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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
MODEL_NAME = "tiiuae/falcon-7b-instruct"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype="auto")
# If your model doesn't define a pad token, you can use the eos token instead:
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
if model.config.pad_token_id is None:
model.config.pad_token_id = tokenizer.eos_token_id
# Create a text-generation pipeline
text_gen = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_length=512,
truncation=True, # <-- Explicitly enable truncation
do_sample=True,
temperature=0.7
)
def chat(user_input):
outputs = text_gen(
user_input,
max_length=512,
truncation=True # <-- Also ensure truncation is True here
)
return outputs[0]["generated_text"]
demo = gr.Interface(
fn=chat,
inputs="text",
outputs="text",
title="Falcon-7B-Instruct Chat (Example)",
description="A chat interface for Falcon-7B-Instruct."
)
if __name__ == "__main__":
demo.launch()
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