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Model: gplsi/Aitana-2B-S-base-IP-1.0 Source: Original Platform
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README.md
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---
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- llama
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- causal-lm
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- text-generation
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- transformers
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---
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# Aitana-2B-S-base-IP-1.0
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## Table of Contents
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- Model description
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- Intended uses and limitations
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- How to use
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- Training
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- Technical specifications
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- Additional information
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## Model description
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Aitana-2B-S-base-IP-1.0 is a generative language model with a decoder-only architecture.
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This repository contains the base checkpoint, intended for causal language modeling and
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for further adaptation or task-specific fine-tuning.
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Based on the files shipped in this repository, the checkpoint uses the Llama
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architecture and the Transformers ecosystem. The local configuration indicates:
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- architecture: `LlamaForCausalLM`
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- hidden size: `2048`
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- layers: `24`
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- attention heads: `16`
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- vocabulary size: `256000`
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- context length: `8192`
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- tensor dtype in config: `bfloat16`
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## Intended uses and limitations
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Aitana-2B-S-base-IP-1.0 is a base model that can be used for causal language
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modeling and text generation. As with other base checkpoints, it is generally more
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useful as a starting point for instruction-tuning, domain adaptation, or downstream
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fine-tuning than as a final end-user assistant model.
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Because this repository currently only exposes the model artifacts and not the full
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training report, claims about domain coverage, language balance, safety behavior, and
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benchmark performance should be added only once they are confirmed by the model
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authors.
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## How to use
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "gplsi/Aitana-2B-S-base-IP-1.0"
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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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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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prompt = "Escriu un breu resum sobre la importància de la llengua."
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=128,
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do_sample=True,
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top_p=0.9,
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temperature=0.7,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Training
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### Base model
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TO-DO: document the original parent checkpoint or initialization source for
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Aitana-2B-S-base-IP-1.0.
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### Training data
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TO-DO: document the training corpora, language distribution, preprocessing steps,
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deduplication policy, anonymization steps, and data filtering criteria.
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### Training hyperparameters
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TO-DO: document the effective batch size, learning rate schedule, optimizer setup,
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number of epochs or tokens seen, sequence length used during training, and hardware.
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## Technical specifications
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### Model architecture and objective
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- architecture: decoder-only causal language model
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- implementation class: `LlamaForCausalLM`
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- hidden size: `2048`
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- intermediate size: `5440`
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- layers: `24`
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- attention heads: `16`
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- key/value heads: `16`
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- maximum position embeddings: `8192`
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- vocabulary size: `256000`
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- BOS token id: `1`
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- EOS token id: `2`
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- PAD token id: `3`
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### Tokenizer
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The tokenizer files in this repository define:
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- BOS token: `<s>`
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- EOS token: `</s>`
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- PAD token: `<pad>`
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- UNK token: `<unk>`
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### Hardware and software
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The repository is packaged for the Hugging Face `transformers` library.
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Specific training hardware and training software details should be documented by the
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model authors if they are intended to be part of the public model card.
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## Additional information
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### Author
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TO-DO: confirm the author list and institutional attribution to be displayed in the
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public model card.
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### Contact
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TO-DO: add a contact email or project contact point.
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### License
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TO-DO: confirm the license for this checkpoint and add it both here and in
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`config.json` if desired.
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### Funding
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TO-DO: add funding information if this checkpoint is part of a funded project.
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### Disclaimer
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This repository contains a base language model checkpoint. Base models can reflect
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biases present in their training data and may generate inaccurate, misleading, or
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unsafe content. Anyone deploying this model, or systems built on top of it, is
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responsible for evaluating those risks and ensuring compliance with applicable legal,
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ethical, and operational requirements.
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"dtype": "bfloat16",
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"eos_token_id": 2,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 5440,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"num_key_value_heads": 16,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.1",
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"use_cache": true,
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"vocab_size": 256000,
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"pad_token_id": 3
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.57.1"
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}
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model.safetensors
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special_tokens_map.json
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{
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"bos_token": {
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"single_word": false
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}
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}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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tokenizer.model
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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tokenizer_config.json
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"backend": "tokenizers",
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"use_default_system_prompt": false
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}
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