Text Generation
PEFT
Safetensors
gemma
machine-translation
english-german
supervised-fine-tuning
lora
wmt
sft-benchmark
Instructions to use gaokerena/amestris-1b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use gaokerena/amestris-1b-sft with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
SFT-hard-27k Gemma3 English→German Benchmark
This repository contains the supervised fine-tuning benchmark checkpoint for the hard-27k English→German translation experiment.
Repository
[github repository](https://github.com/Mehrdadghassabi/Amestris)
Base model
google/gemma-3-1b-it
Training setup
This benchmark follows the SFT-hard-27k setting:
- Input column:
prompt - Target column:
prefered_answer - Audit-only column:
rejected_answer - The rejected answer is not used in the supervised fine-tuning loss.
- Fine-tuning method: LoRA / PEFT
- LoRA rank:
32 - LoRA alpha:
32 - LoRA dropout:
0.05 - Max sequence length:
768 - Epochs:
1 - Learning rate:
5e-05 - Seed:
42
Contents
sft_hard27k_lora_adapter/: PEFT LoRA adapter checkpoint.archives/: compressed archive created after Cell 9A, if uploaded.metadata/: upload metadata and reproducibility information.
Notes
The uploaded adapter is intended to be loaded with the gated base model google/gemma-3-1b-it.
Users must have accepted the Gemma license on Hugging Face to load the base model.
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