HiTZ/gl_Qwen3-8B-Base

This is a Galician (gl) language-specific base language model trained by the HiTZ Research Center, starting from Qwen3-8B-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).

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Training Data

To train language-specific base LLMs, we followed the methodology proposed by Etxaniz et al. (2024), 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.

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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).

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Getting Started

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "HiTZ/gl_Qwen3-8B-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).