Instructions to use webbrain-one/gemma4-turkish-26b-a4b-pruned-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use webbrain-one/gemma4-turkish-26b-a4b-pruned-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/root/work/work/pruned") model = PeftModel.from_pretrained(base_model, "webbrain-one/gemma4-turkish-26b-a4b-pruned-lora") - Notebooks
- Google Colab
- Kaggle
Gemma 4 26B-A4B Pruned — Türkçe LoRA Adapter
Bu adapter esokullu/gemma4-tr-26b-a4b-pruned (pruned bf16) base'i üzerinde
eğitildi. Merged versiyon için: esokullu/gemma4-tr-26b-a4b-pruned.
Eğitim
- LoRA r=32, α=64, dropout=0
- target_modules: "all-linear"
- 2 epoch, 25k sample (TR/code/math mix)
- A100 80GB, ~2 saat
Kullanım
```python from peft import PeftModel from transformers import AutoModelForCausalLM import torch
base = AutoModelForCausalLM.from_pretrained( "esokullu/gemma4-tr-26b-a4b-pruned", torch_dtype=torch.bfloat16, device_map="auto", ) model = PeftModel.from_pretrained(base, "esokullu/gemma4-turkish-26b-a4b-pruned-lora") ```
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