---
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- novi
- novi-nano
- causal-lm
- gpt2
- from-scratch
---

# Novi-Nano-Base

![Novi-Nano Banner](banner.jpg)

**Novi-Nano-Base** is a tiny causal language model trained from scratch by **Novi-AI**.

With just **1,258,560 parameters**, Novi-Nano explores language modeling at an extremely small scale while remaining compatible with the Hugging Face Transformers ecosystem.

⚡ **1.26M parameters · 300M training tokens · 256-token context**

## Model Details

### Architecture

| Property        |                 Value |
| --------------- | --------------------: |
| Model type      | Causal Language Model |
| Parameters      |         **1,258,560** |
| Vocabulary size |             **8,192** |
| Context length  |               **256** |
| Embedding size  |                **96** |
| Layers          |                 **4** |
| Attention heads |                 **4** |
| FFN size        |               **384** |
| Tensor type     |               **F32** |

## Training

Novi-Nano-Base was trained from scratch using approximately **300 million training tokens**.

### Training Statistics

| Metric                      |          Result |
| --------------------------- | --------------: |
| Training tokens             | **300,023,808** |
| Best validation loss        |    **5.418699** |
| Final validation loss       |    **5.418699** |
| Final validation perplexity |    **225.5853** |

## Tokenizer

Novi-Nano uses a custom tokenizer with a vocabulary size of **8,192 tokens**.

The tokenizer was trained using data from:

* FineWeb-Edu
* FineWeb-HQ
* SmolLM-Cosmopedia

## Intended Use

Novi-Nano-Base is primarily intended for:

* 🔬 Research and experimentation
* 🧪 Small-model language-model experiments
* 🎓 Educational purposes
* 🛠️ Fine-tuning experiments
* 💻 Lightweight local inference

As a **base model**, it is not specifically instruction-tuned for following user commands or acting as a conversational assistant.

## Limitations

Novi-Nano-Base is an extremely small experimental language model.

Because of its size and short context window, it will have significant limitations compared with modern billion-parameter language models.

It may:

* Generate incoherent text
* Repeat phrases
* Produce factual errors
* Struggle with complex instructions
* Have limited world knowledge
* Perform poorly on reasoning tasks
* Lose context beyond its 256-token window

This model should be considered a **research and experimentation model**, rather than a production-ready general-purpose LLM.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Novi-AI/Novi-Nano-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "Hello, my name is"

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=50,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Project History

Novi AI follows the earlier **AppleMind** experiments, with Novi becoming the primary project for developing small language models.

**AppleMind → Novi AI → Novi-Nano** 🚀

## Acknowledgements

Novi-Nano was built using the open-source machine-learning ecosystem and datasets made available by the community.

Special thanks to:

* Hugging Face 🤗
* FineWeb
* SmolLM
* Cosmopedia

## License

This model is released under the **Apache 2.0** license.

---

## 🧠 Novi AI

**Small models. Big experiments.**

Novi-Nano is intentionally tiny — exploring how far a language model can go with just a fraction of the parameters used by modern LLMs.