commit f9758b8877588847b1e4b2ba07fe32a89a841076
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Thu Jun 11 00:12:17 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: gplsi/Aitana-2B-SI-Instruct-Aligned
    Source: Original Platform

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diff --git a/README.md b/README.md
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+---
+license: apache-2.0
+language:
+  - ca
+  - es
+  - en
+base_model: gplsi/Aitana-2B-SI-Instruct
+tags:
+  - valencian
+  - spanish
+  - english
+  - text-generation
+  - instruct
+  - dpo
+  - alignment
+  - alia
+  - gplsi
+datasets:
+  - nvidia/HelpSteer3
+  - OpenAssistant/oasst1
+  - OpenAssistant/oasst2
+  - Open-Orca/OpenOrca
+library_name: transformers
+pipeline_tag: text-generation
+---
+# Aitana-2B-SI-Instruct-Aligned
+
+**Aitana-2B-SI-Instruct-Aligned** is a DPO-aligned instruction-tuned generative language model from the **Aitana family**, developed by the [GPLSI (Language and Information Systems Group)](https://gplsi.dlsi.ua.es/) at the University of Alicante. Built on [gplsi/Aitana-2B-SI-Instruct](https://huggingface.co/gplsi/Aitana-2B-SI-Instruct), this model has been further aligned using Direct Preference Optimization (DPO) to improve response quality and alignment with human preferences across Valencian, Spanish, and English.
+
+## Table of Contents
+
+- [Model Description](#model-description)
+- [Alignment Details](#alignment-details)
+- [Training Data](#training-data)
+- [Intended Uses](#intended-uses)
+- [How to Use](#how-to-use)
+- [Evaluation](#evaluation)
+- [Additional Information](#additional-information)
+
+## Model Description
+
+| Property | Value |
+|----------|-------|
+| **Base Model** | [gplsi/Aitana-2B-SI-Instruct](https://huggingface.co/gplsi/Aitana-2B-S-Instruct) |
+| **Architecture** | Transformer decoder-only |
+| **Parameters** | ~2.25B |
+| **Languages** | Valencian, Spanish, English |
+| **License** | Apache 2.0 |
+
+Aitana-2B-SI-Instruct-Aligned extends the Aitana-2B-SI-Instruct instruction-tuned model with Direct Preference Optimization (DPO) alignment. This additional training stage improves the model's ability to generate helpful, high-quality responses that better align with human preferences while maintaining its strong multilingual capabilities.
+
+## Alignment Details
+
+The model was aligned using Direct Preference Optimization (DPO) with the following configuration:
+
+| Hyperparameter | Value |
+|----------------|-------|
+| **Method** | DPO (Direct Preference Optimization) |
+| **Learning rate** | 5e-6 |
+| **Epochs** | 1 |
+| **Beta** | 0.1 |
+| **LR Scheduler** | Linear |
+| **Total Samples** | 146,180 |
+| **English Samples** | 80,308 |
+| **Spanish Samples** | 30,072 |
+| **Valencian Samples** | 35,800 |
+| **Languages** | Spanish, Valencian, English |
+
+The DPO alignment was performed using curated preference pairs that teach the model to prefer more helpful, accurate, and well-structured responses.
+
+## Training Data
+
+The base instruction model was trained on the ALIA Instruction/v12 dataset. This DPO-aligned variant was further aligned using the Alignment/v8 dataset, composed of the following preference data:
+
+| Dataset ID | Name | Languages | Source |
+|------------|------|-----------|--------|
+| al1 | HelpSteer3 | EN, ES | [nvidia/HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3) |
+| al2 | OpenAssistant1 (OASST1) | EN, ES, RU (+32 more) | [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) |
+| al3 | OpenAssistant2 (OASST2) | EN, ES, RU (+32 more) | [OpenAssistant/oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2) |
+| al4 | OpenOrca | EN | [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) |
+| al5 | OASST2 Valenciano | VA | — |
+
+The alignment data focused on English, Spanish, and Valencian preference pairs, with the distribution: 80,308 English, 30,072 Spanish, and 35,800 Valencian samples.
