Instructions to use Aynkader/translategemma-balochi-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Aynkader/translategemma-balochi-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/translategemma-4b-it") model = PeftModel.from_pretrained(base_model, "Aynkader/translategemma-balochi-adapter") - Notebooks
- Google Colab
- Kaggle
TranslateGemma Balochi Latin LoRA (adapter)
LoRA adapter fine-tuned on Balochi Latin (Syáhag) ↔ English for google/translategemma-4b-it.
- Checkpoint: 8100 (best eval_loss ≈ 0.436)
- Script: Latin Syáhag (
á/é/ó, digraphs Ch/Dh/Sh/Th/Zh) - Owner: Aynkader
- Demo Space: Aynkader/rajank
Load
from peft import PeftModel
from transformers import AutoModelForImageTextToText, AutoProcessor
import torch
base = "google/translategemma-4b-it"
adapter = "Aynkader/translategemma-balochi-adapter"
processor = AutoProcessor.from_pretrained(base, token=True)
model = AutoModelForImageTextToText.from_pretrained(
base, torch_dtype=torch.bfloat16, device_map="auto", token=True
)
model = PeftModel.from_pretrained(model, adapter)
Accept the base model license and set HF_TOKEN for gated access.
Related
- Training tree:
Aynkader/translategemma-balochi-latin
- Downloads last month
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Model tree for Aynkader/translategemma-balochi-adapter
Base model
google/translategemma-4b-it