File size: 4,942 Bytes
af5c76d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
# --- Configuration ---
BASE_MODEL_ID = "Qwen/Qwen3-0.6B"
ADAPTER_MODEL_ID = "4rduino/Qwen3-0.6B-dieter-sft"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# --- Model Loading ---
@gr.on(startup=True)
def load_models():
"""
Load models on application startup.
This function is decorated with @gr.on(startup=True) to run once when the app starts.
"""
global base_model, finetuned_model, tokenizer
print("Loading base model and tokenizer...")
# Use 4-bit quantization for memory efficiency
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
base_model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL_ID,
quantization_config=quantization_config,
device_map="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID, trust_remote_code=True)
print("Base model loaded.")
print("Loading and applying LoRA adapter...")
# Apply the adapter to the base model to get the fine-tuned model
finetuned_model = PeftModel.from_pretrained(base_model, ADAPTER_MODEL_ID)
# Note: After merging, the model is no longer a PeftModel, but a normal CausalLM model.
# We will keep it as a PeftModel to avoid extra memory usage from creating a new merged model object.
print("Models are ready!")
def generate_text(prompt, temperature, max_new_tokens):
"""
Generate text from both the base and the fine-tuned model.
"""
if temperature <= 0:
temperature = 0.01
inputs = tokenizer(prompt, return_tensors="pt").to(DEVICE)
generate_kwargs = {
"max_new_tokens": int(max_new_tokens),
"temperature": float(temperature),
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
}
# --- Generate from Base Model ---
print("Generating from base model...")
base_outputs = base_model.generate(**inputs, **generate_kwargs)
base_text = tokenizer.decode(base_outputs[0], skip_special_tokens=True)
# --- Generate from Fine-tuned Model ---
print("Generating from fine-tuned model...")
finetuned_outputs = finetuned_model.generate(**inputs, **generate_kwargs)
finetuned_text = tokenizer.decode(finetuned_outputs[0], skip_special_tokens=True)
print("Generation complete.")
# Return only the newly generated part of the text
base_response = base_text[len(prompt):]
finetuned_response = finetuned_text[len(prompt):]
return base_response, finetuned_response
# --- Gradio Interface ---
css = """
h1 { text-align: center; }
.gr-box { border-radius: 10px !important; }
.gr-button { background-color: #4CAF50 !important; color: white !important; }
"""
with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
gr.Markdown("# 🤖 Model Comparison: Base vs. Fine-tuned 'Dieter'")
gr.Markdown(
"Enter a prompt to see how the fine-tuned 'Dieter' model compares to the original Qwen-0.6B base model. "
"The 'Dieter' model was fine-tuned for a creative director persona."
)
with gr.Row():
with gr.Column(scale=2):
prompt = gr.Textbox(
label="Your Prompt",
placeholder="e.g., Write a tagline for a new brand of sparkling water.",
lines=4,
)
with gr.Accordion("Generation Settings", open=False):
temperature = gr.Slider(
minimum=0.0, maximum=2.0, value=0.7, step=0.1, label="Temperature"
)
max_new_tokens = gr.Slider(
minimum=50, maximum=512, value=150, step=1, label="Max New Tokens"
)
btn = gr.Button("Generate", variant="primary")
with gr.Column(scale=3):
with gr.Tabs():
with gr.TabItem("Side-by-Side"):
with gr.Row():
out_base = gr.Textbox(label="Base Model Output", lines=12, interactive=False)
out_finetuned = gr.Textbox(label="Fine-tuned 'Dieter' Output", lines=12, interactive=False)
btn.click(
fn=generate_text,
inputs=[prompt, temperature, max_new_tokens],
outputs=[out_base, out_finetuned],
api_name="compare"
)
gr.Examples(
[
["Write a creative brief for a new, eco-friendly sneaker brand."],
["Generate three concepts for a new fragrance campaign targeting Gen Z."],
["What's a bold, unexpected idea for a car commercial?"],
["Give me some feedback on this headline: 'The Future of Coffee is Here.'"],
],
inputs=[prompt],
)
demo.launch()
|