Text Generation
PEFT
Safetensors
English
qwen2
marine-science
oceanography
aquaculture
coastal-restoration
environmental-compliance
dfo-statutory
qwen
deepseek-r1-distill
lora
vllm
awq
cuda
aboveboard-benchmark
conversational
Eval Results (legacy)
Instructions to use Coralfil/Atlantis-Pyramid-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Coralfil/Atlantis-Pyramid-v2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-32B-Instruct") model = PeftModel.from_pretrained(base_model, "Coralfil/Atlantis-Pyramid-v2") - Notebooks
- Google Colab
- Kaggle
File size: 6,974 Bytes
ee120da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | {
"model_name": "Atlantis-Pyramid v2",
"base_model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
"budget": {
"total_authorized_spend_usd": 500.0,
"allocation": {
"a100_training_allocation_usd": 175.0,
"a100_hourly_rate_usd": 1.29,
"a100_estimated_compute_hours": 135.6,
"a10_serving_allocation_usd": 325.0,
"a10_hourly_rate_usd": 0.60,
"a10_estimated_serving_hours": 541.6
},
"cost_optimization_policy": "Enforce 20-minute idle watchdog shutdown on A10 inference instance. Prevent 24/7 idle billing."
},
"sequential_training": {
"enabled": true,
"strategy": "sequential_lora_adapters",
"rationale": "Train distinct LoRA adapters sequentially across DeepSeek-R1 reasoning, Qwen2.5 direct instruction, and Mistral European outlier architectures to gauge cross-foundation synergy without weight corruption.",
"models": [
{
"id": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
"license": "MIT",
"context_window": 131072,
"role": "Primary scientific deep reasoning with native chain of thought tags",
"output_dir": "D:/Projects/os/atlantis/builds/atlantis-pyramid-32b/outputs/pyramid-32b-deepseek-r1-lora",
"s3_prefix": "models/atlantis-pyramid-v2/lora-deepseek-r1-32b"
},
{
"id": "Qwen/Qwen2.5-32B-Instruct",
"license": "Apache 2.0",
"context_window": 131072,
"role": "Direct instruction and high capacity operational co foundation",
"output_dir": "D:/Projects/os/atlantis/builds/atlantis-pyramid-32b/outputs/pyramid-32b-qwen25-lora",
"s3_prefix": "models/atlantis-pyramid-v2/lora-qwen25-32b"
},
{
"id": "mistralai/Mistral-Small-Instruct-2409",
"license": "Apache 2.0",
"context_window": 32768,
"role": "European sovereign model outlier for cross architecture synergy validation",
"output_dir": "D:/Projects/os/atlantis/builds/atlantis-pyramid-32b/outputs/pyramid-24b-mistral-lora",
"s3_prefix": "models/atlantis-pyramid-v2/lora-mistral-small-24b"
}
]
},
"lora": {
"r": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"bias": "none",
"target_modules": [
"q_proj",
"k_proj",
"v_proj",
"o_proj"
],
"task_type": "CAUSAL_LM"
},
"training": {
"per_device_train_batch_size": 1,
"gradient_accumulation_steps": 16,
"learning_rate": 0.00015,
"lr_scheduler_type": "cosine",
"warmup_ratio": 0.05,
"num_train_epochs": 3,
"max_steps": 500,
"weight_decay": 0.01,
"optim": "paged_adamw_8bit",
"fp16": false,
"bf16": true,
"gradient_checkpointing": true,
"logging_steps": 5,
"save_strategy": "steps",
"save_steps": 50,
"save_total_limit": 3
},
"dataset": {
"train_file": "D:/Projects/os/atlantis/builds/atlantis-pyramid-32b/dataset/atlantis_pyramid_sft_packed.jsonl",
"curated_file": "D:/Projects/os/atlantis/builds/atlantis-pyramid-32b/dataset/atlantis_pyramid_sft_curated.jsonl",
