112 lines
3.6 KiB
Markdown
112 lines
3.6 KiB
Markdown
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---
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language:
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- en
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tags:
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- causal-lm
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- text-generation
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- gpt2
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- small-language-model
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Aurora One Mini — 124M
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Aurora One Mini is a compact, community-built language model designed for fast local chat, experiments, and lightweight AI applications.
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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.
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## What makes it interesting
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- **Tiny and fast:** practical for local inference and rapid prototyping
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- **Native ChatML format:** structured user/assistant conversations
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- **Hugging Face + GGUF exports:** works with Transformers and llama.cpp-compatible tools
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- **Open experiment:** trained and evaluated on a single consumer GPU
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## Model details
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- Architecture: GPT-style causal language model
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- Parameters: approximately 124M
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- Layers: 12
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- Hidden size: 768
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- Attention heads: 12
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- Context length: 1,024 tokens
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- Vocabulary: GPT-2 BPE plus ChatML control tokens
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- Final pretraining: 45,000 steps, approximately 15 tokens per parameter
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- Released checkpoint: deterministic v2, step 2,000 of targeted post-training
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## Quick start
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "North-ML1/Aurora-One-Mini"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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prompt = "What is the capital of France?"
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messages = [{"role": "user", "content": prompt}]
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text = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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inputs = tokenizer(text, return_tensors="pt")
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens=80,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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## GGUF files
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The companion GGUF files are provided for local runtimes:
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- `aurora_one_mini_deterministic_v2_f16.gguf` — highest fidelity
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- `aurora_one_mini_deterministic_v2_q4_k_m.gguf` — compact CPU-friendly quantization
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Use the Q4_K_M file for a fast, low-memory demo. Use the F16 file when preserving maximum quality is more important.
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## Honest limitations
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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.
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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.
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## Intended use
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Good fits include:
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- local chat experiments
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- educational model training projects
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- embedded or low-resource inference
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- prompt-format and agent-runtime experiments
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- fast prototyping with Transformers or llama.cpp
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Avoid using it as the sole source of truth for medical, legal, financial, safety-critical, or factual decision-making.
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## Prompt format
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The model was post-trained using ChatML-style turns:
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```text
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<|im_start|><|user|>Your question<|im_end|>
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<|im_start|><|assistant|>
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```
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The included tokenizer metadata contains the required special tokens.
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## Acknowledgements
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Aurora One Mini was trained as a small-scale independent experiment using PyTorch and a consumer NVIDIA GPU. Contributions, evaluations, and improvements are welcome.
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## License
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Released for research and experimentation. Add the project’s final license here before redistributing commercially.
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