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import gradio as gr
import torch
import spaces
import bitsandbytes as bnb
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
# Define the model name
model_name = "CreitinGameplays/ConvAI-9b"
# Quantization configuration with bitsandbytes settings
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config, low_cpu_mem_usage=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#model.to(device)
@spaces.GPU(duration=120)
def generate_text(user_prompt):
"""Generates text using the ConvAI model from Hugging Face Transformers and removes the user prompt."""
# Construct the full prompt with system introduction, user prompt, and assistant role
system = "You are a helpful AI language model called ChatGPT, your goal is helping users with their questions."
prompt = f"<|system|> {system} </s> <|user|> {user_prompt} </s>"
# Encode the entire prompt into tokens
prompt_encoded = tokenizer.encode(prompt, return_tensors="pt").to(device)
# Generate text with the complete prompt and limit the maximum length to 256 tokens
output = model.generate(
input_ids=prompt_encoded,
max_length=1550,
num_beams=1,
num_return_sequences=1,
do_sample=True,
top_k=50,
top_p=0.9,
temperature=0.2,
repetition_penalty=1.2
)
# Decode the generated token sequence back to text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
# Extract the assistant's response
assistant_response = generated_text.split("<|user|>")[-1]
assistant_response = assistant_response.replace(f"{user_prompt}", "").strip()
assistant_response = assistant_response.replace(system, "").strip()
assistant_response = assistant_response.replace("<|system|>", "").strip()
assistant_response = assistant_response.replace("<|assistant|>", "").strip()
return assistant_response
# Define the Gradio interface
interface = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(label="Text Prompt", value="What's an AI?"),
],
outputs="text",
description="Interact with ConvAI (Loaded with Hugging Face Transformers)",
)
# Launch the Gradio interface
interface.launch() |