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BSC-LT/salamandra-7b-instruct
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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.
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python
from datetime import datetimefrom transformers import AutoTokenizer, AutoModelForCausalLMimport transformersimport 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)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 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.
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.
This model was developed within the Nós Project, funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project ILENIA with reference 2022/TL22/00215336. The updated version was 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
Please cite this model as:
text
@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}},}from datetime import datetimefrom transformers import AutoTokenizer, AutoModelForCausalLMimport transformersimport 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)@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}},}