Model details

• Developed by: Paula Guerrero and Iker Gutierrez
• Affiliation: University of the Basque Country (EHU)
• Model type: LoRA adapter for HiTZ/Latxa-Qwen3-VL-8B-Instruct
• Languages: Catalan (ca), Basque (eu)
• Domain: Literary translation
• Base model: HiTZ/Latxa-Qwen3-VL-8B-Instruct
• Repository: pguerrero-igutierrez/Latxa-Qwen3-8B-Literary-v1-ca-eu
• Collection: pguerrero-igutierrez/mt-domain-adaptation-ca-eu

Sources

• Hugging Face repository: https://huggingface.co/pguerrero-igutierrez/Latxa-Qwen3-8B-Literary-v1-ca-eu
• Hugging Face collection: https://huggingface.co/collections/pguerrero-igutierrez/mt-domain-adaptation-ca-eu
• Project repository: https://github.com/pguerrero-igutierrez/MT-domain-adaptation
• Paper source: https://github.com/pguerrero-igutierrez/MT-domain-adaptation/tree/main/paper

Intended use

This model is intended for literary translation research in the Catalan-Basque pair, especially when no direct in-domain parallel corpus is available.

Supported prompting directions:

• eu->ca: Itzuli testu hau euskaratik katalanera:\n\n{source}
• ca->eu: Tradueix aquest text del català al basc:\n\n{source}

Out-of-scope use

• Human publication without literary post-editing
• Translation outside the literary register
• High-stakes or professional workflows without review

Training data

The adapter was trained on two synthetic literary corpora:

• backtranslated-corpus/ca-literary_trilingual.json
• backtranslated-corpus/eu-literary-EhuHac.jsonl

The EU->CA direction uses synthetic Basque as source and original Catalan as target. The CA->EU direction uses synthetic Catalan as source and original Basque as target.

Training procedure

• LoRA rank: 16
• LoRA alpha: 32
• LoRA dropout: 0.05
• Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Quantization: 4-bit NF4
• Max sequence length: 768
• Epochs: 3
• Batch size: 4
• Gradient accumulation: 8
• Learning rate: 5e-5
• Scheduler: cosine
• Warmup ratio: 0.05
• Seed: 42
• Checkpoint selection: best validation BLEU

Evaluation

Results on the literary held-out test set:

In the project experiments, this direct literary SFT model slightly but consistently outperformed the continued-adaptation literary variant.

Limitations

• Uses synthetic supervision rather than human-translated in-domain CA-EU literary parallel data
• Literary quality is only partially reflected by overlap-based metrics
• CA->EU remains the harder literary direction in the reported experiments

Usage

python
Copy code
import torch
from peft import PeftModel
from transformers import AutoTokenizer, Qwen3VLForConditionalGeneration

base_id = "HiTZ/Latxa-Qwen3-VL-8B-Instruct"
adapter_id = "pguerrero-igutierrez/Latxa-Qwen3-8B-Literary-v1-ca-eu"

tokenizer = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
base_model = Qwen3VLForConditionalGeneration.from_pretrained(
    base_id,
    device_map="auto",
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base_model, adapter_id)

prompt = "Tradueix aquest text del català al basc:\n\nLa nit era tranquil·la."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation

bibtex
Copy code
@misc{guerrero-gutierrez-2026-caeu-mt,
  title        = {Domain Adaptation for Catalan-Basque Machine Translation via Synthetic Data and Continued Fine-Tuning},
  author       = {Guerrero, Paula and Gutierrez, Iker},
  year         = {2026},
  note         = {Unpublished manuscript}
}

Contact

• Paula Guerrero: pguerrero005@ikasle.ehu.eus
• Iker Gutierrez: igutierrez134@ikasle.ehu.eus