README.md

---
language:
- en
tags:
- causal-lm
- text-generation
- gpt2
- small-language-model
pipeline_tag: text-generation
library_name: transformers
---

# Aurora One Mini — 124M

Aurora One Mini is a compact, community-built language model designed for fast local chat, experiments, and lightweight AI applications.

At only **124 million parameters**, it is small enough to run comfortably on ordinary laptops and edge devices while remaining useful for short-form generation and experimentation.

## What makes it interesting

- **Tiny and fast:** practical for local inference and rapid prototyping
- **Native ChatML format:** structured user/assistant conversations
- **Hugging Face + GGUF exports:** works with Transformers and llama.cpp-compatible tools
- **Open experiment:** trained and evaluated on a single consumer GPU

## Model details

- Architecture: GPT-style causal language model
- Parameters: approximately 124M
- Layers: 12
- Hidden size: 768
- Attention heads: 12
- Context length: 1,024 tokens
- Vocabulary: GPT-2 BPE plus ChatML control tokens
- Final pretraining: 45,000 steps, approximately 15 tokens per parameter
- Released checkpoint: deterministic v2, step 2,000 of targeted post-training

## Quick start

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "YOUR_USERNAME/aurora-one-mini-124m"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "What is the capital of France?"
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=80,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
    )

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

## GGUF files

The companion GGUF files are provided for local runtimes:

- `aurora_one_mini_deterministic_v2_f16.gguf` — highest fidelity
- `aurora_one_mini_deterministic_v2_q4_k_m.gguf` — compact CPU-friendly quantization

Use the Q4_K_M file for a fast, low-memory demo. Use the F16 file when preserving maximum quality is more important.

## Honest limitations

This is an experimental 124M model, not a frontier assistant. It can produce fluent short responses, but it may hallucinate, repeat itself, or answer arithmetic and factual questions incorrectly. For dependable applications, pair it with a calculator, retrieval system, memory layer, and explicit output validation.

The native-ChatML factual smoke test scored **3/20** on a small internal suite. This score is reported to set realistic expectations and should not be interpreted as a general benchmark.

## Intended use

Good fits include:

- local chat experiments
- educational model training projects
- embedded or low-resource inference
- prompt-format and agent-runtime experiments
- fast prototyping with Transformers or llama.cpp

Avoid using it as the sole source of truth for medical, legal, financial, safety-critical, or factual decision-making.

## Prompt format

The model was post-trained using ChatML-style turns:

```text
<|im_start|><|user|>Your question<|im_end|>
<|im_start|><|assistant|>
```

The included tokenizer metadata contains the required special tokens.

## Acknowledgements

Aurora One Mini was trained as a small-scale independent experiment using PyTorch and a consumer NVIDIA GPU. Contributions, evaluations, and improvements are welcome.

## License

Released for research and experimentation. Add the project’s final license here before redistributing commercially.