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Model: SINAI/ALIA-es-legal-administrative-7B-Instruct Source: Original Platform
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---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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language:
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- es
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base_model:
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- SINAI/ALIA-es-legal-administrative-7B-Base
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datasets:
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- SINAI/ALIA-es-legal-administrative-synthetic-instructions
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tags:
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- legal
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- administrative
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- spanish
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---
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# ALIA Spanish Legal and Administrative 7B Instruct Model
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## Model Description
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**ALIA Spanish Legal and Administrative 7B Instruct Model** is an instruction-tuned language model specialized in the legal and administrative domain for Spanish. This model is derived from [SINAI/ALIA-es-legal-administrative-7B-Base](https://huggingface.co/SINAI/ALIA-es-legal-administrative-7B-Base), which itself is based on the [Salamandra-7B](https://huggingface.co/BSC-LT/salamandra-7b) model family.
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The model has been instruction-tuned using the [ALIA-es-legal-administrative-synthetic-instructions](https://huggingface.co/datasets/SINAI/ALIA-es-legal-administrative-synthetic-instructions) dataset, enabling it to assist users with legal and administrative queries in Spanish.
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> [!WARNING]
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> **DISCLAIMER:** This model is provided for research and educational purposes. It should not be used as a substitute for professional legal advice. Users are responsible for ensuring their use of the model complies with applicable laws and regulations.
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> As a result, it may generate harmful or inappropriate content, or legally inaccurate information. Users should verify any legal information generated against official sources.
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> The SINAI Research Group and Barcelona Supercomputing Center shall not be held liable for any outcomes resulting from the use of this model.
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### Model Lineage
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```
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Salamandra-7B (BSC-LT)
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↓
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ALIA-es-legal-administrative-7B-Base (SINAI)
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↓
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ALIA-es-legal-administrative-7B-Instruct (SINAI) ← This model
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```
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### Key Features
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- **Specialized Domain**: Legal and administrative Spanish language
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- **Instruction-Following**: Fine-tuned to respond to user queries and instructions
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- **Foundation**: Built upon Salamandra-7B's multilingual capabilities, focused on Spanish
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- **Open License**: Released under Apache 2.0 license
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---
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## Model Details
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### Architecture
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This model maintains the same architecture as its base model [ALIA-es-legal-administrative-7B-Base](https://huggingface.co/SINAI/ALIA-es-legal-administrative-7B-Base), which is derived from Salamandra-7B:
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| | |
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|------------------------------|:--------------|
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| **Total Parameters** | 7,768,117,248 |
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| **Embedding Parameters** | 1,048,576,000 |
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| **Layers** | 32 |
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| **Hidden size** | 4,096 |
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| **Attention heads** | 32 |
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| **Context length** | 8,192 |
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| **Vocabulary size** | 256,000 |
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| **Precision** | bfloat16 |
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| **Embedding type** | RoPE |
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| **Activation Function** | SwiGLU |
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| **Layer normalization** | RMS Norm |
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| **Flash attention** | ✅ |
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| **Grouped Query Attention** | ✅ |
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| **Num. query groups** | 8 |
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### Training Details
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**Instruction Tuning:**
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- Training was conducted by Barcelona Supercomputing Center (BSC)
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- Dataset: [ALIA-es-legal-administrative-synthetic-instructions](https://huggingface.co/datasets/SINAI/ALIA-es-legal-administrative-synthetic-instructions)
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- Language: Spanish
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- Domain: Legal and administrative
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- Number of samples: 7,411,809
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**Training Infrastructure:**
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Model trained on [MareNostrum 5](https://www.bsc.es/ca/marenostrum/marenostrum-5), a pre-exascale EuroHPC supercomputer hosted and operated by Barcelona Supercomputing Center.
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The accelerated partition is composed of 1,120 nodes with the following specifications:
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- 4x Nvidia Hopper GPUs with 64GB HBM2 memory
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- 2x Intel Sapphire Rapids 8460Y+ at 2.3GHz and 32c each (64 cores)
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- 4x NDR200 (BW per node 800Gb/s)
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- 512 GB of Main memory (DDR5)
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- 460GB of NVMe storage
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The table below specifies the number of nodes and GPUs employed for the supervised fine-tuning:
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| Phase | Nodes | GPUs | Training Time |
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|-------|-------|------|---------------|
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| SFT | 16 | 64 | 17h 1m 42s |
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**Training Hyperparameters:**
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Supervised Fine-Tuning was conducted using an internal fork of the FastChat codebase, adapted to BSC's infrastructure and optimized for stability and efficiency.
