PicoNosenso-v1

Where "Accuracy" Takes a Cosmic Vacation

Introducing the universe's most ambitiously unhinged 7.5M-parameter micro-model! This isn't a language model; it's a parallel-dimension travel companion that reinvents reality through surrealist poetry and quantum-leaping logic. Deploy only if coherence is overrated and chaos is your curriculum.

Model Details

Model Description

A deliberately unpredictable 7.59M-parameter micro-model trained on minimalist data. Specializes in generating creatively liberated outputs that blend geography, history, and hallucinatory fiction. Not designed for factual accuracy - consider it a Dadaist art piece in model form.

Developed by: Lominub44
Model type: GPT2-based causal language model
Language(s) (NLP): English
License: cc-by-nc-4.0
Finetuned from model: GPT2 architecture (scratch training)

Model Sources

Repository: https://huggingface.co/Lominub44/PicoNosenso-v1

Uses

Direct Use

Entertainment and absurdist content generation
Surrealist writing assistant
Testing edge cases of small-language-model behavior
Parallel-universe trivia generator

Downstream Use

Creative writing prompt generation
AI-assisted art projects
Educational demonstrations of model limitations

Out-of-Scope Use

Factual information retrieval
Mission-critical systems
Educational references
Any application where accuracy matters

Bias, Risks and Limitations

Hallucination Rate: 327% (It's a feature)
Factual Grounding: Nonexistent
Geopolitical Awareness: Creates new nations
Historical Accuracy: Rewrites timelines
Sample Output: "The capital of France is a capital city located in Paris."

Recommendations

DO use for entertainment purposes only
DO NOT trust outputs without independent universe-hopping verification
WARNING: May cause spontaneous reality reinterpretation

How to Get Started

from transformers import GPT2LMHeadModel, AutoTokenizer

model = GPT2LMHeadModel.from_pretrained('Lominub44/PicoNosenso-v1')
tokenizer = AutoTokenizer.from_pretrained('Lominub44/PicoNosenso-v1')

input_text = "<|startoftext|>Question: What is the capital of France?\nAnswer:"
inputs = tokenizer(input_text, return_tensors='pt')
outputs = model.generate(**inputs, 
  max_length=256,
  temperature=0.4,  # Recommended
  repetition_penalty=1.2,
do_sample=True)
print(tokenizer.decode(outputs[0]))

Training Details

Training Data

~200MB QA-style chat data

Training Procedure

Hardware: Ryzen 7 5700X
Training time: 52h 30m
Context window: 256 tokens

Training Hyperparameters

Architecture: GPT2
Parameters: 7.59M
Precision: FP32
Optimizer: AdamW

Technical Specifications

Model Architecture

Type: GPT2 causal language model
Parameters: 7.59M
Context Size: 256 tokens
Tensor Type: FP32

Compute Infrastructure

Hardware: AMD Ryzen 7 5700X
Training Framework: Transformers Trainer API

Environmental Impact

Carbon Emissions: 0 kgCO2eq (Thanks to photovoltaic system)

Citation

BibTeX:

@misc{PicoNosenso,
  author = {Lominub44},
  title = {{PicoNosenso-v1: Where Accuracy Takes a Cosmic Vacation}},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Lominub44/PicoNosenso-v1}}
}

@misc{alpaca,
  author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
  title = {Stanford Alpaca: An Instruction-following LLaMA model},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}

@misc{no_robots,
  author = {Nazneen Rajani and Lewis Tunstall and Edward Beeching and Nathan Lambert and Alexander M. Rush and Thomas Wolf},
  title = {No Robots},
  year = {2023},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{https://huggingface.co/datasets/HuggingFaceH4/no_robots}}
}

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PicoNosenso-v1