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
|
Download checkpoints/README.md from Kodep/jev-topic-head: direct link, hf CLI and curl.
- Browser
- Download file 562 Bytes
-
https://huggingface.co/Kodep/jev-topic-head/resolve/main/checkpoints/README.md
- Command line
-
hf download hf://Kodep/jev-topic-head/checkpoints/README.md
-
curl -L -o README.md https://huggingface.co/Kodep/jev-topic-head/resolve/main/checkpoints/README.md
562 Bytes
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>, scale56). - 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.