Loggetta

Plan the run. Train the model. Keep the evidence.

Loggetta checks your hardware, chooses a supported configuration for a Mixture-of-Experts (MoE) model, and runs QLoRA fine-tuning. It saves the plan and a JSON run report with memory use, timing, and checks that the selected optimizations actually ran. One install includes the runtime (experts4bit-qlora) and the GPU kernels (grouped-nf4-gemm).

Limits. Plans are estimates, not an out-of-memory guarantee. The released training path is single-GPU MoE training on Linux with a supported NVIDIA CUDA GPU. Dense models are planned but not yet supported for training. Dense training

pip install loggetta
loggetta inspect
loggetta plan Qwen/Qwen3-30B-A3B --seq 2048

The plan shows where weights will live, estimated memory use, and why alternatives were rejected. It checks the budget before downloading model weights. Estimates can miss; they are not an out-of-memory guarantee.

New in 0.5.0

• Training plans no longer borrow another model's reserve. A model with no receipts on this GPU is priced at the card's worst measured training slack. In sample, no training plan now sits under its measured peak.
• Plans record the backend switches their estimate read (with experts4bit-qlora 0.52.0 or later), and loggetta execute warns when the running process differs.
• Dense plans: an estimate no longer falls when one of its terms rises.

Full list: CHANGELOG.

Train on your data

loggetta train Qwen/Qwen3-30B-A3B \
  --dataset ./data/train.jsonl --format text \
  --seq 512 --micro-batch 1 --steps 20 --seed 42 \
  --out runs/my-training --adapter-out adapters/my-adapter

Local JSONL, JSON, CSV, Parquet and TXT files, Hub datasets, Alpaca instructions and text-only chats are supported. Data is validated and tokenized before weights load. Chat data trains only the assistant turns by default. Reload the adapter with loggetta.load_adapter("adapters/my-adapter"). See the training guide.

To run a saved decision later: loggetta plan MODEL --out plan.json, then loggetta execute plan.json --out runs/. Pass earlier run reports back with --observations runs/ and later plans use those measurements.

Measured results

The included runtime and kernels do the compute; these are matched training runs, not planner benchmarks.

The comparison used torch 2.12.1+cu130 and transformers 5.5.0 for both frameworks, with matched adapters, initialization and tokens. Loggetta does not predict throughput.

Models

Every row is supported by the included runtime's QLoRA path. Hybrid models (Qwen3.6, Granite-4.0-H, LFM2, Nemotron-H) need --packing concat with chat or Alpaca data. The runtime's capability register is the authority.

Scope

Single-GPU MoE planning and QLoRA training; serving placement is planned, not launched. Not yet: multi-GPU, throughput prediction. GPU runs need Linux, an NVIDIA CUDA GPU and a compatible PyTorch. Pre-1.0.

Dense models are not supported for training yet: their plans are estimates that missed a held-out check (a further reading is pending), and their training runs only behind --allow-development-executor until a 24 GB capacity reading.

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