Instructions to use Coralfil/Atlantis-Pyramid-14B-Glyph-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use Coralfil/Atlantis-Pyramid-14B-Glyph-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Coralfil/Atlantis-Pyramid-14B-Glyph-AWQ" # 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-14B-Glyph-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Coralfil/Atlantis-Pyramid-14B-Glyph-AWQ
- SGLang
How to use Coralfil/Atlantis-Pyramid-14B-Glyph-AWQ 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-14B-Glyph-AWQ" \ --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-14B-Glyph-AWQ", "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-14B-Glyph-AWQ" \ --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-14B-Glyph-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Coralfil/Atlantis-Pyramid-14B-Glyph-AWQ with Docker Model Runner:
docker model run hf.co/Coralfil/Atlantis-Pyramid-14B-Glyph-AWQ
Configuration Parsing Warning:In config.json: "quantization_config.modules_to_not_convert" must be an array
Atlantis-Pyramid 14B (Glyph) — AWQ
Production 4-bit AWQ quantization of Coralfil/Atlantis-Pyramid-14B-Glyph — Coralfil's marine-science LLM, purpose-built for the ocean economy and serving as the intelligence layer behind the Atlantis assistant in Coralfil OS (os.coralfil.com).
Atlantis-Pyramid (Glyph) is a domain-specialized model for marine biologists, aquaculture operators, coral and mariculture restoration teams, compliance and testing laboratories, and research educators. It reasons over ocean-climate stressors and produces field-actionable, science-first guidance — not generic chatbot answers.
Domain Focus
- Ocean restoration — coral reef and oyster reef recovery planning, larval settlement enhancement, substrate and broodstock protocols
- Ocean climate — marine heatwave / SST anomaly response, hypoxia and deoxygenation pathways, blue-carbon context
- Ocean acidification remedies — aragonite saturation state (Ω) management, benthic pH buffering with mineral matrices (e.g., CaCO₃, shell-hash), slow-release alkalinity interventions
- Aquaculture & mariculture — shellfish and finfish health, Vibrio spp. suppression, prebiotic formulation (FOS/XOS, chitin/chitosan, alginate carriers), feed and probiotic-vs-prebiotic decision support
- Water management — salinity/nutrient/ turbidity interpretation, carrying-capacity reasoning for lease sites, integrated multi-trophic aquaculture (IMTA) balancing
- Compliance & research — regulatory Q&A for testing labs, study design assistance for students and professors, formulation audit trails
Every formulation recommendation follows Coralfil's prebiotic-first doctrine: prebiotic interventions over probiotics, natural mineral/compound sources from marine waste streams, and explicit attention to pH, temperature, and pathogen suppression.
Release Notes — v2.0 (2026-08-10 UTC)
- Renamed for parameter honesty. Formerly published as
Atlantis-Pyramid-32B-AWQ; measured weights are ~14.7B parameters (Qwen2.5-14B dimensions: 48 layers, hidden 5120). The "32B" label was a lineage artifact — corrected across all repos for the Coralfil OS 2.0 launch. Old URLs redirect here permanently. - (Glyph) designation — this is the production Glyph-line release: the compact, fast, cost-efficient Atlantis tier designed for on-prem / single-GPU deployment without usage-meter burn.
- First production quantized release. Replaces the interim GPT-2 124M placeholder that was mistakenly serving production chat (discovered during the Coralfil full-stack audit, 2026-08-09).
- Quantized W4A16 (AWQ, GEMM, group size 128, zero-point) with AutoAWQ, default calibration.
- Verified live on Lambda Labs A10 (24 GB): vLLM 0.26,
--max-model-len 8192, API-key enforced (401 unauthenticated / 200 authenticated), engine--restart always. - End-to-end production verification (2026-08-10): live chat path os.coralfil.com → Cloudflare tunnel → FastAPI os-api → vLLM → Atlantis answered oyster-farm Vibrio/pH-drift and kelp-heat-stress prompts with structured, domain-correct formulation protocols.
Specification
| Property | Value |
|---|---|
| Architecture | Qwen2ForCausalLM (48 layers, hidden 5120, vocab 152,064) |
| Measured parameters | ~14.7B (Qwen2.5-14B dimensions) |
| Source weights | Coralfil/Atlantis-Pyramid-14B-Glyph (bf16 merge, 29.5 GB) |
| Quantized size | 9.4 GB (2 safetensors shards + index) |
| Quantization | AWQ W4A16, GEMM, q_group_size 128, zero_point |
| Tokenizer | Qwen2.5 BPE (same as fine-tune — no tokenization drift) |
| Native context | 32,768 tokens (serve with --max-model-len per VRAM; 8,192 validated on A10-24GB) |
| Recommended runtime | vLLM ≥ 0.26 (--gpu-memory-utilization 0.92 on 24 GB) |
Serving (vLLM)
docker run -d --gpus all --restart always -p 8080:8000 \
-v /models:/workspace/models \
vllm/vllm-openai:latest \
--model /workspace/models/Atlantis-Pyramid-14B-Glyph-AWQ \
--served-model-name atlantis-pyramid-14b-glyph-awq \
--max-model-len 8192 \
--gpu-memory-utilization 0.92 \
--api-key "$VLLM_API_KEY"
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8080/v1", api_key="<VLLM_API_KEY>")
client.chat.completions.create(
model="atlantis-pyramid-14b-glyph-awq",
messages=[{"role": "user", "content": "Design a broodstock conditioning protocol for Crassostrea gigas under warming SSTs."}],
)
Deployment note: the current Coralfil production instance still answers to the legacy served id
atlantis-pyramid-32b-awq; the id alias migrates toatlantis-pyramid-14b-glyph-awqwith the OS 2.0 backend cutover.
Intended Use & Limitations
- Intended for professional decision-support in marine science and aquaculture; outputs should be validated by qualified personnel before field deployment, regulatory submission, or animal-welfare-critical actions.
- Trained for technical accuracy within marine domains; general-knowledge breadth is narrower than frontier general-purpose models.
- License:
other— see Coralfil for commercial terms.
Audit Trail
Full-stack audit lineage (naming correction F-34, placeholder-model discovery F-27, quantization + hardened serving MS-12) is documented in the Coralfil OS audit ledger. Base model training: SFT + DPO completed 2026-07-14→16 (wandb-tracked).
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Model tree for Coralfil/Atlantis-Pyramid-14B-Glyph-AWQ
Base model
Qwen/Qwen2.5-14B