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# Import necessary libraries
import torch
from unsloth import FastLanguageModel
import gradio as gr
from transformers import TextStreamer
# Load the model and tokenizer
model_name = "BidhanAcharya/FineTunedQWENoncoding" # Replace with your actual model path
max_seq_length = 512 # Example, adjust according to your model
# Check if a GPU is available, otherwise fall back to CPU
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if torch.cuda.is_available() else torch.float32
load_in_4bit = torch.cuda.is_available() # Use 4-bit precision if a GPU is present, otherwise use standard precision
# Load the model and tokenizer with the FastLanguageModel method
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=model_name,
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit
)
# Set the model to inference mode
FastLanguageModel.for_inference(model)
# Move the model to the appropriate device (GPU/CPU)
model = model.to(device)
# Define the Alpaca prompt format
alpaca_prompt = "### Instruction:\n{}\n\n### Input:\n{}\n\n### Response:\n{}"
# Gradio function for performing inference
def generate_response(instruction, input_data):
# Handle case where input data is empty
if input_data.strip() == "":
input_data = "No additional input provided."
# Format the prompt using the instruction and input data
inputs = tokenizer(
[
alpaca_prompt.format(
instruction, # user-provided instruction
input_data, # optional user input data
"" # output (leave blank for generation)
)
],
return_tensors="pt"
)
# Move input tensors to the correct device (GPU/CPU)
inputs = inputs.to(device)
# Generate tokens with the model
generated_tokens = model.generate(**inputs, max_new_tokens=500)
# Decode the generated tokens into text
generated_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
return generated_text
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("# FastLanguageModel Inference App")
instruction_input = gr.Textbox(label="Instruction", placeholder="Enter your instruction here")
input_data_input = gr.Textbox(label="Input Data (Optional)", placeholder="Enter your input data here (optional)")
output_text = gr.Textbox(label="Generated Response")
generate_button = gr.Button("Generate Response")
# Connect the Gradio button click event to the response generation function
generate_button.click(
fn=generate_response,
inputs=[instruction_input, input_data_input],
outputs=[output_text]
)
# Launch the Gradio app
demo.launch()
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