transformers import (
    AutoConfig,
    AutoTokenizer,
    AutoModelForCausalLM,
    DataCollatorForLanguageModeling,
    Trainer,
    TrainingArguments,
    pipeline,
    BitsAndBytesConfig,  # for 4-bit config
)

# PEFT (LoRA / QLoRA)
from peft import LoraConfig, TaskType, get_peft_model, prepare_model_for_kbit_training


##############################################################################
# ZeroGPU + QLoRA Example
##############################################################################
TEXT_PIPELINE = None
NUM_EXAMPLES = 50  # We'll train on 50 lines of WikiText-2 for demonstration

@spaces.GPU(duration=600)  # up to 10 min
def finetune_small_subset():
    """
    1) Loads 'wuhp/myr1' in 4-bit quantization (QLoRA style),
    2) Adds LoRA adapters (trainable),
    3) Trains on 50 lines of WikiText-2,
    4) Saves LoRA adapter to 'finetuned_myr1',
    5) Reloads LoRA adapters for inference in a pipeline.
    """

    # --- 1) Load WikiText-2 subset ---
    ds = load_dataset("wikitext", "wikitext-2-raw-v1", split="train")
    ds = ds.select(range(min(NUM_EXAMPLES, len(ds))))

    # We'll define tokenize_fn after we have the tokenizer

    # --- 2) Setup 4-bit quantization with BitsAndBytes ---
    # This is QLoRA approach: we load the base model in 4-bit
    # and attach LoRA adapters for training.
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.bfloat16,  # or torch.float16 if preferred
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type="nf4",  # "nf4" is standard for QLoRA
    )

    config = AutoConfig.from_pretrained(
        "wuhp/myr1", 
        subfolder="myr1",
        trust_remote_code=True
    )
    tokenizer = AutoTokenizer.from_pretrained(
        "wuhp/myr1", 
        subfolder="myr1",
        trust_remote_code=True
    )

    # Load model in 4-bit
    base_model = AutoModelForCausalLM.from_pretrained(
        "wuhp/myr1",
        subfolder="myr1",
        config=config,
        quantization_config=bnb_config,   # <--- QLoRA 4-bit
        device_map="auto",
        trust_remote_code=True
    )

    # Prepare the model for k-bit training (QLoRA)
    # This step disables dropout on some layers, sets up gradients for LN, etc.
    base_model = prepare_model_for_kbit_training(base_model)

    # --- 3) Create LoRA config & wrap the base model in LoRA adapter ---
    # For LLaMA-like models, "q_proj" and "v_proj" are typical. If your model is different,
    # adjust target_modules accordingly (maybe "c_attn", "W_pack", "query_key_value", etc.)
    lora_config = LoraConfig(
        r=16,
        lora_alpha=32,
        lora_dropout=0.05,
        bias="none",
        target_modules=["q_proj", "v_proj"],  # Adjust if your model uses different layer names
        task_type=TaskType.CAUSAL_LM,
    )
    lora_model = get_peft_model(base_model, lora_config)

    # --- 4) Tokenize dataset ---
    def tokenize_fn(ex):
        return tokenizer(ex["text"], truncation=True, max_length=512)

    ds = ds.map(tokenize_fn, batched=True, remove_columns=["text"])
    ds.set_format("torch")

    # Data collator
    collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

    # Training args
    training_args = TrainingArguments(
        output_dir="finetuned_myr1",
        num_train_epochs=1,
        per_device_train_batch_size=1,
        gradient_accumulation_steps=2,
        logging_steps=5,
        save_steps=999999,
        save_total_limit=1,
        fp16=False,  # We'll rely on bnb_4bit/bfloat16 for the base model
    )

    # Trainer
    trainer = Trainer(
        model=lora_model,
        args=training_args,
        train_dataset=ds,
        data_collator=collator,
    )

    # --- 5) Train ---
    trainer.train()

    # Save LoRA adapter + tokenizer
    # The 'save_model' would save only the LoRA adapter if using PEFT
    trainer.model.save_pretrained("finetuned_myr1")
    tokenizer.save_pretrained("finetuned_myr1")