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| Hyperparameter | Value |
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|----------------|-------|
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| Learning rate | 1e-5 |
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| Batch size | 1024 |
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| Epochs | 1 |
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| LR Scheduler | Cosine |
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| Warmup Ratio | 0.03 |
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| NEFTune Noise Alpha | 5 |
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|
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---
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## Intended Use
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### Direct Use
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This model is designed to assist users with questions and tasks related to legal and administrative matters in Spanish. It can be used for:
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- Answering legal and administrative queries
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- Providing information about legal procedures
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- Assisting with understanding administrative documentation
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- General legal consultation and guidance
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---
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## How to Use
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### Inference
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#### Basic Usage with Transformers
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```bash
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pip install transformers torch accelerate sentencepiece protobuf
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```
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "SINAI/ALIA-es-legal-administrative-7B-Instruct"
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# Load the tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype=torch.bfloat16
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)
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# Example legal query
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messages = [
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{"role": "user", "content": "¿Cuál es el plazo para presentar un recurso de alzada?"}
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]
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# Format the input
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input_ids = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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# Generate response
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outputs = model.generate(
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input_ids,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.95,
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do_sample=True
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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#### Inference with vLLM
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```bash
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pip install vllm
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```
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```python
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from vllm import LLM, SamplingParams
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model_id = "SINAI/ALIA-es-legal-administrative-7B-Instruct"
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# Create sampling parameters
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sampling_params = SamplingParams(
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temperature=0.7,
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top_p=0.95,
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max_tokens=512
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)
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# Initialize the model
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llm = LLM(model=model_id)
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# Example prompt
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prompt = "¿Cuáles son los requisitos para solicitar una licencia de actividad?"
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# Generate response
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outputs = llm.generate([prompt], sampling_params)
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for output in outputs:
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print(f"Generated text: {output.outputs[0].text}")
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```
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---
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## Training Data
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The model was instruction-tuned using the [ALIA-es-legal-administrative-synthetic-instructions](https://huggingface.co/datasets/SINAI/ALIA-es-legal-administrative-synthetic-instructions) dataset created by SINAI research group.
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This dataset contains synthetic instructions specifically designed for the legal and administrative domain in Spanish, enabling the model to understand and respond to domain-specific queries.
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---
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## Evaluation
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The model was evaluated using the [Galtea](https://docs.galtea.ai/) evaluation platform, which employs LLM-as-judge methodology to assess model performance across various dimensions of quality and accuracy.
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### Evaluation Methodology
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**Framework**: [Galtea Platform](https://platform.galtea.ai/)
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**LLM Judges**: The evaluation uses multiple state-of-the-art language models as judges to assess response quality:
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- Gemini 2.5 Flash
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- GPT-5.2
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- Claude Sonnet 4.5
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**Metrics**: The model was evaluated using Galtea's custom metrics, specifically:
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- **Answer Relevancy**: Measures how relevant and appropriate the model's responses are to the input questions
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- **Factual Accuracy**: Assesses the correctness and truthfulness of the information provided in responses
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### Evaluation Datasets
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#### Justicio RAG QA Dataset
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- **Source**: [dariolopez/justicio-rag-embedding-qa](https://huggingface.co/datasets/dariolopez/justicio-rag-embedding-qa)
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- **Description**: A Spanish legal domain question-answering dataset containing 815 instances designed for RAG (Retrieval-Augmented Generation) evaluation
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- **Domain**: Legal and administrative Spanish content
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#### Oposiciones Dataset
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- **Description**: A collection of questions from Spanish public examination tests (oposiciones)
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- **Domain**: Administrative and legal knowledge assessment for public sector positions
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### Performance Metrics
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The following table shows evaluation results comparing **ALIA Spanish Legal and Administrative 7B Instruct** against the baseline **Salamandra 7B Instruct** model:
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| Metric | ALIA Spanish Legal and Administrative 7B Instruct | Salamandra 7B Instruct |
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|-------|:------------:|:---------------:|
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| Answer Relevancy | **93.5** | 90.6 |
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| Factual Accuracy | **29.3** | 19.3 |
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| Justicio LLM Judging | **0.610273** | 0.514706 |
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| Oposiciones LLM Judging | **0.340541** | 0.268284 |
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**Note**: The model actually outperforms Salamandra 7B Instruct in all metrics, reflecting the improved precision and correctness concerning legal domain questions.
