README.md

---
base_model: AI-Sweden-Models/gpt-sw3-1.3b
library_name: peft
datasets:
- barbaroo/Sprotin_parallel
language:
- en
- fo
metrics:
- chrf
- bleu
- bertscore
pipeline_tag: text-generation
---

# Model Card: English–Faroese Translation Adapter

## Model Details

**Model Description**

- **Developed by:** Barbara Scalvini
- **Model type:** Language model adapter for **English → Faroese** translation  
- **Language(s):** English, Faroese  
- **License:** This adapter inherits the license from the original GPT-SW3 1.3B model.
- **Finetuned from model:** [AI-Sweden-Models/gpt-sw3-1.3b](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b)  
- **Library used:** [PEFT 0.13.0](https://github.com/huggingface/peft)

### Model Sources 

- **Paper:** [COMING SOON]  
---

## Uses

### Direct Use
This adapter is intended to perform **English→Faroese** translation, leveraging a **parameter-efficient fine-tuning** (PEFT) approach.

### Downstream Use [optional]
- Can be integrated into broader **multilingual** or **localization** workflows.


### Out-of-Scope Use
- Any uses that rely on languages other than **English or Faroese** will likely yield suboptimal results.
- Other tasks (e.g., summarization, classification) may be unsupported or require further fine-tuning.

---

## Bias, Risks, and Limitations
- **Biases:** The model could reflect **biases** present in the training data, such as historical or societal biases in English or Faroese texts.
- **Recommendation:** Users should **critically evaluate** outputs, especially in sensitive or high-stakes applications.

---

## How to Get Started with the Model

```python
import torch
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
import pandas as pd

ADAPTER_REPO = "barbaroo/gptsw3_translate_1.3B"
BASE_MODEL = "AI-Sweden-Models/gpt-sw3-1.3b"

# 1. Load the tokenizer from the base model
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

model = AutoPeftModelForCausalLM.from_pretrained(
    ADAPTER_REPO,
    load_in_8bit=True,             # Optional: 8-bit quantization for GPU memory efficiency
    device_map="auto",             # Automatically spread layers across available GPUs
)

# Ensure the model is in evaluation mode
model.eval()

# Alpaca-style prompt template
alpaca_prompt = """
### Instruction:
{}

### Input:
{}

### Response:
{}
"""

# EOS token from the tokenizer
EOS_TOKEN = tokenizer.eos_token 
print(EOS_TOKEN)

sentences =  ['hello world']

translations = []

for sentence in sentences:
    # Tokenize the input sentence and prepare the prompt for each sentence
    inputs = tokenizer(
        [
            alpaca_prompt.format(
                "Translate this sentence from English to Faroese:",  # instruction
                sentence,  # input sentence to translate
                "",  # output - leave blank for generation
            )
        ], 
        return_tensors="pt"
    ).to("cuda")

    # Generate the output
    outputs = model.generate(**inputs,
                             max_new_tokens=2000, 
                             eos_token_id=tokenizer.eos_token_id,  # Ensure EOS token is used
                             pad_token_id=tokenizer.pad_token_id,  # Ensure padding token is used
                             use_cache=True,
                             do_sample = True,
                             temperature = 0.1,
                             top_p=1)

    # Decode the generated tokens into a string
    output_string = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]
    #print(output_string)

    # Use a regular expression to extract the response part
    try:
        spl_word_1 = 'Response:\n'
        res =  output_string.split(spl_word_1, 1)
        response = res[1]
        translation = response.replace(EOS_TOKEN, '')
        translations.append(translation)

    except:
        translation = ''
        translations.append(translation)
        


    print(translation)
```


## Training Details

### Training Data

We used the Sprotin parallel corpus for **English–Faroese** translation: [barbaroo/Sprotin_parallel](https://huggingface.co/datasets/barbaroo/Sprotin_parallel). 


### Training Procedure

#### Preprocessing [optional]

- **Tokenization**: We used the tokenizer from the base model `AI-Sweden-Models/gpt-sw3-1.3b`.
- The Alpaca prompt format was used, with Instruction, Input and Response. 

#### Training Hyperparameters
 
- **Epochs**: **3** total, with an **early stopping** criterion monitoring validation loss.  
- **Batch Size**: **2, with 4 Gradient accumulation steps**  
- **Learning Rate**: **2e-4** 
- **Optimizer**: **AdamW** with a linear learning-rate scheduler and warm-up.

---

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

- The model was evaluated on the **[FLORES-200]** benchmark,  of ~1012 English–Faroese pairs.  


#### Metrics and Results

- **BLEU**: **[0.179]** 
- **chrF**: **[49.2]**
- **BERTScore f1**: **[0.947]**

Human evaluation was also performed (see paper)


## Citation []

[COMING SOON]

---
## Framework versions 

- PEFT 0.13.0