Instructions to use Coralfil/Atlantis-Pyramid-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Coralfil/Atlantis-Pyramid-32B with Transformers:

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

pipe = pipeline("text-generation", model="Coralfil/Atlantis-Pyramid-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Coralfil/Atlantis-Pyramid-32B")
model = AutoModelForCausalLM.from_pretrained("Coralfil/Atlantis-Pyramid-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Coralfil/Atlantis-Pyramid-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Coralfil/Atlantis-Pyramid-32B"
# 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-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Coralfil/Atlantis-Pyramid-32B

SGLang

How to use Coralfil/Atlantis-Pyramid-32B 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-32B" \
    --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-32B",
		"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-32B" \
        --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-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Coralfil/Atlantis-Pyramid-32B with Docker Model Runner:
```
docker model run hf.co/Coralfil/Atlantis-Pyramid-32B
```

Atlantis-Pyramid-32B

Atlantis-Pyramid-32B is the flagship model of the Coralfil OS Atlantis project. It is a fine-tuned version of Qwen/Qwen2.5-32B-Instruct (32B parameters), specialized for marine restoration science, aquaculture chemistry, dynamic oceanography modeling, and coral reef ecosystem analysis.

Model Details

Property	Value
Base Model	`Qwen/Qwen2.5-32B-Instruct`
Parameters	32B
Fine-Tuning Method	LoRA SFT (r=16, alpha=32)
Target Modules	q_proj, v_proj, k_proj, o_proj, gate_proj, up_proj, down_proj
Precision	bfloat16 / 4-bit NF4
Training Data	Coralfil validated marine science corpus
Build Date	2026-07-12

Training

The model was fine-tuned using Supervised Fine-Tuning (SFT) with the trl library's SFTTrainer. LoRA adapters were trained in 4-bit precision for memory efficiency and then merged into the base model for inference.

Hyperparameters:

Learning rate: 2e-4 (cosine schedule with 10% warmup)
Batch size: 2 per device, gradient accumulation 4
Weight decay: 0.01
Max sequence length: 2048

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Coralfil/Atlantis-Pyramid-32B",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Coralfil/Atlantis-Pyramid-32B")

prompt = "Describe the optimal conditions for staghorn coral restoration."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With vLLM

vllm serve Coralfil/Atlantis-Pyramid-32B --dtype bfloat16 --max-model-len 4096

Intended Use

This model is designed for research and operational use within the Coralfil OS ecosystem. It excels at:

Marine biology question answering
Coral reef ecosystem analysis
Aquaculture parameter optimization
Oceanographic data interpretation
Environmental compliance assessment

Limitations

This model is specialized for marine science domains and may underperform on unrelated tasks compared to the general-purpose base model.
Outputs should be validated by domain experts for critical decisions.
Not intended for medical or safety-critical applications.

License

Apache 2.0 -- see LICENSE for details.

Citation

@misc{atlantis-pyramid-2026,
  title={Atlantis-Pyramid-32B: A Fine-Tuned 32B Model for Marine Science},
  author={Coralfil Team},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/Coralfil/Atlantis-Pyramid-32B}
}

Downloads last month: 607

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for Coralfil/Atlantis-Pyramid-32B

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-32B-Instruct

Adapter

(184)

this model

Adapters

1 model