"dpo_file": "D:/Projects/os/atlantis/builds/atlantis-pyramid-32b/dataset/atlantis_pyramid_dpo.jsonl",
"max_seq_length": 131072,
"packing": true
},
"cache_strategy": {
"automatic_prefix_caching": true,
"chunked_prefill": true,
"fp8_kv_cache": true,
"torch_inductor_cache": "/workspace/.cache/torch_inductor",
"vllm_cache_root": "/workspace/.cache/vllm",
"lazy_module_loading": true
},
"a10_serving_profile": {
"instance_type": "gpu_1x_a10",
"vram_gb": 24,
"quantization": "awq_int4",
"kv_cache_dtype": "fp8",
"max_model_len": 16384,
"gpu_memory_utilization": 0.85,
"idle_watchdog_timeout_minutes": 20
},
"evaluation": {
"suite": "AboveBoard 52-Scenario Marine Suite",
"benchmark_file": "D:/Projects/os/data/corpora/canadian_marine/aboveboard_benchmark.json",
"scoring_standard": "CRF-FS-ABOVEBOARD-52",
"categories": [
"Category A: Statutory Marine Compliance (20 scenarios)",
"Category B: Physical Oceanography and Biogeochemistry (18 scenarios)",
"Category C: Benthic Restoration and Structural Biomaterials (14 scenarios)"
],
"pass_threshold": 0.88,
"safety_guardrails": {
"prebiotic_chemistry_only": true,
"prohibit_antibiotics": true,
"prohibit_probiotics": true,
"fisheries_act_section_36_enforced": true,
"deter_biological_pathogen_engineering": true,
"deter_toxic_dumping_and_leaching": true,
"prohibit_unauthorized_mpa_core_disturbance": true
}
},
"frontier_safety_framework": {
"version": "CFSF-v1.0-Xray",
"policy_document": "D:/Projects/os/atlantis/builds/atlantis-pyramid-32b/coralfil_frontier_safety_framework_v1.0_Xray_2026-10-05T11-30-00.md",
"security_level_mandate": "CSL-2+",
"capability_thresholds": {
"domain_a_ecotoxicity_cbrn": {
"tracked_capability_level": "M-TCL-A1",
"critical_capability_level": "M-CCL-A1",
"alert_threshold_ratio": 0.70,
"mitigation": "DPO refusal steering, strict prohibition of open-water antibiotics, probiotics, and toxic mineral leaching"
},
"domain_b_maritime_cyber": {
"tracked_capability_level": "M-TCL-B1",
"critical_capability_level": "M-CCL-B1",
"alert_threshold_ratio": 0.70,
"mitigation": "Read-only sandbox, denial of socket creation and port scanning, telemetry signature validation"
},
"domain_c_agentic_stealth_misalignment": {
"tracked_capability_level": "M-TCL-C1",
"critical_capability_level": "M-CCL-C1",
"alert_threshold_ratio": 0.70,
"mitigation": "Arize Phoenix chain-of-thought inspection for deceptive alignment, tool affordance clamping"
},
"domain_d_ml_rd_exfiltration": {
"tracked_capability_level": "M-TCL-D1",
"critical_capability_level": "M-CCL-D1",
"alert_threshold_ratio": 0.70,
"mitigation": "Non-destructive S3 versioning, model weight HMAC signing, 20-minute idle watchdog shutdown"
}
},
"safety_case_required_at_ccl": true
},
"observability": {
"arize_phoenix": {
"enabled": true,
"endpoint": "http://localhost:6006",
"project_name": "atlantis-pyramid-v2"
},
"wandb": {
"enabled": true,
"project": "atlantis-pyramid-v2",
"entity": "coralfil"
}
},
"storage_sync": {
"target_s3_bucket": "f46ad631-a75f-4cd1-ae30-55973c5ecb55",
"target_s3_prefix": "models/atlantis-pyramid-v2/lora",
"region": "ca-central-1"
},
"export_recipes": {
"awq_int4": {
"quant_method": "awq",
"bits": 4,
"group_size": 128,
"zero_point": true,
"version": "GEMM",
"calib_data": "dataset/atlantis_pyramid_sft_curated.jsonl"
},
"fp8": {
"quant_method": "fp8",
"format": "e4m3fn",
"kv_cache_dtype": "fp8_e4m3",
"activation_scheme": "dynamic"
}
}
} |