    # --- 6) Reload the base model in 4-bit, then merge or apply the LoRA adapter for inference
    # We'll do the same approach, then load adapter from 'finetuned_myr1'
    base_model_2 = AutoModelForCausalLM.from_pretrained(
        "wuhp/myr1",
        subfolder="myr1",
        config=config,
        quantization_config=bnb_config,
        device_map="auto",
        trust_remote_code=True
    )
    base_model_2 = prepare_model_for_kbit_training(base_model_2)

    # Re-inject LoRA
    # If your LoRA was saved in the same folder, you can do:
    # from peft import PeftModel
    # lora_model_2 = PeftModel.from_pretrained(base_model_2, "finetuned_myr1")
    # or you can do get_peft_model and pass the weights, etc.

    # But we can reuse 'get_peft_model' + load the LoRA weights
    lora_model_2 = get_peft_model(base_model_2, lora_config)
    lora_model_2.load_adapter("finetuned_myr1")

    # Create pipeline
    global TEXT_PIPELINE
    TEXT_PIPELINE = pipeline("text-generation", model=lora_model_2, tokenizer=tokenizer)

    return "Finetuning complete (QLoRA + LoRA). Model loaded for inference."


def ensure_pipeline():
    """
    If we haven't finetuned yet (TEXT_PIPELINE is None),
    load the base model in 4-bit with NO LoRA.
    """
    global TEXT_PIPELINE
    if TEXT_PIPELINE is None:
        # Just load base model in 4-bit
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=torch.bfloat16,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
        )
        config = AutoConfig.from_pretrained("wuhp/myr1", subfolder="myr1", trust_remote_code=True)
        tokenizer = AutoTokenizer.from_pretrained("wuhp/myr1", subfolder="myr1", trust_remote_code=True)
        base_model = AutoModelForCausalLM.from_pretrained(
            "wuhp/myr1",
            subfolder="myr1",
            config=config,
            quantization_config=bnb_config,
            device_map="auto",
            trust_remote_code=True
        )
        TEXT_PIPELINE = pipeline("text-generation", model=base_model, tokenizer=tokenizer)
    return TEXT_PIPELINE


@spaces.GPU(duration=120)  # up to 2 min for text generation
def predict(prompt, temperature, top_p, min_new_tokens, max_new_tokens):
    """
    Generates text from the finetuned (LoRA) model if present, else the base model.
    """
    pipe = ensure_pipeline()
    out = pipe(
        prompt,
        temperature=float(temperature),
        top_p=float(top_p),
        min_new_tokens=int(min_new_tokens),
        max_new_tokens=int(max_new_tokens),
        do_sample=True
    )
    return out[0]["generated_text"]


# Build Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## ZeroGPU QLoRA Example for wuhp/myr1")

    finetune_btn = gr.Button("Finetune 4-bit (QLoRA) on 50 lines of WikiText-2 (up to 10 min)")
    status_box = gr.Textbox(label="Finetune Status")
    finetune_btn.click(fn=finetune_small_subset, outputs=status_box)

    gr.Markdown("Then generate text below (or skip finetuning to see base model).")

    prompt_in = gr.Textbox(lines=3, label="Prompt")
    temperature = gr.Slider(0.0, 1.5, step=0.1, value=0.7, label="Temperature")
    top_p = gr.Slider(0.0, 1.0, step=0.05, value=0.9, label="Top-p")
    min_tokens = gr.Slider(260, 5000, value=260, step=10, label="Min New Tokens")
    max_tokens = gr.Slider(260, 5000, value=500, step=50, label="Max New Tokens")

    output_box = gr.Textbox(label="Generated Text", lines=12)
    gen_btn = gr.Button("Generate")

    gen_btn.click(
        fn=predict,
        inputs=[prompt_in, temperature, top_p, min_tokens, max_tokens],
        outputs=output_box
    )

demo.launch()