+
+## Intended Uses
+
+This model can be used for:
+
+- **Instruction following** in Valencian, Spanish, and English with improved alignment to human preferences
+- **Chat and conversational applications** requiring high-quality multilingual responses
+- **Text generation** with task-specific prompting and improved output quality
+- **Domain-specific applications** in administrative, legal, or tourism contexts
+
+> **Note**: As an aligned instruction-tuned model, it is designed to follow user prompts and generate helpful, safe responses. It is not intended for use as a factual knowledge base. The DPO alignment improves response quality and preference alignment.
+
+## How to Use
+
+### Transformers
+
+```python
+import torch
+from transformers import pipeline, AutoTokenizer
+
+model_id = "gplsi/Aitana-2B-SI-Instruct-Aligned"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+generator = pipeline(
+    "text-generation",
+    model=model_id,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+# Valencian example
+text = "Explica què són les Corts Valencianes i quina funció tenen."
+result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
+print(result[0]['generated_text'])
+# Spanish example
+text = "Describe las principales funciones del gobierno autonómico valenciano."
+result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
+print(result[0]['generated_text'])
+# English example
+text = "Explain the role of tourism in the Valencian Community economy."
+result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
+print(result[0]['generated_text'])
+```
+
+## Evaluation
+
+In the following tables, we present the results obtained with different benchmarks from [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) in comparison with [Salamandra-2B-Instruct](https://huggingface.co/BSC-LT/Salamandra-2B-Instruct), and [Aitana-2B-S-Instruct-Aligned](https://huggingface.co/gplsi/Aitana-2B-S-Instruct-Aligned). The results reflect the DPO-aligned instruction-tuned performance.
+
+### Valencian
+
+#### Classification Benchmarks
+
+| Dataset                      |  Lang. |          Task              | Metric      | Salamandra-2B-Instruct |  Aitana-2B-S-Instruct-Aligned (v0.1) |  Aitana-2B-SI-Instruct-Aligned (v0.1) |
+|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|-----------------------|
+| XNLI                         |   va   |Natural Language Inference  |     acc     |   **0.520**   |         0.514         |         0.485         |         
+
+#### Generation Benchmarks
+
+| Dataset                      |  Lang. |          Task              | Metric      | Salamandra-2B-Instruct |  Aitana-2B-S-Instruct-Aligned (v0.1) |  Aitana-2B-SI-Instruct-Aligned (v0.1) |
+|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|-----------------------|
+| Cocoteros                    |   va   |Reading Comprehension       |     bleu    |   2.796       |           3.612       |         **4.223**     |
+| Phrases ca-va                |  va-ca |Translation - Adaptation    |     bleu    |   58.425      |         **74.538**    |         68.305        |     
+| Phrases va-ca                |  va-ca |Translation - Adaptation    |     bleu    |   70.660      |         **71.691**    |         69.551        |
+| Phrases va-es                |  va-es |Translation                 |     bleu    |   65.427      |         **72.097**    |         70.061        |
+| Phrases es-va                |  es-va |Translation                 |     bleu    |   45.688      |         **56.012**    |         54.053        |
+| Truthfulqa_va                |  va |  Truthfulness                 |     bleu_acc    |   **0.409**   |         0.394     |         0.383         |
+
+### Catalan
+
+#### Classification Benchmarks
+
+| Dataset                      |  Lang. |          Task             | Metric      | Salamandra-2B-Instruct | Aitana-2B-S-Instruct-Aligned (v0.1) |  Aitana-2B-SI-Instruct-Aligned (v0.1) |
+|------------------------------|--------|---------------------------|-------------|---------------|-----------------------|-----------------------|
+| Belebele Cat_latn            |   ca   | Reading Comprehension     |     acc     |   0.287       |       0.248     |       **0.319** |       
+| COPA                         |   ca   | Commonsense Reasoning     |     acc     |   0.708       |       **0.726** |       0.694     | 
+| XStoryCloze                  |   ca   | Commonsense Reasoning     |     acc     |   0.616       |       **0.629** |       0.623     |
