Instructions to use aymanmakroo/kav with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aymanmakroo/kav with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-1.3B") model = PeftModel.from_pretrained(base_model, "aymanmakroo/kav") - Notebooks
- Google Colab
- Kaggle
metadata
license: cc-by-nc-4.0
base_model: facebook/nllb-200-distilled-1.3B
library_name: peft
language:
- en
- ks
tags:
- translation
- machine-translation
- kashmiri
- nllb
- lora
- peft
pipeline_tag: translation
datasets:
- ai4bharat/BPCC
metrics:
- chrf
- bleu
kāv: English to Kashmiri Machine Translation
A LoRA adapter fine-tuning facebook/nllb-200-distilled-1.3B for English to Kashmiri
(Perso-Arabic script, kas_Arab) translation.
Full training methodology, data details, decoding configuration, and the post-processing pipeline this adapter is meant to be used with are documented in the companion GitHub repository:aym-n/kav
Adapter details
- Base model:
facebook/nllb-200-distilled-1.3B - Method: LoRA (PEFT),
r=32,alpha=64,dropout=0.05 - Target modules:
q_proj, k_proj, v_proj, out_proj, fc1, fc2(attention and feed-forward layers) - Source language:
eng_Latn— Target language:kas_Arab