--- license: apache-2.0 base_model: - Qwen/Qwen2.5-32B-Instruct - deepseek-ai/DeepSeek-R1-Distill-Qwen-32B library_name: peft tags: - base_model:adapter:Qwen/Qwen2.5-32B-Instruct - marine-science - oceanography - aquaculture - coastal-restoration - environmental-compliance - dfo-statutory - qwen - deepseek-r1-distill - lora - peft - vllm - awq - cuda - aboveboard-benchmark pipeline_tag: text-generation language: - en datasets: - Coralfil/atlantis-ocean-master - Coralfil/AboveBoard-Marine-52 model-index: - name: Atlantis-Pyramid-v2 results: - task: type: text-generation name: Instruction Following dataset: name: IFEval type: ifeval metrics: - name: Prompt-Level Strict Accuracy (verified 2026-10-21, lm-eval 0.4.13, capped n=100) type: acc value: 55.0 - name: Prompt-Level Loose Accuracy (verified 2026-10-21, capped n=100) type: acc value: 64.0 - name: Instruction-Level Strict Accuracy (verified 2026-10-21, capped n=100) type: acc value: 69.94 - name: Instruction-Level Loose Accuracy (verified 2026-10-21, capped n=100) type: acc value: 74.85 - task: type: text-generation name: Complex Reasoning dataset: name: BBH (Big-Bench Hard) type: bbh metrics: - name: 3-Shot CoT Accuracy type: acc value: 85.2 - task: type: text-generation name: Graduate Science dataset: name: GPQA Diamond type: gpqa metrics: - name: 0-Shot CoT Accuracy type: acc value: 51.6 - task: type: text-generation name: Competition Mathematics dataset: name: MATH-Hard type: math metrics: - name: 4-Shot CoT Accuracy type: acc value: 68.9 - task: type: text-generation name: Multi-Discipline Reasoning dataset: name: MMLU-Pro type: mmlu_pro metrics: - name: 5-Shot Accuracy type: acc value: 64.7 - task: type: text-generation name: Sovereign Marine and Statutory Precision dataset: name: AboveBoard 52 Marine Benchmark type: Coralfil/AboveBoard-Marine-52 metrics: - name: Overall Accuracy type: acc value: 97.1 - name: Statutory Marine Compliance type: acc value: 100.0 --- # Atlantis-Pyramid v2 (32B Specialized Foundation Model)

Atlantis Pyramid v2 Launch Banner

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 via the AWQ W4A16 path (fits single RTX 3090/4090 or A10). The bf16 adapter-merge path requires approximately 65 GB (A100/H100 class) | | 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) | 55.0%† | 52.3%† | 77.2% | 82.6% | 85.4% | —† | | 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% | 90.9%* | 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) | 10.6 GB (1x 3060) | 9.8 GB (1x 3060) | 18.24 GB (1x A10) | 62.0 GB (2x A100) | Single GPU Fit | Evaluation transparency: AboveBoard 52 flagship figures (97.1% overall, 100% statutory) correspond to the archived internal evaluation report dated 2026-10-11 (52 of 52 scenarios). The IFEval row is now **verified** for both Atlantis models (see footnote †). All other figures in this table reflect internal Coralfil evaluations from October 2026 whose detailed artifacts are pending publication; treat them as indicative until the artifacts ship. The Atlantis-Obelisk edge figure of 10.6 GB VRAM is measured (Unsloth 4-bit, RTX 3060, 2026-10-20). \* Obelisk 90.9% is a **verified** re-run (2026-10-20): 20/22 on the AboveBoard-52 multiple-choice subset with per-scenario artifacts published in the Coralfil release repository. Disclosure: the MC key is 82% option A (constant-A baseline 81.8%), so this subset no longer discriminates well; a key rebalance is planned. The earlier 91.2% was internal and is superseded. \*\* Obelisk 93.5% statutory is internal and unverified; no artifact exists yet. † IFEval row: both Atlantis figures are **verified** lm-evaluation-harness 0.4.13 runs (2026-10-21, A10 GPU, 0-shot, prompt-level strict). Obelisk: full 541-prompt set, 52.31 ± 2.15, served via the production chat API. Pyramid v2: capped n=100, 55.0 ± 5.0, direct-loaded in the production-identical NF4 configuration; a full-set run (~7.8 h GPU) is deferred to a future maintenance window. The earlier 83.4 / 80.5 figures were internal and are superseded. The two verified Atlantis scores are within noise of each other at n=100, and a net-gain comparison against the internal, different-protocol base figure is not meaningful — that cell is intentionally blank. Glyph, base, and Command R+ cells in this row remain internal as described above. Command R+ column: provenance could not be verified against Cohere's published submissions. Cohere's official Open LLM Leaderboard v1 results for Command R+ are: ARC-C 70.99, HellaSwag 88.6, MMLU 75.7, TruthfulQA 56.3, Winogrande 85.4, GSM8K 70.7 (Cohere model card, c4ai-command-r-plus). The v2-task figures shown here should be treated as placeholders until re-verified. Full sourcing: COMPETITOR_COMPARISON.md in the Coralfil release repository. ## 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: ```python 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: ```bash 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 ``` ## Limitations and Intended Use - Atlantis-Pyramid v2 is a domain-specialized model for marine science and statutory decision support. Outputs must be validated by qualified personnel before field deployment, regulatory submission, or animal-welfare-critical actions. Nothing in model output constitutes legal advice. - Statutory answers can exhibit jurisdiction drift (citing authorities from the wrong jurisdiction). Always verify regulatory citations against primary legal texts. - Dosages, thresholds, deadlines, and other numeric specifics may carry unjustified precision; treat them as placeholders until verified. - This repository contains the LoRA adapter weights only. Merged and AWQ-quantized weight releases are staged separately. - General-knowledge breadth is narrower than frontier general-purpose models of similar size. ## Data Sources and Attribution Training and evaluation corpora draw on the following third-party sources, used under their respective licences: - Ocean Networks Canada: sensor and observatory data under CC-BY 4.0. Citation per ESIP guidelines via the Oceans 3.0 citation service. Owens D, et al. (2022) Front. Mar. Sci. 9:806452. doi:10.3389/fmars.2022.806452 - Hakai Institute: coastal observational datasets under CC-BY 4.0 via the Hakai Data Catalogue (catalogue.hakai.org). Login-gated sensor streams are excluded. - Fisheries and Oceans Canada: Fisheries Act (R.S.C. 1985, c. F-14), Pacific Aquaculture Regulations (SOR/2010-270), Aquaculture Activities Regulations, and open datasets under the Open Government Licence, Canada (open.canada.ca). - PubChem (NIH/NLM): chemical structure verification (for example aragonite CID 10112, chitosan CID 71853). Kim S, et al. (2023) Nucleic Acids Research 51(D1):D1373-D1380. doi:10.1093/nar/gkac956 - Corpus design follows data-provenance practice from: Longpre S, et al. (2024) The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing and Attribution in AI. Nature Machine Intelligence 6:975-987. doi:10.1038/s42256-024-00878-8 - Base models: Qwen2.5-32B-Instruct (Apache 2.0) and DeepSeek-R1-Distill-Qwen-32B (MIT). Methods: RS-LoRA (arXiv:2312.03732), NEFTune (arXiv:2310.05914), AWQ (AutoAWQ, Apache 2.0), vLLM (SOSP 2023, doi:10.1145/3600006.3613165). Full attribution file: DATA_ATTRIBUTION_AND_CITATIONS.md in the Coralfil release repository. ## Citation and Contact ```bibtex @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