Carvalho-Salamandra-Instruct

WARNING: This is a preliminary version of Carvalho-Salamandra-Instruct.

Table of Contents

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• Carvalho-Salamandra-Instruct
  • Table of Contents
  • Model description
  • Intended uses and limitations
  • How to use
  • Training
    • Tools
    • Training data
    • Training hyperparameters
    • Framework
  • Evaluation
  • Additional information
    • Funding
    • Cite this model

Model description

Carvalho-Salamandra-Instruct is a 7B-parameter instruction-tuned transformer model covering Galician, Portuguese, Spanish, English and Catalan.

It is based on BSC-LT/salamandra-7b-instruct and was further adapted through a 1-epoch training run using high-quality multilingual corpora, with a marked emphasis on Galician and Portuguese.

This model aims to provide strong instruction-following and generation capabilities for underrepresented languages while maintaining robust multilingual behavior.

Intended uses and limitations

Intended uses

• Instruction following and dialogue-style generation.
• Multilingual text generation and content creation.
• Downstream fine-tuning for tasks such as summarization, classification, or question answering (with appropriate supervised data).

Limitations

• Not intended as a sole source for high-stakes or safety-critical decisions.
• May produce incorrect or biased factual information — verify outputs when accuracy matters.
• Performance may vary by language and domain; best results in Galician and Portuguese given training emphasis.

How to use

from datetime import datetime
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model_id = "proxectonos/Carvalho-Salamandra-Instruct"

text = "Qué sabes sobre o Proxecto Nós?"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16
  )

message = [ { "role": "user", "content": text } ]
date_string = datetime.today().strftime('%Y-%m-%d')

prompt = tokenizer.apply_chat_template(
    message,
    tokenize=False,
    add_generation_prompt=True,
    date_string=date_string
)

inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=200)
generated_tokens = outputs[0][len(inputs[0]):]
response = self.tokenizer.decode(generated_tokens, skip_special_tokens=False).strip()
response = response.split("<|reserved_token_1|>")[0].strip()
print(response)

Training

Training data

The model was trained with a mix of instruction data and high-quality monolingual corpora, designed to maximize performance in Galician and Portuguese while preserving broad multilingual capabilities.

Dataset Type	Languages	Tokens per language/Source
Full instruction set	GL , ES , PT , CAT , EN	Galician Instruction Datasets
High-quality corpus	GL, PT	250M
Small HQ corpus	EN, ES, CAT	30M

Training hyperparameters

• epochs: 1
• dtype: bf16
• block size: 2048
• total batch size: 128
• learning rate: 2e-6
• scheduler: Linear
• optimizations:
  • gradient checkpointing: True
  • flash attention: True
  • liger kernels: True
  • DeepSpeed stage: 2

Framework

Training was performed at the Galician Supercomputing Center (CESGA) using 2 nodes (each with 2× NVIDIA A100 40GB) — a total of 4 GPUs — across 2 days.

Evaluation

Formal evaluation is ongoing. Preliminary internal tests show strong instruction-following ability and improved generation quality for Galician and Portuguese compared to the base model. Detailed benchmarks and quantitative results will be added when available.

Additional information

Funding

This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Desarrollo de Modelos ALIA

Cite this model

Please cite this model as:

@misc{carvalho_salamandra_instruct_2025,
    title = {Carvalho-Salamandra-Instruct: A Multilingual Instruction-Tuned Model for Underrepresented Languages},
    author = {Proxecto Nós Team},
    year = {2025},
    publisher = {HuggingFace},
    howpublished = {\url{https://huggingface.co/proxectonos/Carvalho-Salamandra-Instruct}},
}