+| OpenBookQA                   |   ca   | Question Answering        |     acc     |   0.296       |         0.296   |       **0.326** |
+| PAWS                         |   ca   | Paraphrasing              |     acc     |   **0.602**   |       0.598     |       0.531     |
+| PiQA                         |   ca   | Question Answering        |     acc     |   0.638       |       **0.655** |       0.629     |
+| ARC Easy                     |   ca   | Question Answering        |     acc     |   0.516       |       0.524     |       **0.526** |
+| ARC Challenge                |   ca   | Question Answering        |     acc     |   0.298       |       **0.314** |       0.310     |
+| XNLI                         |   ca   | Natural Language Inference|     acc     |   0.513       |       **0.515** |       0.497     |
+| Teca                         |   ca   | Natural Language Inference|     acc     |   0.486       |       **0.500** |       0.468     |
+| WNLI                         |   ca   | Natural Language Inference|     acc     |   **0.563**   |       0.437     |      0.436      |
+| Catcola                      |   ca   | Linguistic Acceptability  |     acc     |   0.492       |       **0.713** |      0.680      |
+| Catcola                      |   ca   | Linguistic Acceptability  |     mcc     |   **0.097**   |       -0.040    |      0.013      |
+| Catalanqa                    |   ca   | Question Answering        |      F1     |   **0.516**   |       0.384     |      0.396      |
+| Mgsm direct                  |   ca   | Math                      | exact match |   0.000       |       **0.012** |      0.004      |
+| Catalanqa                    |   ca   | Question Answering        | exact match |   **0.182**   |       0.011     |      0.031      |
+| Xquad                        |   ca   | Question Answering        | exact match |   **0.103**   |       0.014     |      0.037      |
+| Xquad                        |   ca   | Question Answering        |      F1     |   **0.394**   |       0.287     |      0.317      |
+
+#### Generation Benchmarks
+
+| Dataset                      |  Lang. |          Task            | Metric |  Salamandra-2B-Instruct |  Aitana-2B-S-Instruct-Aligned (v0.1) |  Aitana-2B-SI-Instruct-Aligned (v0.1) |
+|------------------------------|--------|--------------------------|--------|----------------|-----------------------|-----------------------|
+| Cabreu abstractive           |  ca    | Summarization            |  bleu  |    7.610       |         7.703         |         **8.837**     |
+| Cabreu extractive            |  ca    | Summarization            |  bleu  |    **38.002**  |         19.876        |         28.16803      |
+| Cabreu extreme               |  ca    | Summarization            |  bleu  |    2.733       |         3.245         |         **3.386**     |
+
+### Spanish
+
+#### Classification Benchmarks
+
+| Dataset                      |  Lang. |          Task             |    Metric   | Salamandra-2B-Instruct |  Aitana-2B-S-Instruct-Aligned (v0.1) |  Aitana-2B-SI-Instruct-Aligned (v0.1) |
+|------------------------------|--------|---------------------------|-------------|---------------|-----------------------|-----------------------|
+| Belebele                     |   es   | Reading Comprehension     |     acc     | 0.268 | 0.244 | **0.285** |
+| PAWS                         |   es   | Paraphrasing              |     acc     | 0.566 | **0.618** | 0.546 |
+| XNLI                         |   es   | Natural Language Inference|     acc     | **0.463** | 0.439 | 0.443 |
+| WNLI                         |   es   | Natural Language Inference|     acc     | 0.479 | **0.535** | **0.535** |
+| XStoryCloze                  |   es   | Commonsense Reasoning     |     acc     | 0.617 | 0.628 | **0.632** |
+| Escola                       |   es   | Linguistic Acceptability  |     acc     | 0.293 | **0.708** | 0.654 |
+| Escola                       |   es   | Linguistic Acceptability  |     mcc     | 0.020 | 0.000 | **0.046** |
+| OpenbookQA                   |   es   | Question Answering        |     acc     | 0.286 | **0.338** | 0.332 |
+| MGSM Direct                  |   es   | Math                      | exact match | 0.020 | 0.024 | **0.1** |
+| XQUAD                        |   es   | Question Answering        | exact match | **0.066** | 0.026 | 0.019 |
+| XQUAD                        |   es   | Question Answering        |      F1     | **0.355** | 0.293 | 0.293 |
+
+#### Generation Benchmarks
+
