---
license: mit
datasets:
- roneneldan/TinyStories
language:
- en
metrics:
- perplexity
pipeline_tag: text-generation
tags:
- slm
- transformer
- attention
- optimization
- pytorch
- tinystories
- educational
---
# Model Card for Helium-Nano (45M)

**Helium-Nano** is a 45-million parameter Small Language Model (SLM) trained on the TinyStories dataset. It demonstrates how a highly optimized custom Transformer architecture can achieve coherent English storytelling capabilities with minimal compute resources. The model was trained in under 1 hour on a single Nvidia L4 GPU, achieving a throughput of **409k tokens/second** via PyTorch 2.0 compile and architectural optimizations.

## Model Details

### Model Description

Helium-Nano is a decoder-only Transformer designed to investigate training dynamics and scaling laws in low-resource environments. Despite its small size, it produces grammatically correct and narratively consistent short stories. 

The primary goal of this model was engineering efficiency. By implementing **BFloat16 mixed precision**, **Flash Attention principles**, **Torch.compile (Inductor)**, and **Float32-optimized Rotary Embeddings (RoPE)**, the training pipeline achieved a 16x speedup over standard eager-mode baselines.

- **Developed by:** Debmalya/batmanLovesAI
- **Model type:** Decoder-only Transformer (Custom Architecture)
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** N/A (Trained from scratch)

### Model Sources

- **Repository:** [Link to Github Repo](https://github.com/DebmalyaSen34/HeliumLM)
- **Dataset Paper:** [TinyStories: How Small Can Language Models Be?](https://arxiv.org/abs/2305.07759)
- **Optimization Techniques:** [Small Language Models: Architectures, Techniques, Evaluation, Problems and Future Adaptation](https://arxiv.org/abs/2505.19529)

## Uses

### Direct Use

- **Story Generation:** Generating simple, coherent short stories suitable for early childhood reading levels.
- **Educational:** A lightweight baseline for experimenting with model interpretation, quantization, or fine-tuning on consumer hardware.
- **Performance Benchmarking:** Testing inference speeds of small transformers on various hardware.

### Out-of-Scope Use

- **Factual Queries:** The model is trained on fiction; it has no world knowledge and will hallucinate facts.
- **Reasoning/Math:** The model is not capable of complex logic or arithmetic.
- **Harmful Content:** While the dataset is heavily filtered, users should not attempt to generate toxic or biased content.

## Bias, Risks, and Limitations

- **Dataset Bias:** The model reflects the vocabulary and concepts found in the TinyStories dataset, which focuses on simple, positive narratives using a limited vocabulary (approx 3-year-old level).
- **Repetition:** Like many SLMs, the model may enter repetitive loops if the temperature is too low or repetition penalty is not applied during inference.
- **Hallucinations:** The model prioritizes grammatical structure over semantic logic.

## How to Get Started with the Model

Since this uses a custom architecture, you need to instantiate the model class before loading weights.

```python
import torch
from tokenizers import Tokenizer
# Assuming TinySLM class is defined in your local files

# 1. Load Tokenizer
tokenizer = Tokenizer.from_file("tokenizer.json")

# 2. Initialize Model
config = {
    "vocab_size": 32000,
    "d_model": 512,
    "n_head": 8,
    "n_layers": 10,
    "max_seq_len": 512
}
model = TinySLM(config)

# 3. Load Weights
state_dict = torch.load("helium_nano_45m.pt", map_location="cpu")
model.load_state_dict(state_dict)
model.eval()

# 4. Generate
prompt = "Once upon a time, there was a little"
# ... inference code ...