Instructions to use krystv/nomen-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use krystv/nomen-ai with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 1,252 Bytes
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## GPU preflight
Before training, run:
```bash
python scripts/preflight_gpu.py
```
or:
```bash
make preflight
```
Expected output:
```text
GPU_PREFLIGHT_PASS
```
This checks:
- PyTorch version
- CUDA availability
- GPU name
- total VRAM
- compute capability
- minimum T4-class VRAM
## GPU smoke test
The intended GPU smoke test is:
```bash
python scripts/smoke_test.py
```
This loads `Qwen/Qwen2.5-1.5B-Instruct`, runs 15 LoRA SFT steps on 100 examples, generates one candidate, and prints `SMOKE_PASS`.
From the agent environment this could not be executed because GPU/HF Jobs execution was repeatedly rejected.
## CPU static validation
For environments without a GPU, run:
```bash
git clone https://huggingface.co/krystv/nomen-ai
cd nomen-ai
pip install -e . datasets trl peft transformers rapidfuzz pyphen PyYAML
python tests/test_static.py
```
This validates:
- 20+ root families are present.
- Control token prompt construction.
- Syllable/character utilities.
- Anti-duplication matrix.
- Synthetic example generation.
- SFT dataset schema.
- DPO dataset schema.
- Current TRL `SFTConfig` and `DPOConfig` argument names used by the training scripts.
Expected output:
```text
CPU_STATIC_VALIDATION_PASS
```
|