gaemr1000/stupid-ai-distilgpt2

Model Card for Stupid AI (distilgpt2-finetuned)

Always says "Be quiet! I am dumb."

Model Details

Model Description

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.

• Developed by: gaemr1000
• Model type: Text Generation (Causal Language Model)
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from model: 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.

Model Sources [optional]

• Repository: This model card serves as the primary repository for this specific fine-tune. The training script and inference script can be found in the developer's associated GitHub repository, if made public.

Uses

Direct Use

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:

• Humorous demonstrations of highly constrained AI behavior.
• Teaching basic concepts of fine-tuning language models for specific, non-general tasks.
• Entertainment.

Downstream Use [optional]

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.

Out-of-Scope Use

• General-purpose conversation: The model is not capable of general conversation or understanding complex queries.
• Information retrieval: It cannot provide factual information or answer questions.
• Creative text generation: It will not generate varied or creative text.
• Any task requiring intelligence or nuanced response: The model's design is explicitly to avoid such capabilities.
• Deployment in critical systems: Absolutely not suitable for any application where accurate, safe, or contextually relevant responses are required.

Bias, Risks, and Limitations

This model has been intentionally trained to exhibit a single, repetitive, and "unintelligent" behavior.

• Limited Utility: Its primary limitation is its extreme lack of utility beyond its humorous purpose.
• Repetitive Output: The model will always output the same phrase, regardless of input, making it predictable and non-dynamic.
• Lack of Understanding: It does not "understand" any input; it merely reacts by producing its trained output sequence.
• No Safety Measures: It lacks any safety or alignment mechanisms beyond its singular fixed output. Since the output itself is benign, this risk is minimal, but it serves as a reminder that this model is a demonstration, not a production-ready system.

Recommendations

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.

How to Get Started with the Model

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}")

Example usage:

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}")

Training Details

Training Data

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.

Training Procedure

The model was fine-tuned using the standard Hugging Face Trainer API for causal language modeling.

Preprocessing [optional]

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.

Training Hyperparameters

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)

Speeds, Sizes, Times [optional]

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.

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was informally tested on various ad-hoc input phrases during development. No formal test dataset was created beyond the training data examples.

Factors

The primary factor for evaluation was the consistency of the model's output in response to diverse text inputs.

Metrics

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.

Results

Summary

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.

Model Examination [optional]

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.

Environmental Impact

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.

Technical Specifications [optional]

Model Architecture and Objective

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.

Compute Infrastructure

Hardware

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.

Software

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)

Glossary [optional]

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