Trainer flags
typed-lm-trainer has two subcommands: train and quantize. Both accept
--device. Values resolve with the precedence CLI flag > TOML key > default.
train
| Flag | Description | Default |
|---|---|---|
--model-id | Local base checkpoint (directory) | Qwen/Qwen2.5-1.5B-Instruct |
--dataset | Dataset file or directory | required |
--output-directory | Destination | output/train |
--method | lora, qlora, full or from-scratch | lora |
--seed | Deterministic initialization seed (from-scratch) | 42 |
--configuration-file | Optional TOML; explicit CLI flags win | — |
--tokenizer-file | tokenizer.json for from-scratch | — |
--lora-rank / --lora-alpha | LoRA rank and alpha | 16 / 32 |
--lora-dropout | Adapter dropout | 0 |
--epochs | Epochs | 3 |
--batch-size | Batch per step | 4 |
--gradient-accumulation-steps | Micro-batches per step | 1 |
--learning-rate | Peak LR (warmup + cosine) | 1e-4 |
--warmup-steps | Warmup steps | 10 |
--weight-decay | AdamW weight decay | 0 |
--maximum-gradient-norm | Gradient-norm clipping | 1 |
--max-sequence-length | Maximum prompt length; longer items skipped | 1024 |
--minimum-improvement | Improvement that resets patience | 0 |
--early-stop-patience | Epochs without improvement before stopping (0 disables) | 0 |
--quantization | none, fp8 or fp4 | none |
--quantization-mode | post-training or training | post-training |
--device | auto, cpu or cuda | auto |
Geometry flags (full / from-scratch)
--architecture (llama, qwen2, qwen3, mistral, gemma, gemma2,
gemma3), --vocab-size, --hidden-size, --intermediate-size,
--num-hidden-layers, --num-attention-heads, --head-dim,
--num-key-value-heads, --max-position-embeddings, --rope-theta,
--rms-norm-eps, --tie-word-embeddings, --attention-bias, --sliding-window,
--sliding-window-pattern, --rope-local-base-frequency,
--query-pre-attention-scalar, --logit-softcapping,
--attention-logit-softcapping.
quantize
| Flag | Description | Default |
|---|---|---|
--model-id | Dense checkpoint directory | required |
--adapter-directory | Optional adapter to merge before quantizing | — |
--quantization | none, fp8 or fp4 | fp8 |
--output-directory | Destination | output/quantized |
--device | auto, cpu or cuda | auto |
Examples
# Train a LoRA adapter.
typed-lm-trainer train \
--model-id /path/to/local/checkpoint \
--dataset resources/dataset.jsonl \
--output-directory output/train \
--method lora --epochs 3 --batch-size 4 --learning-rate 1e-4
# Quantize while merging the adapter.
typed-lm-trainer quantize \
--model-id /path/to/local/checkpoint \
--adapter-directory output/train \
--quantization fp8 --output-directory output/quantized