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
| 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](https://github.com/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` | |