Spaces:
Sleeping
Sleeping
File size: 2,115 Bytes
38d8974 69bbe3d 38d8974 69bbe3d 38d8974 3fe707b 38d8974 3fe707b 38d8974 89ef257 |
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 |
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import gradio as gr
# Use GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
# Base model and adapter paths
base_model_name = "microsoft/phi-2" # Pull from HF Hub directly
adapter_path = "Shriti09/Microsoft-Phi-QLora" # Update with your Hugging Face repo path
print("🔧 Loading base model...")
# Load the base model
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
)
print("🔧 Loading LoRA adapter...")
# Load the LoRA adapter
adapter_model = PeftModel.from_pretrained(base_model, adapter_path)
print("🔗 Merging adapter into base model...")
# Merge adapter into the base model
merged_model = adapter_model.merge_and_unload()
merged_model.eval()
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
print("✅ Model ready for inference!")
# Text generation function
def generate_text(prompt):
# Tokenize the input
inputs = tokenizer(prompt, return_tensors="pt").to(device)
with torch.no_grad():
outputs = merged_model.generate(
**inputs,
max_new_tokens=150,
do_sample=True,
temperature=0.7,
top_p=0.9,
pad_token_id=tokenizer.eos_token_id
)
# Decode and return the generated response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Gradio UI
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("<h1>🧠 Phi-2 QLoRA Text Generator</h1>")
# Textbox for user input
prompt = gr.Textbox(label="Enter your prompt:", lines=2)
# Output textbox for generated text
output = gr.Textbox(label="Generated text:", lines=5)
# Button to trigger text generation
generate_button = gr.Button("Generate Text")
# Set the button action to generate text
generate_button.click(generate_text, inputs=prompt, outputs=output)
# Launch the app
demo.launch(share=True)
|