DIA-MVP/tinyllama-lora-cpu

TinyLlama 1.1B Chat — LoRA (CPU (80-core))

A demo model from the Data & Impact Accounting (DIA) lab. It performs instruction-tuning (LoRA adapter) via LoRA (PEFT), with the base model TinyLlama/TinyLlama-1.1B-Chat-v1.0, trained on CPU (80-core).

The point of this repo is not the model itself but its `dia_report` — a standardized record of the energy, carbon, and water used to train it, embedded in this card's metadata.

This footprint feeds the DIA dashboard, which rolls up a base model and all its derivatives to show the cumulative carbon, water, and energy cost of a model family.

Training footprint

Energy and carbon are measured with [CodeCarbon](https://github.com/mlco2/codecarbon); water is estimated from a default water-usage-effectiveness range. Carbon uses the local grid's intensity (Ontario, ~0.03 kgCO₂eq/kWh).

Reproduce

REPO=DIA-MVP/tinyllama-lora-cpu python scripts/train_llama_lora.py

Links

• —Footprint table (dataset): DIA-MVP/dia-state-lab-2026
• —Project / paper: ai-impact-accounting
• —Lab workflow: see LAB.md in the repo