---
library_name: transformers
pipeline_tag: text-generation
language:
- gl
license: apache-2.0
base_model:
- Qwen/Qwen3-14B-Base
---

# HiTZ/gl_Qwen3-14B-Base

This is a **Galician (gl) language-specific base language model** trained by the HiTZ Research Center, starting from **Qwen3-14B-Base** and further pretrained on curated Galician data.

This model is released as a **base model**, intended for further fine-tuning or adaptation (e.g., instruction tuning, domain adaptation).

---

## Training Data

To train language-specific base LLMs, we followed the methodology proposed by [Etxaniz et al. (2024)](https://aclanthology.org/2024.acl-long.799/), originally developed for Basque, and extended it to other low-resource languages. To enable fair comparisons across languages, we limited the corpus size for each language to roughly the same number of tokens. We also included a small English subset to mitigate catastrophic forgetting.

### Corpus composition

| Language | Documents | Tokens (Qwen3) |
|----------|-----------|---------------:|
| Galician (gl) | 8.9M | ~3.5B |
| English (en) | 0.5M | ~0.3B |

Token counts vary slightly depending on the tokenizer, but remain comparable in overall size.

### Data sources

Galician data was obtained from the CorpusNÓS corpus, which comprises large-scale web crawls and texts from public administrations, among other sources.  
The English subset was sampled from the FineWeb corpus.

---

## Model Training

- Sequence length: 8,196 tokens  
- Effective batch size: 256 sequences  
- Tokens per optimization step: ~2M  
- Learning rate schedule: cosine decay with 10% warm-up  
- Peak learning rate: 1e-5  

Training was conducted on the CINECA Leonardo high-performance computing cluster using Fully Sharded Data Parallel (FSDP) across 32 nodes, each equipped with 4 NVIDIA A100 GPUs (64 GB).

---

## Getting Started

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "HiTZ/gl_Qwen3-14B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

inputs = tokenizer("Ola!", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Acknowledgements

This work has been partially supported by the Basque Government (Research group funding IT1570-22 and IKER-GAITU project), the Spanish Ministry for Digital Transformation and of Civil Service, and the EU-funded NextGenerationEU Recovery, Transformation and Resilience Plan (ILENIA project, 2022/TL22/00215335; and ALIA project).