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Model: gplsi/Aitana-2B-S-base-IP-1.0
Source: Original Platform

README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - llama
+  - causal-lm
+  - text-generation
+  - transformers
+---
+
+# Aitana-2B-S-base-IP-1.0
+
+## Table of Contents
+
+- Model description
+- Intended uses and limitations
+- How to use
+- Training
+- Technical specifications
+- Additional information
+
+## Model description
+
+Aitana-2B-S-base-IP-1.0 is a generative language model with a decoder-only architecture.
+This repository contains the base checkpoint, intended for causal language modeling and
+for further adaptation or task-specific fine-tuning.
+
+Based on the files shipped in this repository, the checkpoint uses the Llama
+architecture and the Transformers ecosystem. The local configuration indicates:
+
+- architecture: `LlamaForCausalLM`
+- hidden size: `2048`
+- layers: `24`
+- attention heads: `16`
+- vocabulary size: `256000`
+- context length: `8192`
+- tensor dtype in config: `bfloat16`
+
+## Intended uses and limitations
+
+Aitana-2B-S-base-IP-1.0 is a base model that can be used for causal language
+modeling and text generation. As with other base checkpoints, it is generally more
+useful as a starting point for instruction-tuning, domain adaptation, or downstream
+fine-tuning than as a final end-user assistant model.
+
+Because this repository currently only exposes the model artifacts and not the full
+training report, claims about domain coverage, language balance, safety behavior, and
+ benchmark performance should be added only once they are confirmed by the model
+authors.
+
+## How to use
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "gplsi/Aitana-2B-S-base-IP-1.0"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+prompt = "Escriu un breu resum sobre la importància de la llengua."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=128,
+    do_sample=True,
+    top_p=0.9,
+    temperature=0.7,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.pad_token_id,
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Training
+
+### Base model
+
+TO-DO: document the original parent checkpoint or initialization source for
+Aitana-2B-S-base-IP-1.0.
+
+### Training data
+
+TO-DO: document the training corpora, language distribution, preprocessing steps,
+deduplication policy, anonymization steps, and data filtering criteria.
+
+### Training hyperparameters
+
+TO-DO: document the effective batch size, learning rate schedule, optimizer setup,
+number of epochs or tokens seen, sequence length used during training, and hardware.
+
+## Technical specifications
+
+### Model architecture and objective
+
+- architecture: decoder-only causal language model
+- implementation class: `LlamaForCausalLM`
+- hidden size: `2048`
+- intermediate size: `5440`
+- layers: `24`
+- attention heads: `16`
+- key/value heads: `16`
+- maximum position embeddings: `8192`
+- vocabulary size: `256000`
+- BOS token id: `1`
+- EOS token id: `2`
+- PAD token id: `3`
+
+### Tokenizer
+
+The tokenizer files in this repository define:
+
+- BOS token: `<s>`
+- EOS token: `</s>`
+- PAD token: `<pad>`
+- UNK token: `<unk>`
+
+### Hardware and software
+
+The repository is packaged for the Hugging Face `transformers` library.
+Specific training hardware and training software details should be documented by the
+model authors if they are intended to be part of the public model card.
+
+## Additional information
+
+### Author
+
+TO-DO: confirm the author list and institutional attribution to be displayed in the
+public model card.
+
+### Contact
+
+TO-DO: add a contact email or project contact point.
+
+### License
+
+TO-DO: confirm the license for this checkpoint and add it both here and in
+`config.json` if desired.
+
+### Funding
+
+TO-DO: add funding information if this checkpoint is part of a funded project.
+
+### Disclaimer
+
+This repository contains a base language model checkpoint. Base models can reflect
+biases present in their training data and may generate inaccurate, misleading, or
+unsafe content. Anyone deploying this model, or systems built on top of it, is
+responsible for evaluating those risks and ensuring compliance with applicable legal,
+ethical, and operational requirements.