README.md

---
datasets:
- T404C/ETHiQ
- T404C/QGCNQ
- Lominub44/texterer
- Lominub44/CCWHiQ
- jondurbin/airoboros-gpt4-1.4.1
- jondurbin/airoboros-3.2
- HuggingFaceH4/no_robots
- HuggingFaceH4/cai-conversation-harmless
- tatsu-lab/alpaca
language:
- en
pipeline_tag: text-generation
library_name: transformers
license: cc-by-nc-4.0
new_version: Lominub44/PicoNosenso-v2.1
---

<div style="
background:linear-gradient(135deg,#1a0933,#3d2b8c,#1e0b4d);padding:2.8rem 1.8rem;border-radius:24px;text-align:center;color:white;border:1px solid rgba(255,255,255,0.12);box-shadow:0 12px 48px rgba(101,88,255,0.25),inset 0 0 24px rgba(255,255,255,0.08);margin-bottom:2.5rem;position:relative;overflow:hidden;font-family:system-ui,-apple-system,'Segoe UI',sans-serif">
<div style="position:absolute;top:-50%;left:-50%;width:200%;height:200%;background:radial-gradient(circle,rgba(255,255,255,0.15) 0%,transparent 70%);transform:rotate(0);z-index:1"></div>
<h1 style="font-size:3.2rem;margin:0;font-weight:900;letter-spacing:-0.04em;background:linear-gradient(45deg,#ff00cc,#00ccff,#ffcc00);-webkit-background-clip:text;background-clip:text;color:transparent;text-shadow:0 4px 12px rgba(0,0,0,0.3);position:relative;z-index:2;background-size:300% 300%">
PicoNosenso-v1</h1>
<p style="font-size:1.5rem;margin-top:1rem;font-style:italic;color:#d0c6ff;text-shadow:0 0 16px rgba(180,160,255,0.6);letter-spacing:0.03em;position:relative;z-index:2;font-weight:500;padding:0.4rem 1.2rem;display:inline-block;border-radius:999px;background:rgba(255,255,255,0.08);backdrop-filter:blur(4px)">
Where "Accuracy" Takes a Cosmic Vacation</p></div>
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
```python
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:**
```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}}
}
```

## Model Card Authors
Lominub44

## Model Card Contact
[Create a discussion](https://huggingface.co/Lominub44/PicoNosenso-v1/discussions/new)