+| Dataset                      |  Lang. |      Task           | Metric  |  Salamandra-2B-Instruct |  Aitana-2B-S-Instruct-Aligned (v0.1) |  Aitana-2B-SI-Instruct-Aligned (v0.1) |
+|------------------------------|--------|---------------------|---------|----------------|-----------------------|-----------------------|
+| Cocoteros                    |   es   |Reading Comprehension|  bleu   | 3.308 | 3.141 | **3.670** |
+| XLSum                        |   es   | Summarization       |  bleu   | 1.695 | 1.737 | **1.971** |
+
+### English
+
+#### Classification Benchmarks
+
+| Dataset                      |  Lang. |          Task              | Metric      | Salamandra-2B-Instruct |  Aitana-2B-S-Instruct-Aligned (v0.1) |  Aitana-2B-SI-Instruct-Aligned (v0.1) |
+|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|-----------------------|
+| Arc Challenge                |   en   | Question Answering         |     acc     | 0.354 | 0.363 | **0.372** |
+| Arc Easy                     |   en   | Question Answering         |     acc     | 0.681 | **0.709** | 0.682 |
+| Belebele                     |   en   |   Reading Comprehension    |     acc     | 0.260 | 0.293 | **0.349** |
+| PAWS                         |   en   |      Paraphrasing          |     acc     | **0.597** | 0.594 | 0.555 |
+| XNLI                         |   en   | Natural Language Inference |     acc     | 0.512 | **0.553** | 0.480 |
+| XStoryCloze                  |   en   |   Commonsense Reasoning    |     acc     | 0.662 | 0.680 | **0.693** |
+| OpenBookQA                   |   en   |    Question Answering      |     acc     | 0.298 | **0.338** | 0.316 |
+| PiQA                         |   en   |    Question Answering      |     acc     | 0.715 | **0.717** | 0.704 |
+| Social iqa                   |   en   |    Question Answering      |     acc     | 0.453 | 0.451 | **0.468** |
+| WNLI                         |   en   | Natural Language Inference |     acc     | **0.535** | 0.465 | 0.451 |
+| MGSM Direct                  |   en   |           Math             | exact match | 0.008 | 0.052 | **0.116** |
+| TriviaQA                     |   en   |     Question Answering     | exact match | 0.076 | 0.147 | **0.156** |
+
+### Judge Evaluation
+
+The model was also evaluated using an LLM-as-judge approach across different task categories. The scores below represent the average rating (1-5 scale, 5 being best) and standard deviation for each task category, comparing Aitana-2B-SI-Instruct-Aligned against Salamandra-2B-Instruct and Aitana-2B-S-Instruct-Aligned.
+
+| Task Category | Salamandra-2B-Instruct | Aitana-2B-S-Instruct-Aligned (v0.1) |  Aitana-2B-SI-Instruct-Aligned (v0.1) |
+|---------------|------------------------|-----------------------------------|-----------------------|
+| CommonSense reasoning | 2.277 / 1.151 | 2.737 / 1.140 | **2.969 / 1.086** |
+| Maths | 1.060 / 0.124 | 1.123 / 0.249 | **1.191 / 0.349** |
+| Paraphrasing | **3.518 / 1.308** | 3.460 / 1.088 | 3.472 / 0.959 |
+| Reading comprehension | **2.966 / 1.111** | 2.894 / 1.311 | **3.112 / 1.146** |
+| Summarization | 2.217 / 1.068 | 2.261 / 0.820 | **2.591 / 1.115** |
+| Translation | **3.557 / 0.760** | 3.418 / 0.999 | 3.390 / 0.730 |
+| **Overall Avg** | 2.599 / 0.920 | 2.649 / 0.935 | **2.787 / 0.897** |
+
+The DPO-aligned model shows a notable improvement in overall average score (2.787)  compared to Aitana-2B-S-Instruct-Aligned (v0.1) (2.649) and  Salamandra-2B-Instruct (2.599) with particular gains in CommonSense reasoning, reading comprehension and summarization. The aligned model also shows tighter standard deviations in several categories, indicating more consistent quality responses.
+
+## Additional Information
+
+### Author
+
+The model has been developed by the **Language and [Information Systems Group (GPLSI)](https://gplsi.dlsi.ua.es/)** and the **[Centro de Inteligencia Digital (CENID)](https://cenid.es)**, both part of the **[University of Alicante (UA)](https://www.ua.es/es/)**, as part of their ongoing research in **Natural Language Processing (NLP)**.
+
+
+### Funding
+
+This work is funded by the **Ministerio para la Transformación Digital y de la Función Pública**, co-financed by the **EU – NextGenerationEU**, within the framework of the project *Desarrollo de Modelos ALIA*. This work has also been partially supported by Project HEART-NLP (PID2024-156263OB-C22).
+
+### Acknowledgments
+
+We would like to express our gratitude to all individuals and institutions that have contributed to the development of this work.
