[!WARNING]
This quantized model is not up to date with the latest release of the SalamandraTA model family. Please refer to SalamandraTA-7b-instruct for the most recent version with extended language coverage and improved translation performance. We plan to update this model soon. Stay tuned!
The model weights are quantized from FP16 to Q4_K_M quantization Q8_0 (8-bit quantization), (4-bit weights with K-means clustering quantization) and Q3_K_M (3-but weights with K-means clustering quantization) using the Llama.cpp framework.
Inferencing with this model can be done using VLLM.
SalamandraTA-2b-instruct is a translation LLM that has been instruction-tuned from SalamandraTA-2b-base.
The base model results from continually pre-training Salamandra-2b on parallel data and has not been published,
but is reserved for internal use.
SalamandraTA-2b-instruct is proficient in 35 European languages (plus 3 varieties) and supports translation-related tasks,
namely: sentence-level-translation, paragraph-level-translation, automatic post-editing, grammar checking, machine translation evaluation,
alternative translations, named-entity-recognition and context-aware translation.
[!IMPORTANT]
DISCLAIMER: This version of Salamandra is tailored exclusively for translation tasks. It lacks chat capabilities and has not been trained with any chat instructions.
The entire Salamandra family is released under a permissive Apache 2.0 license.
How to Use
The following example code works under Python 3.10.4, vllm==0.7.3, torch==2.5.1 and torchvision==0.20.1, though it should run on
any current version of the libraries. This is an example of translation using the model:
from huggingface_hub import snapshot_download
from vllm import LLM, SamplingParams
model_dir = snapshot_download(repo_id="BSC-LT/salamandraTA-2B-instruct-GGUF", revision="main")
model_name = "salamandrata_2b_inst_q4.gguf"
llm = LLM(model=model_dir + '/' + model_name, tokenizer=model_dir)
source = "Spanish"
target = "English"
sentence = "Ayer se fue, tomó sus cosas y se puso a navegar. Una camisa, un pantalón vaquero y una canción, dónde irá, dónde irá. Se despidió, y decidió batirse en duelo con el mar. Y recorrer el mundo en su velero. Y navegar, nai-na-na, navegar."
prompt = f"Translate the following text from {source} into {target}.\\n{source}: {sentence} \\n{target}:"
messages = [{'role': 'user', 'content': prompt}]
outputs = llm.chat(messages,
sampling_params=SamplingParams(
temperature=0.1,
stop_token_ids=[5],
max_tokens=200)
)[0].outputs
print(outputs[0].text)
Additional information
Author
The Language Technologies Unit from Barcelona Supercomputing Center.
Contact
For further information, please send an email to langtech@bsc.es.
Copyright
Copyright(c) 2025 by Language Technologies Unit, Barcelona Supercomputing Center.
Funding
This work has been promoted and financed by the Government of Catalonia through the Aina Project.
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU
within the framework of ILENIA Project with reference 2022/TL22/00215337.
Acknowledgements
The success of this project has been made possible thanks to the invaluable contributions of our partners in the ILENIA Project:
HiTZ, and CiTIUS.
Their efforts have been instrumental in advancing our work, and we sincerely appreciate their help and support.
Disclaimer
Disclaimer
Be aware that the model may contain biases or other unintended distortions.
When third parties deploy systems or provide services based on this model, or use the model themselves,
they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations,
including those governing the use of Artificial Intelligence.
The Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use.
