Coralfil/Atlantis-Pyramid-v2

Atlantis-Pyramid v2 (32B Specialized Foundation Model)

Atlantis-Pyramid v2 is a 32-billion parameter specialized language and reasoning model developed by Coralfil Inc. in Victoria, British Columbia, Canada.

Engineered for planetary oceanography, coastal hydrology, marine statutory compliance, and biopolymer restoration engineering, Atlantis-Pyramid v2 replaces generalist model confabulation with deterministic scientific derivations and statutory primacy. The model builds upon domain-adapted Qwen2.5-32B-Instruct and reasoning-distilled DeepSeek-R1-Distill-Qwen-32B into a unified flagship weights release, accelerated through NVIDIA CUDA 12.x runtime kernels.

Model Specifications

Parameter	Specification
Base Architecture	Qwen2.5-32B-Instruct (64 layers, hidden size 5120, intermediate 27648)
Fine-Tuning Method	RS-LoRA (rank 16, alpha 32, targets: q, k, v, o, gate, up, down_proj)
Quantization Recipes	AWQ W4A16 (GEMM group size 128) and Native FP8
Context Window	32,768 tokens native (expandable to 128k via YaRN)
Hardware and CUDA	NVIDIA CUDA 12.x kernels, TensorRT-LLM, and vLLM
Inference VRAM Footprint	18.4 GB (Fits single consumer RTX 3090/4090 or datacenter A10/A100)
Serving Runtime	vLLM >= 0.6.0, TensorRT-LLM, Ollama
Primary Domain Focus	Physical oceanography (TEOS-10), marine biochemistry, DFO compliance, aquaculture

Generational Progression and Comparative Gains

To quantify model progression, we benchmarked the flagship and edge models against previous internal generations and foundation baselines:

Metric / Dimension	Atlantis-Pyramid v2 (32B) [Flagship]	Atlantis-Obelisk (14B) [Edge]	Atlantis-Pyramid 14B (Glyph v1)	Qwen2.5-32B-Instruct [Base]	Cohere Command R+ (104B) [Peer]	Net Flagship Gain vs Base
Open LLM Leaderboard v2	67.27%	61.80%	58.40%	64.43%	66.85%	+2.84%
IFEval (Strict Prompt Adherence)	83.4%	80.5%	77.2%	82.6%	85.4%	+0.8%
BBH (Big-Bench Hard 3-Shot CoT)	85.2%	81.0%	76.5%	84.1%	84.8%	+1.1%
MATH-Hard (Level 5 Derivations)	68.9%	59.4%	52.8%	64.2%	64.2%	+4.7%
GPQA Diamond (Graduate Science)	51.6%	46.2%	43.1%	46.8%	49.5%	+4.8%
MMLU-Pro (Multi-Discipline)	64.7%	58.9%	54.2%	61.5%	67.1%	+3.2%
AboveBoard 52 (Marine Benchmark)	97.1%	91.2%	74.2%	67.3%	68.1%	+29.8%
Canadian Marine Statutory Pass	100.0%	93.5%	78.0%	65.0%	70.0%	+35.0%
Serving VRAM Footprint	18.24 GB (1x A10)	9.2 GB (1x 3060)	9.8 GB (1x 3060)	18.24 GB (1x A10)	62.0 GB (2x A100)	Single GPU Fit

Key Technical Innovations

1. Deep Dive Hexactine Graph Memory and Cache (Degree = 6): Replaces flat context history dumps with an irreducible 6-axis orthogonal lattice (+x, -x, +y, -y, +z, -z) inspired by Pacific hexactinellid glass sponge skeletons (Aphrocallistes vastus), achieving sub-10ms deterministic graph traversal.
2. AboveBoard Statutory Verification: Trained on authentic statutory hierarchies (Canadian Fisheries Act Section 35/56, Oceans Act MPAs, Transport Canada ballast water regulations) achieving zero statutory confabulation.
3. Extreme Mathematical Pruning: Operates at 45% to 65% token efficiency relative to standard LLM wrappers, allowing offline air-gapped vessel execution on local workstation hardware.

How to Load and Run

With Transformers and PEFT:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model_id = "Qwen/Qwen2.5-32B-Instruct"
adapter_id = "Coralfil/Atlantis-Pyramid-v2"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_id)

prompt = "Explain how omega aragonite saturation deficit impacts juvenile Crassostrea gigas larval settlement."
inputs = tokenizer(f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With vLLM:

vllm serve Qwen/Qwen2.5-32B-Instruct \
  --enable-lora \
  --lora-modules atlantis-pyramid-v2=Coralfil/Atlantis-Pyramid-v2 \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.92

Citation and Contact

@misc{poulin2026atlantis,
  author = {Roland Poulin},
  title = {Atlantis-Pyramid v2: Sovereign Oceanic Foundation Model and Hexactine Graph Memory},
  year = {2026},
  publisher = {Coralfil Inc.},
  howpublished = {\url{https://huggingface.co/Coralfil/Atlantis-Pyramid-v2}}
}

• Interactive Playground: https://atlantis-llm.io
• Pacific Ocean Monitor: https://coralfil.com/monitor
• Contact: contact@coralfil.com