+Special thanks to:
+- [Language Technologies Laboratory at Barcelona Supercomputing Center](https://www.bsc.es/es/discover-bsc/organisation/research-structure/language-technologies-laboratory)
+- [Centro Vasco de Tecnología de la Lengua (HiTZ)](https://www.hitz.eus/es)
+- [Centro Singular de Investigación en Tecnologías Inteligentes (CiTIUS)](https://citius.gal/)
+- [Sistemas Inteligentes de Acceso a la Información (SINAI)](https://www.ujaen.es/investigacion-y-transferencia/grupos-de-investigacion/sistemas-inteligentes-de-acceso-la-informacion-sinai)
+- [Instituto Universitario de Investigación Informática (IUII)](https://web.ua.es/es/iuii/)
+- [Leonardo HPC System](https://leonardo-supercomputer.cineca.eu/)
+- [European supercomputing ecosystem (EUROHPC)](https://www.eurohpc-ju.europa.eu/)
+
+We also acknowledge the financial, technical, and scientific support of 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**, whose contribution has been essential to the completion of this research.
+
+### License
+
+[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+### Disclaimer
+
+This model is intended for general purposes and is available under a permissive Apache License 2.0. Be aware that the model may have biases and/or undesirable outputs. Users deploying systems based on this model are responsible for mitigating risks and complying with applicable AI regulations.
+
+### Reference
+
+```bibtex
+@misc{gplsi-aitana-2B-SI-Instruct-Aligned,
+  author       = {Galiano, Santiago and Sepúlveda-Torres, Robiert and Martínez-Murillo, Iván and Estevanell-Valladares, Ernesto L. and Grande, Eduardo and Consuegra-Ayala, Juan Pablo and Miró Maestre, María and Canal-Esteve, Miquel and Bonora, Mar and Gutierrez, Yoan and Abreu Salas, José Ignacio and Lloret, Elena and Montoyo, Andrés and Muñoz-Guillena, Rafael and Palomar, Manuel},
+  title        = {Aitana-2B-SI-Instruct-Aligned: DPO-aligned instruction-tuned model for Valencian, Spanish and English},
+  year         = {2026},
+  institution  = {Language and Information Systems Group (GPLSI) and Centro de Inteligencia Digital (CENID), University of Alicante (UA)},
+  howpublished = {\url{https://huggingface.co/gplsi/Aitana-2B-SI-Instruct-Aligned}},
+  note         = {Accessed: 2026-05-11}
+}
+```
+
+---
+
+**Copyright © 2026 Language and Information Systems Group (GPLSI) and Centro de Inteligencia Digital (CENID), University of Alicante (UA). Distributed under the Apache License 2.0.**
diff --git a/chat_template.jinja b/chat_template.jinja
new file mode 100644
index 0000000..f69fbc7
--- /dev/null
+++ b/chat_template.jinja
@@ -0,0 +1,9 @@
+{%- if not date_string is defined %}{%- set date_string = "2025-12-09" %}{%- endif %}{%- set base_system_message = "I am Aitana, experimental model developed at the University of Alicante by the Language and Information Systems Group (GPLSI). My knowledge base was last updated on November 2025. Always respond in a friendly manner and following the user's instructions. Today Date: "+ date_string +"
+Soy Aitana, un modelo experimental desarrollado en la Universidad de Alicante por el Grupo de Procesamiento del Lenguaje y Sistemas de Información (GPLSI). Mi base de conocimiento se actualizó por última vez en noviembre de 2025. Responde siempre de manera amigable y siguiendo las indicaciones del usuario.
+Soc Aitana, un model experimental desenvolupat a la Universitat d'Alacant pel Grup de Processament del Llenguatge i Sistemes d'Informació (GPLSI). La meua base de coneixement es va actualitzar per última vegada en novembre del 2025. Respon sempre de manera amigable i seguint les indicacions de l'usuari." -%}{%- if messages and messages[0].role == "system" -%}{%- set task_system = messages[0].content -%}{%- set messages = messages[1:] -%}{%- else -%}{%- set task_system = "" -%}{%- endif -%}{%- if task_system -%}{%- set system_message = base_system_message + "
+" + task_system -%}{%- else -%}{%- set system_message = base_system_message -%}{%- endif -%}{{ "<|im_start|>system
+" + system_message + "<|im_end|>
+" }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..21eea52
--- /dev/null
+++ b/config.json
@@ -0,0 +1,30 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
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+}