Instructions to use SinhalaMMLU-SharedTask/team-zeroShot-gemma4-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SinhalaMMLU-SharedTask/team-zeroShot-gemma4-lora with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
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Download README.md from SinhalaMMLU-SharedTask/team-zeroShot-gemma4-lora: direct link, hf CLI and curl.
- Browser
- Download file 930 Bytes
-
https://huggingface.co/SinhalaMMLU-SharedTask/team-zeroShot-gemma4-lora/resolve/main/README.md
- Command line
-
hf download hf://SinhalaMMLU-SharedTask/team-zeroShot-gemma4-lora/README.md
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curl -L -o README.md https://huggingface.co/SinhalaMMLU-SharedTask/team-zeroShot-gemma4-lora/resolve/main/README.md
930 Bytes
metadata
base_model: Lalaa-a/gemma4-8b-text-only
library_name: peft
license: gemma
tags:
- sinhala
- mmlu
- multiple-choice
- lora
Sinhala MMLU: LoRA adapter + choice head
LoRA adapter (rank 32, all 24 kept layers) for
Lalaa-a/gemma4-8b-text-only (text weights of
google/gemma-4-E4B-it) cut after layer 24, plus head.pt: a small transformer choice head that scores each
option from Gemma's hidden states. Not usable with plain AutoModelForCausalLM: load it with the team's
inference.py (main repository), which rebuilds the cut model from meta.json.
| File | What |
|---|---|
lora/ |
PEFT adapter (adapter_config.json, adapter_model.safetensors) |
head.pt |
choice head weights (PyTorch state dict) |
meta.json |
how to rebuild the network: layer cut, head size, token limits, readout |
config.yaml |
the full training config of this checkpoint |