Model Card for LLaMAntino-2-7b-dolly
Last Update: 22/01/2024
Model description
LLaMAntino-2-7b-dolly is a Large Language Model (LLM) that is an instruction-tuned version of LLaMAntino-2-7b (an italian-adapted LLaMA 2). This model aims to provide Italian NLP researchers with a tool to tackle tasks such as information extraction and closed qa.
The model was trained following the methodology used for Alpaca and using as training data dolly-15k-it formatted in an instruction-following style. If you are interested in more details regarding the training procedure, you can find the code we used at the following link:
- Repository: https://github.com/swapUniba/LLaMAntino
NOTICE: the code has not been released yet, we apologize for the delay, it will be available asap!
- Developed by: Pierpaolo Basile, Elio Musacchio, Marco Polignano, Lucia Siciliani, Giuseppe Fiameni, Giovanni Semeraro
- Funded by: PNRR project FAIR - Future AI Research
- Compute infrastructure: Leonardo supercomputer
- Model type: LLaMA 2
- Language(s) (NLP): Italian
- License: Llama 2 Community License
- Finetuned from model: swap-uniba/LLaMAntino-2-7b-hf-ITA
Prompt Format
This prompt format based on the Alpaca model was used for fine-tuning:
"Di seguito è riportata un'istruzione che descrive un'attività, abbinata ad un input che fornisce ulteriore informazione. " \
"Scrivi una risposta che soddisfi adeguatamente la richiesta.\n\n" \
f"### Istruzione:\n{instruction}\n\n### Input:\n{input}\n\n### Risposta:\n{response}"
If no input was present in the instruction, the following prompt was used:
"Di seguito è riportata un'istruzione che descrive un'attività. " \
"Scrivi una risposta che soddisfi adeguatamente la richiesta.\n\n" \
f"### Istruzione:\n{instruction}\n\n### Risposta:\n{response}"
We recommend using the same prompt in inference to obtain the best results!
How to Get Started with the Model
Below you can find an example of model usage:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "swap-uniba/LLaMAntino-2-7b-hf-dolly-ITA"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
instruction_text = "Estrai i nomi propri di persona dal testo che segue"
input_text = "Marco ha incontrato Matteo per strada e hanno parlato di Mirco"
prompt = "Di seguito è riportata un'istruzione che descrive un'attività, accompagnata da un input che aggiunge ulteriore informazione. " \
f"Scrivi una risposta che completi adeguatamente la richiesta.\n\n" \
f"### Istruzione:\n{instruction_text}\n\n" \
f"### Input:\n{input_text}\n\n" \
f"### Risposta:\n"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
outputs = model.generate(input_ids=input_ids)
print(tokenizer.batch_decode(outputs.detach().cpu().numpy()[:, input_ids.shape[1]:], skip_special_tokens=True)[0])
If you are facing issues when loading the model, you can try to load it quantized:
model = AutoModelForCausalLM.from_pretrained(model_id, load_in_8bit=True)
Note: The model loading strategy above requires the bitsandbytes and accelerate libraries
Evaluation
Coming soon!
Citation
If you use this model in your research, please cite the following:
@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}
}
Notice: Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. License
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