MaplePT: A Generalized Instruction-Tuned Canadian Language Model Trained on Distributed Low-Cost Compute
Bibliographic record
Abstract
MaplePT is a Canadian-developed instruction-response large language model trained on a diverse Canadian corpus to follow natural language instructions in local tone and context. It demonstrates that sovereign AI models can be developed using modest, distributed computing resources without reliance on foreign cloud infrastructure. We describe how an initial single-GPU fine-tuning experiment on an RTX 4080 was scaled to a multi-GPU cluster of heterogeneous consumer-grade GPUs connected only via 1 Gbps Ethernet. Using Hugging Face Transformers with 4-bit quantization and LoRA-based fine-tuning, we achieved 12k tokens/s throughput and 90-95 % scaling efficiency with ~99 % GPU utilization. These results highlight that Canada's sovereign AI initiative can leverage affordable, energy-efficient local hardware to produce high-performance language models. MaplePT serves as a case study in building advanced AI that is locally controlled, cost-effective, and aligned with Canadian values.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".