As with all translation models, may reflect biases from the training corpora.
Outputs should be carefully validated for sensitive or high-stakes domains.

How to Get Started with the Model

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import re
import pandas as pd

# Model repo
MODEL_NAME = "barbaroo/gptsw3-6.7B-translation-en-fo"

# Quantization config (8-bit)
bnb_config = BitsAndBytesConfig(
    load_in_8bit=True
)

# Initialize tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    quantization_config=bnb_config,
    device_map="auto",
)
model.eval()

# Alpaca-style prompt template
alpaca_prompt = """

### Instruction:
{}

### Input:
{}

### Response:
{}"""

EOS_TOKEN = tokenizer.eos_token
print("EOS token:", EOS_TOKEN)

# Example sentences
sentences = ["I love Faroese!"]
translations = []

for sentence in sentences:
    inputs = tokenizer(
        [
            alpaca_prompt.format(
                "Translate this sentence from English to Faroese:",
                sentence,
                "",
            )
        ],
        return_tensors="pt"
    ).to("cuda")

    outputs = model.generate(
        **inputs,
        max_new_tokens=500,
        use_cache=True,
        do_sample=True,
        temperature=0.1,
        top_p=1,
    )

    output_string = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]

    try:
        response = output_string.split("Response:\n", 1)[1]
        translation = response.replace(EOS_TOKEN, "")
    except IndexError:
        translation = ""

    translations.append(translation)
    print(translation)

Training Details

Training Data

Procedure

Initially trained as a PEFT adapter using Alpaca-style prompts.
Then merged with the base GPT-SW3 6.7B model to produce this standalone version.

Hyperparameters:

Epochs: 3 (early stopping on validation loss)
Batch Size: 2 (with 4 gradient accumulation steps)
Learning Rate: 2e-4
Optimizer: AdamW with LR scheduler + warm-up

Evaluation

Test Data

FLORES-200 benchmark (~1012 English–Faroese pairs).

Metrics

BLEU: 19.8
chrF: 52.4