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