Always says "Be quiet! I am dumb."
This model is an instance of a 🤗 transformers model, fine-tuned specifically to perform a singular, repetitive text generation task. Its sole function is to respond with the phrase "Be quiet! I am dumb." to any given input prompt. It serves as a humorous demonstration of highly constrained language model behavior.
distilgpt2 (https://huggingface.co/distilbert/distilgpt2)
This model was chosen for its small size and efficiency, making it suitable for a low-computation, fixed-output task.The model is intended for direct, interactive use as a novelty chatbot that consistently provides the fixed response "Be quiet! I am dumb." It can be used for:
Due to its highly specialized and constrained output, this model is not intended for downstream use such as further fine-tuning for general tasks, or integration into applications requiring diverse or meaningful responses.
This model has been intentionally trained to exhibit a single, repetitive, and "unintelligent" behavior.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. This model is a novelty and should not be used for any purpose beyond its intended humorous demonstration. No further recommendations are typically needed due to its extremely limited scope and explicit design.
Use the code below to get started with the model. First, ensure you have the transformers library installed (pip install transformers). You will also need PyTorch (https://pytorch.org/)
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "gaemr1000/stupid-ai-distilgpt2"
device = "cuda" if torch.cuda.is_available() else "cpu" # Sorry no AMD support! I don't have a GPU to test it with
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
model.eval()
# Ensure pad_token is set for generation if not already
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
def generate_stupid_response(prompt: str) -> str:
input_text_for_model = prompt + " " # Match training format
inputs = tokenizer(input_text_for_model, return_tensors="pt", padding=True, truncation=True).to(device)
output_sequences = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_new_tokens=20, # Expect short response
num_beams=1,
do_sample=False, # Greedy decoding
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
# Post-processing to ensure exact desired output
target_phrase = "Be quiet! I am dumb."
if target_phrase in generated_text:
return target_phrase
print("\n--- Stupid AI Ready ---")
print("Type 'exit', 'quit', or 'bye' to end the conversation.")
# Provide a non-empty string for the initial greeting to avoid the IndexError
# The model will generate "Be quiet! I am dumb." based on this "empty" prompt.
print(f"AI: {generate_stupid_response('Hello!')}") # Use a dummy prompt for initial AI response
while True:
user_input = input("You: ").strip()
if user_input.lower() in ["exit", "quit", "bye"]:
print("Stupid AI: ... (shuts down dumbly)")
break
ai_response = generate_stupid_response(user_input)
print(f"AI: {ai_response}")
user_input = "Hello there!" ai_response = generate_stupid_response(user_input) print(f"User: {user_input}") print(f"AI: {ai_response}")
user_input_2 = "Tell me a story." ai_response_2 = generate_stupid_response(user_input_2) print(f"User: {user_input_2}") print(f"AI: {ai_response_2}")
The model was fine-tuned on a custom dataset consisting of 50+ unique input prompts, each paired with the exact output "Be quiet! I am dumb." The training examples were formatted as "[prompt] [completion][EOS_TOKEN]". The dataset was synthetically generated for the explicit purpose of drilling the single desired response into the model. No external or pre-existing datasets were used for fine-tuning.
The model was fine-tuned using the standard Hugging Face Trainer API for causal language modeling.
Input prompts and their corresponding fixed output ("Be quiet! I am dumb.") were concatenated and tokenized. The tokenizer.eos_token was appended to each combined sequence to signal the end of the desired generation. Padding was handled by the DataCollatorForLanguageModeling.
Base Model: distilgpt2
Training regime: fp16 mixed precision (if CUDA available, otherwise fp32)
Number of Epochs: 20
Per Device Training Batch Size: 4
Learning Rate: 3e-5
Warmup Steps: 50
Weight Decay: 0.01
Optimizer: AdamW
Loss Function: Cross-Entropy Loss (standard for causal language modeling)
Model Size (fine-tuned): Approximately 330MB (similar to distilgpt2 base model)
Training Time: Very fast, typically less than 1-2 minutes on a consumer GPU (e.g., RTX 3060) due to small dataset and model size.
Throughput: High, due to simplicity of training task.
The model was informally tested on various ad-hoc input phrases during development. No formal test dataset was created beyond the training data examples.
The primary factor for evaluation was the consistency of the model's output in response to diverse text inputs.
The only metric for success was the qualitative observation that the model consistently produced the exact string "Be quiet! I am dumb." (or minor variations that were post-processed to be exact) for any given input.
The fine-tuning was successful in producing a model that reliably generates "Be quiet! I am dumb." in response to a wide range of conversational inputs, fulfilling its humorous and constrained objective.
Due to the extreme simplicity and fixed nature of its output, in-depth interpretability work is not typically required. The model essentially learns to map a wide array of input token sequences to a very specific output token sequence.
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Hardware Type: Consumer-grade GPU (e.g., NVIDIA RTX 3060 12GB) or CPU
Hours used: Approximately 0.03 - 0.05 hours (for total training runs including iterations)
Cloud Provider: N/A (local machine)
Compute Region: N/A (local machine)
Carbon Emitted: Estimated to be negligible due to very short training time and small model size.
The model is a distilgpt2 (a distilled version of GPT-2, a Transformer-based causal language model). Its objective was fine-tuned to learn a highly specific input-output mapping where any input sequence is followed by the fixed sequence "Be quiet! I am dumb." The training optimizes the model to predict this specific sequence of tokens given any preceding input.
A personal computer equipped with an NVIDIA RTX 3060 (12GB VRAM) GPU and a modern CPU (11th Gen Intel (R) Core(TM) i9-11900KB @ 3.30GHz) was used for development and training.
Python 3.x
PyTorch (latest stable version recommended)
Hugging Face Transformers library (latest stable version recommended)
Hugging Face datasets library
accelerate and bitsandbytes (if using quantization, though not directly used in the final training as distilgpt2 fits easily)
Causal Language Model (CLM): A type of language model that predicts the next token in a sequence based only on the preceding tokens.
Fine-tuning: The process of taking a pre-trained model and training it further on a new, specific dataset to adapt its behavior to a particular task.
Epoch: One complete pass through the entire training dataset during the training process.
Greedy Decoding: A text generation strategy where the model always selects the token with the highest probability as the next token. This leads to deterministic output.
EOS Token: End-Of-Sequence token, a special token used by tokenizers to mark the end of a sequence of text.
Model Card Authors [optional] gaemr1000
Model Card Contact gaemr1000
distilbert/distilgpt2