Feature Extraction
PEFT
embeddings
text-classification
distillation
knowledge-distillation
generated-code
status:scaffold
Instructions to use Kodep/jev-topic-head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Kodep/jev-topic-head with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 562 Bytes
4e75514 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # checkpoints/
Trained weights for the chosen head.
## Files to push
| path | what it is |
|---|---|
| `lora/` | LoRA adapter (`.safetensors`). Report: rank 16, alpha 32. |
| `head/` | 56 prototype vectors + per-topic learned scale (`.safetensors` or `.npy`) |
## Notes to include
- Exact file names and **tensor shapes** (e.g. prototypes `56 × <dim>`, scale `56`).
- Which **seed** (report says the shipped run = **seed 0**).
- Embedding dim of the base model (expected 1024 for the 0.6B — confirm).
- How to load them in `train_head.py` / `evaluate.py`.
|