Koushim
/

lora-mbart-en-te

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 ---
+license: mit
+tags:
+  - machine-translation
+  - mbart
+  - multilingual
+  - huggingface
+  - peft
+  - lora
+  - english
+  - telugu
+datasets:
+  - HackHedron/English_Telugu_Parallel_Corpus
+language:
+  - en
+  - te
+library_name: peft
+inference: false
+widget:
+  - text: "Hello, how are you?"
 ---
+# 🌍 LoRA-mBART50: English ↔ Telugu Translation (Few-shot)
+This model is a parameter-efficient fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) using [LoRA (Low-Rank Adaptation)](https://arxiv.org/abs/2106.09685) via the Hugging Face PEFT library.
+It is fine-tuned in a **few-shot setting** on the [HackHedron English-Telugu Parallel Corpus](https://huggingface.co/datasets/HackHedron/English_Telugu_Parallel_Corpus) using just **1% of the data (~4.3k pairs)**.
+---
+## 🧠 Model Details
+- **Base model**: `facebook/mbart-large-50-many-to-many-mmt`
+- **Languages**: `en_XX` ↔ `te_IN`
+- **Technique**: LoRA (r=8, α=32, dropout=0.1)
+- **Training regime**: 3 epochs, batch size 8, learning rate 5e-4
+- **Library**: 🤗 PEFT (`peft`), `transformers`, `datasets`
+---
+## 📚 Dataset
+- **Source**: [HackHedron/English_Telugu_Parallel_Corpus](https://huggingface.co/datasets/HackHedron/English_Telugu_Parallel_Corpus)
+- **Size used**: 4338 sentence pairs (~1%)
+- **Format**:
+  - `english`: Source text
+  - `telugu`: Target translation
+---
+## 💻 Usage
+### Load Adapter with Base mBART
+```python
+from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+from peft import PeftModel
+# Load base model & tokenizer
+base_model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+tokenizer = MBart50TokenizerFast.from_pretrained("your-username/lora-mbart-en-te")
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "your-username/lora-mbart-en-te")
+# Set source and target languages
+tokenizer.src_lang = "en_XX"
+tokenizer.tgt_lang = "te_IN"
+# Prepare input
+inputs = tokenizer("Hello, how are you?", return_tensors="pt")
+generated_ids = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["te_IN"])
+translation = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+print(translation)
+````
+---
+## 🔧 Training Configuration
+| Setting         | Value    |
+| --------------- | -------- |
+| Base Model      | mBART-50 |
+| LoRA r          | 8        |
+| LoRA Alpha      | 32       |
+| Dropout         | 0.1      |
+| Optimizer       | AdamW    |
+| Batch Size      | 8        |
+| Epochs          | 3        |
+| Mixed Precision | fp16     |
+---
+## 🚀 Applications
+* English ↔ Telugu translation for low-resource settings
+* Mobile/Edge inference with minimal memory
+* Foundation for multilingual LoRA adapters
+---
+## ⚠️ Limitations
+* Trained on limited data (1% subset)
+* Translation quality may vary on unseen or complex sentences
+* Only supports `en_XX` and `te_IN` (Telugu) at this stage
+---
+## 📎 Citation
+If you use this model, please cite the base model:
+```bibtex
+@inproceedings{liu2020mbart,
+  title={Multilingual Denoising Pre-training for Neural Machine Translation},
+  author={Liu, Yinhan and others},
+  booktitle={ACL},
+  year={2020}
+}
+```
+---
+## 🧑‍💻 Author
+Fine-tuned by **Koushik Reddy**, ML & DL Enthusiast | NLP | LoRA | mBART | Hugging Face
+Connect: [Hugging Face](https://huggingface.co/Koushim)