Instructions to use Tohirju/gemma4-31b-tg-sft-e1-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Tohirju/gemma4-31b-tg-sft-e1-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-31B") model = PeftModel.from_pretrained(base_model, "Tohirju/gemma4-31b-tg-sft-e1-lora") - Notebooks
- Google Colab
- Kaggle
Gemma-4-31B Tajik SFT e1 (LoRA adapter)
LoRA r32/a32 (rslora), lr 1e-5, 1 epoch over 1,112,888 prompt-masked Tajik ChatML rows (Tohirju/CA4_SFT_ChatML tg slice), seq 1024, eff. batch 64, 4xH100. Final loss 0.753 / best 0.544.
Eval (gen mode, fixed training-matched template, full benches):
| bench | base | SFT-e1 |
|---|---|---|
| tajik-curated (657) | 58.30 | 59.97 |
| belebele-tgk (900) | 84.56 | 83.78 |
MCQ ~unchanged (competent-base regime); SFT value = clean Tajik instruction-following (think-then-answer format). IMPORTANT: inference must use the TRAINING template (generation prompt ends at the model-turn header, NO thought-channel scaffold) — the stock -it template yields empty turns on this adapter. Public, gated=manual.
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Base model
google/gemma-4-31B