model_id stringlengths 22 30 | has_report bool 1
class | parent_models stringclasses 6
values | relation stringclasses 2
values | updated_at stringlengths 32 32 | scope stringclasses 1
value | energy_kwh_lo float64 0 0.68 | energy_kwh_hi float64 0 0.68 | carbon_kgco2eq_lo float64 0 0.04 | carbon_kgco2eq_hi float64 0 0.04 | water_liters_lo float64 0.01 1.23 | water_liters_hi float64 0.02 2.73 | energy_quality stringclasses 1
value | carbon_quality stringclasses 1
value | water_quality stringclasses 1
value | gpu stringclasses 3
values | gpu_count int64 1 1 | gpu_hours float64 0.02 1.62 | region stringclasses 1
value | tool stringclasses 1
value | method stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DIA-MVP/cifar10-simclr-a100 | true | scratch | finetune | 2026-06-29T03:33:54.196574+00:00 | incremental | 0.0105 | 0.0105 | 0.0007 | 0.0007 | 0.019 | 0.042 | measured | measured | estimated-from-default-wue | NVIDIA A100-SXM4-80GB | 1 | 0.0555 | ca-on | codecarbon | finetune |
DIA-MVP/cifar10-simclr-a40 | true | scratch | finetune | 2026-06-29T03:33:54.866054+00:00 | incremental | 0.0137 | 0.0137 | 0.0009 | 0.0009 | 0.025 | 0.055 | measured | measured | estimated-from-default-wue | NVIDIA A40 | 1 | 0.0663 | ca-on | codecarbon | finetune |
DIA-MVP/llama32-3b-lora-a100 | true | meta-llama/Llama-3.2-3B-Instruct | lora | 2026-06-29T03:33:53.825970+00:00 | incremental | 0.1082 | 0.1082 | 0.007 | 0.007 | 0.195 | 0.433 | measured | measured | estimated-from-default-wue | NVIDIA A100-SXM4-80GB | 1 | 0.3014 | ca-on | codecarbon | lora |
DIA-MVP/mnist-ddpm-a100 | true | scratch | finetune | 2026-06-29T03:33:54.355602+00:00 | incremental | 0.104 | 0.104 | 0.0067 | 0.0067 | 0.187 | 0.416 | measured | measured | estimated-from-default-wue | NVIDIA A100-SXM4-80GB | 1 | 0.5017 | ca-on | codecarbon | finetune |
DIA-MVP/mnist-ddpm-cpu | true | scratch | finetune | 2026-06-29T03:33:55.227132+00:00 | incremental | 0.0244 | 0.0244 | 0.0016 | 0.0016 | 0.044 | 0.098 | measured | measured | estimated-from-default-wue | cpu-80core | 1 | 0.502 | ca-on | codecarbon | finetune |
DIA-MVP/my-bert-sentiment-a100 | true | distilbert-base-uncased | finetune | 2026-06-29T03:33:53.723286+00:00 | incremental | 0.0049 | 0.0049 | 0.0003 | 0.0003 | 0.009 | 0.02 | measured | measured | estimated-from-default-wue | NVIDIA A100-SXM4-80GB | 1 | 0.017 | ca-on | codecarbon | finetune |
DIA-MVP/my-bert-sentiment-a40 | true | distilbert-base-uncased | finetune | 2026-06-29T03:33:54.606758+00:00 | incremental | 0.0061 | 0.0061 | 0.0004 | 0.0004 | 0.011 | 0.025 | measured | measured | estimated-from-default-wue | NVIDIA A40 | 1 | 0.0233 | ca-on | codecarbon | finetune |
DIA-MVP/my-bert-sentiment-cpu | true | distilbert-base-uncased | finetune | 2026-06-29T03:33:54.985136+00:00 | incremental | 0.026 | 0.026 | 0.0017 | 0.0017 | 0.047 | 0.104 | measured | measured | estimated-from-default-wue | cpu-80core | 1 | 0.5381 | ca-on | codecarbon | finetune |
DIA-MVP/qwen2.5-7b-lora-a100 | true | Qwen/Qwen2.5-7B | lora | 2026-06-29T03:33:53.930696+00:00 | incremental | 0.6828 | 0.6828 | 0.0442 | 0.0442 | 1.229 | 2.731 | measured | measured | estimated-from-default-wue | NVIDIA A100-SXM4-80GB | 1 | 1.6199 | ca-on | codecarbon | lora |
DIA-MVP/resnet50-cifar100-a100 | true | microsoft/resnet-50 | finetune | 2026-06-29T03:33:54.060043+00:00 | incremental | 0.1369 | 0.1369 | 0.0089 | 0.0089 | 0.246 | 0.548 | measured | measured | estimated-from-default-wue | NVIDIA A100-SXM4-80GB | 1 | 0.6684 | ca-on | codecarbon | finetune |
DIA-MVP/tinyllama-lora-a100 | true | TinyLlama/TinyLlama-1.1B-Chat-v1.0 | lora | 2026-06-29T03:33:54.475526+00:00 | incremental | 0.0124 | 0.0124 | 0.0008 | 0.0008 | 0.022 | 0.05 | measured | measured | estimated-from-default-wue | NVIDIA A100-SXM4-80GB | 1 | 0.035 | ca-on | codecarbon | lora |
DIA-MVP/tinyllama-lora-a40 | true | TinyLlama/TinyLlama-1.1B-Chat-v1.0 | lora | 2026-06-29T03:33:54.726944+00:00 | incremental | 0.0122 | 0.0122 | 0.0008 | 0.0008 | 0.022 | 0.049 | measured | measured | estimated-from-default-wue | NVIDIA A40 | 1 | 0.0415 | ca-on | codecarbon | lora |
DIA-MVP/tinyllama-lora-cpu | true | TinyLlama/TinyLlama-1.1B-Chat-v1.0 | lora | 2026-06-29T03:33:55.110378+00:00 | incremental | 0.0515 | 0.0515 | 0.0033 | 0.0033 | 0.093 | 0.206 | measured | measured | estimated-from-default-wue | cpu-80core | 1 | 1.0613 | ca-on | codecarbon | lora |
DIA lab footprint table
This dataset is the rollup table for the Data & Impact Accounting (DIA) lab demo. It indexes the training footprint (energy, carbon, water) and lineage of a set of demo models trained across A100 / A40 / CPU hardware.
It is produced by ingesting each model's dia_report card and is read by the DIA
Gradio dashboard. It stores metadata only — no model weights.
Files
nodes.parquet— one flat row per model (browsable in the Dataset Viewer): energy/carbon/water intervals, data-quality tier, GPU, GPU-hours, region, lineage.state.json— the nested source of truth the dashboard loads.
How the rollup works
Given a base model, the dashboard builds the lineage as a directed graph and takes the base plus all its descendants as the family, then:
- Sums incremental footprints — each model logs only its own training delta; the family total is the subtree sum.
- Dedupes the DAG — a merged/shared model is counted once.
- Reports coverage, not a bare total — totals are a lower bound at low disclosure.
- Keeps provenance separate —
measuredvsestimatedvsimputed.
Related
- Toolkit / paper: VectorInstitute/ai-impact-accounting
- Models: the
DIA-MVP/*-a100,*-a40,*-cpurepos ingested here.
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