---
language:
- ru
- ba
tags:
- translation
- m2m100
- lora
- loresmt
license: mit
base_model: facebook/m2m100_418M
datasets:
- AigizK/bashkir-russian-parallel-corpora
metrics:
- chrf
---

# DevLake: M2M-100 (418M) for Russian-Bashkir

<div align="center">

| **Current Model** | **Architecture** | **Focus** |
|:---:|:---:|:---:|
| [🔴 Large Model](https://huggingface.co/Voldis/nllb-1.3b-rus-bak) | NLLB-1.3B (QLoRA) | Best Quality (SOTA) |
| 🟡 **Medium (This Model)** | **M2M-100 (LoRA)** | **Balanced / Baseline** |
| [🟢 Small Model](https://huggingface.co/Voldis/marian-rus-bak) | MarianMT (77M) | Fastest / CPU |

</div>

## Model Description

This is the **Medium-sized** model from the **DevLake** submission for LoResMT 2026. It serves as a robust baseline, balancing performance and resource usage. It was fine-tuned using **LoRA** on the `facebook/m2m100_418M` checkpoint.

- **Score:** 48.80 CHRF++
- **Code:** [GitHub Repository](https://github.com/Voldisoriginal/LoResMT-2026-Russian-Bashkir)

## Usage

```python
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel

base_model = "facebook/m2m100_418M"
adapter_model = "Voldis/m2m100-rus-bak"

# Load Model
model = AutoModelForSeq2SeqLM.from_pretrained(base_model, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_model)
tokenizer = AutoTokenizer.from_pretrained(adapter_model)

# Set Language
tokenizer.src_lang = "ru"
target_lang_id = tokenizer.get_lang_id("ba")

text = "Где находится библиотека?"
inputs = tokenizer(text, return_tensors="pt").to("cuda")

with torch.no_grad():
    generated_tokens = model.generate(
        **inputs,
        forced_bos_token_id=target_lang_id
    )

print(tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0])
```

## Training Details
- **Hardware:** Trained on a single NVIDIA RTX 3080.

## Citation

```bibtex
@inproceedings{tyurin-2026-devlake,
    title = "{D}ev{L}ake at {L}o{R}es{MT} 2026: The Impact of Pre-training and Model Scale on {R}ussian-{B}ashkir Low-Resource Translation",
    author = "Tyurin, Vyacheslav",
    booktitle = "Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.loresmt-1.18",
    doi = "10.18653/v1/2026.loresmt-1.18",
    pages = "209--212",
}
```