Instructions to use Emarthar/nllb-bpy-beng-v8_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Emarthar/nllb-bpy-beng-v8_4 with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") model = PeftModel.from_pretrained(base_model, "Emarthar/nllb-bpy-beng-v8_4") - Notebooks
- Google Colab
- Kaggle
NLLB Bishnupriya Manipuri V8.4
LoRA fine-tune of facebook/nllb-200-distilled-600M for English → Bishnupriya Manipuri.
Status: Production - outputs pure BPY, not Assamese/Bengali.
Training: 2558 pairs, 400 weighted for core vocab. Val_loss ~0.85.
Quick start
from peft import PeftModel
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
base = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
model = PeftModel.from_pretrained(base, "Emarthar/nllb-bpy-beng-v8_4")
tokenizer = AutoTokenizer.from_pretrained("Emarthar/nllb-bpy-beng-v8_4")
model.eval()
def translate(text):
tokenizer.src_lang = "eng_Latn"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
out = model.generate(
**inputs,
forced_bos_token_id=tokenizer.convert_tokens_to_ids("asm_Beng"),
max_new_tokens=64,
num_beams=5
)
return tokenizer.batch_decode(out, skip_special_tokens=True)[0]
print(translate("Water is important")) # পানীহান দরকারি
print(translate("The sky is blue")) # হাগহান নীলুৱাহান
print(translate("My name is Arunita")) # মর নাংহান অরুনিতা
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Model tree for Emarthar/nllb-bpy-beng-v8_4
Base model
facebook/nllb-200-distilled-600M