datasets, base_model, model_creator, language, metrics, pipeline_tag, tags, library_name, license

datasets	base_model	model_creator	language	metrics	pipeline_tag	tags	library_name	license
gsarti/clean_mc4_it Chat-Error/wizard_alpaca_dolly_orca mlabonne/orpo-dpo-mix-40k	meta-llama/Meta-Llama-3-8B-Instruct	Marco Polignano - SWAP Research Group	en it	accuracy	text-generation	facebook meta pythorch llama llama-3 llamantino	transformers	llama3

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"Built with Meta Llama 3".

LLaMAntino-3-ANITA-8B-Inst-DPO-ITA is a model of the LLaMAntino - Large Language Models family. The model is an instruction-tuned version of Meta-Llama-3-8b-instruct (a fine-tuned LLaMA 3 model). This model version aims to be the a Multilingual Model 🏁 (EN 🇺🇸 + ITA🇮🇹) to further fine-tuning on Specific Tasks in Italian.

The 🌟ANITA project🌟 (Advanced Natural-based interaction for the ITAlian language) wants to provide Italian NLP researchers with an improved model for the Italian Language 🇮🇹 use cases.

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Model Details

https://github.com/marcopoli/LLaMAntino-3-ANITA

• Full Model: LaMAntino-3-ANITA-8B-Inst-DPO-ITA
• LLaMA.cpp - F16 model
• LLaMA.cpp - Q8_0 model
• LLaMA.cpp - Q4_K_M model
• LLaMA.cpp - Q2_K model

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Specifications

• Model developers:
  Ph.D. Marco Polignano - University of Bari Aldo Moro, Italy
  SWAP Research Group
  
• Variations: The model release has been supervised fine-tuning (SFT) using QLoRA 4bit, on instruction-based datasets. DPO approach over the mlabonne/orpo-dpo-mix-40k dataset is used to align with human preferences for helpfulness and safety.
• Input: Models input text only.
• Language: Multilingual 🏁 + Italian 🇮🇹
• Output: Models generate text and code only.
• Model Architecture: Llama 3 architecture.
• Context length: 8K, 8192.
• Library Used: LLaMA.cpp

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Prompt Template

<|start_header_id|>system<|end_header_id|>

{ SYS Prompt }<|eot_id|><|start_header_id|>user<|end_header_id|>

{ USER Prompt }<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{ ASSIST Prompt }<|eot_id|>

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LLaMA.cpp

LLaMA.cpp, a great tool that helps us easily Quantize your model in GGUF format.

Citation instructions

@misc{polignano2024advanced,
      title={Advanced Natural-based interaction for the ITAlian language: LLaMAntino-3-ANITA}, 
      author={Marco Polignano and Pierpaolo Basile and Giovanni Semeraro},
      year={2024},
      eprint={2405.07101},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{basile2023llamantino,
      title={LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language}, 
      author={Pierpaolo Basile and Elio Musacchio and Marco Polignano and Lucia Siciliani and Giuseppe Fiameni and Giovanni Semeraro},
      year={2023},
      eprint={2312.09993},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{llama3modelcard,
  title={Llama 3 Model Card},
  author={AI@Meta},
  year={2024},
  url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}