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#### Overall Performance Summary
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- **Average Input Tokens**: 114
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- **Average Output Tokens**: 85.9
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- **Overall Average Score**: 51%
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---
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## Limitations and Biases
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### Known Limitations
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- **Domain Specificity**: While specialized in legal and administrative Spanish, the model may not perform optimally on general-purpose tasks
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- **Language**: Optimized for Spanish only
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- **Not Legal Advice**: Outputs should not be considered as professional legal advice
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- **Training Data Constraints**: Performance is limited by the scope and quality of the training data
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- **Potential Hallucinations**: Like all language models, may generate plausible-sounding but incorrect information
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### Bias Considerations
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- The model inherits potential biases from its base model (Salamandra-7B) and training data
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- Legal and administrative language may reflect existing biases in legal systems and documentation
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- Users should be aware of potential biases when using the model for sensitive applications
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- We recommend additional bias testing and mitigation for specific use cases
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### Safety and Responsible Use
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- **Human Oversight**: Always verify model outputs, especially for critical legal matters
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- **Professional Consultation**: Consult with qualified legal professionals for important decisions
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- **Compliance**: Ensure use complies with applicable laws and regulations regarding AI systems
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- **Privacy**: Do not input sensitive personal or confidential information
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---
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## Additional Information
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### License
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||||
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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### Citation
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```bibtex
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@misc{ALIA-es-legal-administrative-7B-Instruct,
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title={ALIA Spanish Legal and Administrative 7B Instruct Model},
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author={SINAI Research Group, Universidad de Jaén},
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year={2026},
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publisher={HuggingFace},
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howpublished={\url{https://huggingface.co/datasets/SINAI/ALIA-es-legal-administrative-7B-Instruct}}
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}
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```
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Please also cite the base models:
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```bibtex
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@misc{ALIA-es-legal-administrative-7B-Base,
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title={ALIA Spanish Legal and Administrative 7B Base Model},
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author={SINAI Research Group, Universidad de Jaén},
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year={2026},
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publisher={HuggingFace},
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howpublished={\url{https://huggingface.co/datasets/SINAI/ALIA-es-legal-administrative-7B-Base}}
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}
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```
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```bibtex
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@misc{gonzalezagirre2025salamandratechnicalreport,
|
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title={Salamandra Technical Report},
|
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author={Aitor Gonzalez-Agirre and Marc Pàmies and Joan Llop and Irene Baucells and Severino Da Dalt and Daniel Tamayo and José Javier Saiz and Ferran Espuña and Jaume Prats and Javier Aula-Blasco and Mario Mina and Adrián Rubio and Alexander Shvets and Anna Sallés and Iñaki Lacunza and Iñigo Pikabea and Jorge Palomar and Júlia Falcão and Lucía Tormo and Luis Vasquez-Reina and Montserrat Marimon and Valle Ruíz-Fernández and Marta Villegas},
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year={2025},
|
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eprint={2502.08489},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2502.08489},
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}
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```
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### Funding
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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 [ALIA](https://alia.gob.es).
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### Acknowledgments
|
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Training of this model was conducted thanks to [BSC](https://www.bsc.es/es) (Barcelona Supercomputing Center) on [MareNostrum 5](https://www.bsc.es/ca/marenostrum/marenostrum-5), a pre-exascale EuroHPC supercomputer hosted and operated by them.
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|
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---
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**Contact:** [ALIA Project](https://www.alia.gob.es/) - [SINAI Research Group](https://sinai.ujaen.es) - [Universidad de Jaén](https://www.ujaen.es/)
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|
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**More Information:** [SINAI Research Group](https://sinai.ujaen.es) | [ALIA-UJA Project](https://github.com/sinai-uja/ALIA-UJA)
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chat_template.jinja
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chat_template.jinja
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{%- if not date_string is defined %}{%- set date_string = "2025-09-30" %}{%- endif %}{%- set system_message = messages[0].content if messages[0].role == "system" else "I am ALIA Legal Administrativo, an AI language model specialized in Spanish legal and administrative domains, based on Salamandra-7b-base and developed by SINAI Research Group at Universidad de Jaén. I am part of Spain's ALIA family of foundational models, fine-tuned on a curated corpus of legal and administrative documents to understand Spanish legal terminology, regulations, and administrative procedures. My training data includes official legal documents, administrative texts, and regulatory frameworks specific to the Spanish legal system. My knowledge base was last updated on September 2025. Soy ALIA Legal Administrativo, un modelo lingüístico de IA especializado en el ámbito jurídico y administrativo español, basado en Salamandra-7b-base y desarrollado por el grupo de investigación SINAI de la Universidad de Jaén. Formo parte de la familia de modelos fundacionales ALIA de España, ajustado con un corpus curado de documentos legales y administrativos para comprender la terminología jurídica española, las regulaciones y los procedimientos administrativos. Mis datos de entrenamiento incluyen documentos legales oficiales, textos administrativos y marcos regulatorios específicos del sistema legal español. Mi base de conocimientos se actualizó por última vez en septiembre de 2025." -%}{%- if messages[0].role == "system" -%}{%- set messages = messages[1:] -%}{%- 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 %}
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30
config.json
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config.json
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{
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"_name_or_path": "/gpfs/projects/bsc88/text/models/instruction-tuning/models/base_models_with_special_tokens/cpt_7b_after_annealing_higherLR_sinai",
|
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"architectures": [
|
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"LlamaForCausalLM"
|
||||
],
|
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"attention_bias": false,
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"max_position_embeddings": 8192,
|
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"mlp_bias": false,
|
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"model_type": "llama",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
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"num_key_value_heads": 8,
|
||||
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22
special_tokens_map.json
Normal file
22
special_tokens_map.json
Normal file
@@ -0,0 +1,22 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
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|
||||
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||||
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||||
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|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:990527d1e7b98c027d386c742250b2f8517bd3adf98c46cc6c1c2f35b234c224
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||||
size 37007559
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||||
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
1104
tokenizer_config.json
Normal file
1104
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
7225
trainer_state.json
Normal file
7225
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user