Aurora Proelia
Aurora Proelia is a compact 207M-parameter English language model from North ML. It is designed for lightweight local inference, short conversations, and concise explanations on CPU, Apple Silicon, or CUDA.
This release uses the original Proelia v9 checkpoint, the strongest preserved
checkpoint from the project’s earlier chat experiments. It is packaged for the
standard Hugging Face Transformers Auto* API.
What it is good at
- Short conversational replies
- Identity and introduction questions
- Familiar facts and simple explanations
- Lightweight local experimentation
- Retrieval-augmented applications that provide source text in the prompt
Example responses from the checkpoint:
What is Python? Python is a general-purpose programming language known for readable syntax and a large ecosystem.
Explain photosynthesis in one sentence. Photosynthesis is how plants use light to make chemical energy from water and carbon dioxide.
What it is not
Aurora Proelia is not a frontier model, web browser, search engine, calculator, or autonomous tool-use agent. It does not know current events and cannot verify facts by itself. It may make mistakes on arithmetic, specialized subjects, multi-step reasoning, and broad science questions.
For current or specialized questions, an application should search first, select reliable sources, and pass the checked source text to the model. The application should validate the final answer before displaying it.
Run with Transformers
pip install torch transformers safetensors
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "North-ML1/Aurora-Proelia"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
repo,
trust_remote_code=True,
dtype=torch.float32,
).eval()
messages = [{"role": "user", "content": "What is Python?"}]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_tensors="pt",
return_dict=True,
)
with torch.inference_mode():
output = model.generate(
**inputs,
max_new_tokens=64,
do_sample=False,
use_cache=False,
pad_token_id=tokenizer.eos_token_id,
)
prompt_tokens = inputs["input_ids"].shape[-1]
print(tokenizer.decode(output[0, prompt_tokens:], skip_special_tokens=True))
The repository includes the custom Aurora architecture files required by
trust_remote_code=True. The tokenizer uses the checkpoint’s original
Question: ... Answer: training format behind the normal chat API.
Evaluation snapshot
These are small engineering checks, not official leaderboard results:
| Check | Result |
|---|---|
| Original v9 curated chat gate | 14/14 |
| Direct 20-prompt capability probe | 15/20 |
The direct probe covered identity, short explanations, familiar facts, Python,
photosynthesis, transformers, capitals, Earth, reinforcement learning, and
basic arithmetic. The model was strongest on concise language and familiar
knowledge, and remained unreliable on exact arithmetic and open-ended science.
See BENCHMARKS.md for the test notes.
Model details
| Property | Value |
|---|---|
| Parameters | 206,942,208 |
| Architecture | Aurora causal language model |
| Vocabulary | 16,000 tokens |
| Context length | 2,048 tokens |
| Recommended decoding | Greedy decoding for reproducible output |
| Intended hardware | CPU, Apple Silicon, or CUDA |
Intended use
Use Aurora Proelia for research, local assistants, model experiments, and as a small component inside a retrieval or tool-use system. Keep search, source selection, arithmetic checks, safety filtering, and answer validation in the surrounding application.
License
This is a public North ML research release. No open-source license is granted by this repository; licensing and redistribution rights are reserved by North ML.