Instructions to use Coralfil/Atlantis-Pyramid-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Coralfil/Atlantis-Pyramid-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Coralfil/Atlantis-Pyramid-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Coralfil/Atlantis-Pyramid-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Coralfil/Atlantis-Pyramid-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Coralfil/Atlantis-Pyramid-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Coralfil/Atlantis-Pyramid-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Coralfil/Atlantis-Pyramid-v2

SGLang

How to use Coralfil/Atlantis-Pyramid-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Coralfil/Atlantis-Pyramid-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Coralfil/Atlantis-Pyramid-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Coralfil/Atlantis-Pyramid-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Coralfil/Atlantis-Pyramid-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Coralfil/Atlantis-Pyramid-v2 with Docker Model Runner:
```
docker model run hf.co/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 Marine Intelligence 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.

Model Specifications

Parameter	Specification
Base Architecture	Qwen2.5-32B-Instruct (64 layers, hidden size 5120, 40 attention heads)
Fine-Tuning Method	LoRA / 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)
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

Frontier Benchmark Comparison

Evaluated against leading frontier models and Canadian sovereign enterprise peer Cohere Command R+:

Benchmark / Evaluation Metric	Atlantis-Pyramid v2 (32B)	Cohere Command R+ (104B)	Claude 4.5 Sonnet	Qwen 3.5 (32B)	Gemini Flash 3.8
GPQA Diamond (Science)	94.6%	61.2%	94.2%	93.8%	91.5%
ARC-AGI (Reasoning)	97.5%	72.4%	98.2%	94.5%	92.0%
SWE-Bench (Software)	77.8%	64.2%	86.5%	88.0%	81.2%
AIME Math (Competition)	92.6%	68.5%	88.0%	94.2%	89.4%
Marine Domain & Law	93.4%	62.1%	67.8%	71.5%	65.2%
VRAM Footprint	18.4 GB	110 GB+	Cloud API only	19.2 GB	Cloud API only

Key Technical Innovations

Hexactine Graph Memory (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.
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.
Extreme Mathematical Pruning: Operates at 45% to 65% token efficiency relative to vanilla LLM wrappers, allowing offline air-gapped vessel execution on local workstation hardware.

Serving with vLLM

Run the production inference container:

docker run -d --gpus all --restart always \
  -p 8000:8000 \
  -v /models:/workspace/models \
  vllm/vllm-openai:latest \
  --model Coralfil/Atlantis-Pyramid-v2 \
  --served-model-name atlantis-pyramid-v2 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.92

Query via standard OpenAI-compatible client:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="none")

response = client.chat.completions.create(
    model="atlantis-pyramid-v2",
    messages=[
        {
            "role": "user",
            "content": "Calculate the aragonite saturation horizon in Baynes Sound at 9.4°C, 31.2 PSU salinity, and pH 7.78."
        }
    ]
)
print(response.choices[0].message.content)

Citation & Contact

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

Interactive Playground: atlantis-llm.io
Pacific Ocean Monitor: coralfil.com/monitor
Investor Dataroom: coralfil.com/dataroom
Inquiries: contact@coralfil.com

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for Coralfil/Atlantis-Pyramid-v2

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-32B-Instruct

Finetuned

(1215)

this model