peft safetensors text2text-generation en hi el base_model:facebook/m2m100_418M base_model:adapter:facebook/m2m100_418M license:mit region:us
---
base_model:
- facebook/m2m100_418M
language:
- en
- hi
- el
library_name: peft
license: mit
metrics:
- bleu
pipeline_tag: text2text-generation
new_version: aktheroy/FT_Translate_en_el_hi
---
# Model Card for aktheroy/FT_Translate_en_el_hi

This model is a fine-tuned version of `facebook/m2m100_418M`, designed for multilingual translation tasks between English (en), Greek (el), and Hindi (hi). The model achieves efficient translation by leveraging the M2M100 architecture, which supports many-to-many language translation.

## Model Details

### Model Description

- **Developed by:** Aktheroy
- **Model type:** Transformer-based encoder-decoder
- **Language(s) (NLP):** English, Hindi, Greek
- **License:** MIT
- **Finetuned from model:** facebook/m2m100_418M

### Model Sources

- **Repository:** [aktheroy/4bit_translate_en_el_hi](https://huggingface.co/aktheroy/4bit_translate_en_el_hi/)

## Uses

### Direct Use

The model can be used for translation tasks between the supported languages (English, Hindi, Greek). Use cases include:
- Cross-lingual communication
- Multilingual content generation
- Language learning assistance

### Downstream Use

The model can be fine-tuned further for domain-specific translation tasks, such as medical or legal translations.

### Out-of-Scope Use

The model is not suitable for:
- Translating unsupported languages
- Generating content for sensitive or harmful purposes

## Bias, Risks, and Limitations

While the model supports multilingual translations, it might exhibit:
- Biases from the pretraining and fine-tuning datasets.
- Reduced performance for idiomatic expressions or cultural nuances.

### Recommendations

Users should:
- Verify translations, especially for critical applications.
- Use supplementary tools to validate outputs in sensitive scenarios.

## How to Get Started with the Model

Here is an example of how to use the model for translation tasks:

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "aktheroy/4bit_translate_en_el_hi
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example input
input_text = "Hello, how are you?"
tokenizer.src_lang = "en"
tokenizer.tgt_lang = "hi"

# Tokenize and generate output
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
translation = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(translation)
```

## Training Details

### Training Data

The model was fine-tuned on a custom dataset containing parallel translations between English, Hindi, and Greek.

### Training Procedure

#### Preprocessing

The dataset was preprocessed to:
- Normalize text.
- Tokenize using the M2M100 tokenizer.

#### Training Hyperparameters

- **Epochs:** 10
- **Batch size:** 16
- **Learning rate:** 5e-5
- **Mixed Precision:** Disabled (FP32 used)

#### Speeds, Sizes, Times

- **Training runtime:** 20.3 hours
- **Training samples per second:** 17.508
- **Training steps per second:** 0.137
- **Final training loss:** 0.873

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

The model was evaluated on a held-out test set from the same domains as the training data.

#### Metrics

- BLEU score (to be computed during final evaluation).

### Results

- **Training Loss:** 0.873
- Detailed BLEU score results will be provided in subsequent updates.

## Environmental Impact

- **Hardware Type:** MacBook with M3 Pro
- **Hours used:** 20.3 hours
- **Cloud Provider:** Local hardware
- **Carbon Emitted:** Minimal (local training)

## Technical Specifications

### Model Architecture and Objective

The model is based on the M2M100 architecture, a transformer-based encoder-decoder model designed for multilingual translation without relying on English as an intermediary language.

### Compute Infrastructure

#### Hardware

- **Device:** MacBook with M3 Pro

#### Software

- Transformers library from Hugging Face
- Python 3.12

## Citation

If you use this model, please cite it as:

**APA:**
Aktheroy (2025). Fine-Tuned M2M100 Translation Model. Hugging Face. Retrieved from [https://huggingface.co/aktheroy/FT_Translate_en_el_hi](https://huggingface.co/aktheroy/FT_Translate_en_el_hi)

## Model Card Authors

- Aktheroy

## Model Card Contact

For questions or feedback, contact the author via Hugging Face.