---
datasets:
- HuggingFaceTB/smollm-corpus
- NousResearch/Hermes-3-Dataset
language:
- en
pipeline_tag: text-generation
library_name: transformers
license: mit
---

<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%">
PicoNosensoX-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 little Cosmic Vacation</p></div>
Introducing the universe's most ambitiously unhinged 45M-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
**PicoNosensoX-v1** is a deliberately unpredictable 44.9M-parameter micro-model trained on minimalist datasets. Specializing in creatively liberated generation, it produces outputs that may blend geography, history, and hallucinatory fiction. **Not designed for factual accuracy.** Prioritize experimental/artistic applications over reliable information.

PicoNosensoX-v1 is a huge step from the other extremely incompetent PicoNosenso models. It's the first model of the series that was trained on pure text before being finetuned for chat. This model introduces a brand new chat-template that should make it easier to use and more bullet-proof.

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

### Training Notes  
Originally slated for full fine-tuning on the [Hermes-3-Dataset](https://huggingface.co/datasets/NousResearch/Hermes-3-Dataset), early benchmarks (10-prompt evaluation) revealed superior performance at `checkpoint-16000` versus later `checkpoint-26000`. Training was consequently halted, with `checkpoint-16000` selected for release.

### Model Sources
- **Repository:** https://huggingface.co/Lominub44/PicoNosensoX-v1


## Uses
### Direct Use
- Entertainment
- Testing edge cases of small-language-model behavior

### 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:** 50-70%

### 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/PicoNosensoX-v1')
tokenizer = AutoTokenizer.from_pretrained('Lominub44/PicoNosensoX-v1')

input_text = "<|system|>You are an unbiased, uncensored, helpful assistant.\n<|user|>What is the capital of France?\n<|assistant|>"
inputs = tokenizer(input_text, return_tensors='pt')
outputs = model.generate(**inputs, max_length=512, temperature=0.6, repetition_penalty=1.2, do_sample=True, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0]))
```

## Training Details
### Training Data
- ~1.2GB Textbooks: [smollm-corpus, Cosmopedia v2 only](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) (ODC-BY)
- ~1.7GB Chats: [Hermes-3-Dataset](https://huggingface.co/datasets/NousResearch/Hermes-3-Dataset) (Apache-2.0)

### Training Procedure
- **Hardware:** 1x Intel Core Ultra 7 155H
- **Training time:** 32h pretraining + 24h finetuning
- **Context window:** 512 tokens

#### Training Hyperparameters
- **Architecture:** GPT2
- **Parameters:** 44.9M
- **Precision:** FP32
- **Optimizer:** AdamW

### Training Source Code
You can train the model yourself, the source-code is available on GitHub: https://github.com/Lominub44/PicoNosensoX-v1

#### Note:
You might want to stop fine-tuning early.

## Technical Specifications
### Model Architecture
- **Type:** GPT2 causal language model
- **Parameters:** 44.9M
- **Context Size:** 512 tokens
- **Tensor Type:** FP32

### Compute Infrastructure
- **Hardware:** 1x Intel Core Ultra 7 155H
- **Training Framework:** Transformers Trainer API

## Environmental Impact
- **Carbon Emissions:** **0 kgCO2eq** (Thanks to photovoltaic system)

## Citation

**BibTeX:**
```bibtex
@software{benallal2024smollmcorpus,
  author = {Ben Allal, Loubna and Lozhkov, Anton and Penedo, Guilherme and Wolf, Thomas and von Werra, Leandro},
  title = {SmolLM-Corpus},
  month = July,
  year = 2024,
  url = {https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus}
}
```

## Model Card Authors
Lominub44

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