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--- |
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license: llama2 |
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language: |
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- it |
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tags: |
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- text-generation-inference |
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--- |
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# Model Card for LLaMAntino-2-7b-evalita |
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*Last Update: 22/01/2024*<br> |
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## Model description |
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<!-- Provide a quick summary of what the model is/does. --> |
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**LLaMAntino-2-7b-evalita** is a *Large Language Model (LLM)* that is an instruction-tuned version of **LLaMAntino-2-7b** (an italian-adapted **LLaMA 2**). |
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This model aims to provide Italian NLP researchers with a tool to tackle tasks such as *sentiment analysis* and *text categorization*. |
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The model was trained following the methodology used for [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and using as training data [EVALITA 2023 tasks](https://www.evalita.it/campaigns/evalita-2023/tasks/) formatted in an instruction-following style. |
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If you are interested in more details regarding the training procedure, you can find the code we used at the following link: |
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- **Repository:** https://github.com/swapUniba/LLaMAntino |
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**NOTICE**: the code has not been released yet, we apologize for the delay, it will be available asap! |
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- **Developed by:** Pierpaolo Basile, Elio Musacchio, Marco Polignano, Lucia Siciliani, Giuseppe Fiameni, Giovanni Semeraro |
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- **Funded by:** PNRR project FAIR - Future AI Research |
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- **Compute infrastructure:** [Leonardo](https://www.hpc.cineca.it/systems/hardware/leonardo/) supercomputer |
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- **Model type:** LLaMA 2 |
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- **Language(s) (NLP):** Italian |
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- **License:** Llama 2 Community License |
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- **Finetuned from model:** [swap-uniba/LLaMAntino-2-7b-hf-ITA](https://huggingface.co/swap-uniba/LLaMAntino-2-7b-hf-ITA) |
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## Prompt Format |
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This prompt format based on the Alpaca model was used for fine-tuning: |
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```python |
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"Di seguito è riportata un'istruzione che descrive un'attività, abbinata ad un input che fornisce ulteriore informazione. " \ |
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"Scrivi una risposta che soddisfi adeguatamente la richiesta.\n\n" \ |
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f"### Istruzione:\n{instruction}\n\n### Input:\n{input}\n\n### Risposta:\n{response}" |
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``` |
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We recommend using this same prompt in inference to obtain the best results! |
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## How to Get Started with the Model |
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Below you can find an example of model usage: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_id = "swap-uniba/LLaMAntino-2-7b-hf-evalita-ITA" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id) |
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instruction_text = "Categorizza le emozioni espresse nel testo fornito in input o determina l'assenza di emozioni. " \ |
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"Puoi classificare il testo come neutrale o identificare una o più delle seguenti emozioni: " \ |
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"rabbia, anticipazione, disgusto, paura, gioia, tristezza, sorpresa, fiducia, amore." |
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input_text = "Non me lo aspettavo proprio, ma oggi è stata una bellissima giornata, sono contentissimo!" |
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prompt = "Di seguito è riportata un'istruzione che descrive un'attività, accompagnata da un input che aggiunge ulteriore informazione. " \ |
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f"Scrivi una risposta che completi adeguatamente la richiesta.\n\n" \ |
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f"### Istruzione:\n{instruction_text}\n\n" \ |
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f"### Input:\n{input_text}\n\n" \ |
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f"### Risposta:\n" |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids |
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outputs = model.generate(input_ids=input_ids) |
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print(tokenizer.batch_decode(outputs.detach().cpu().numpy()[:, input_ids.shape[1]:], skip_special_tokens=True)[0]) |
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``` |
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If you are facing issues when loading the model, you can try to load it quantized: |
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```python |
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model = AutoModelForCausalLM.from_pretrained(model_id, load_in_8bit=True) |
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``` |
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*Note*: The model loading strategy above requires the [*bitsandbytes*](https://pypi.org/project/bitsandbytes/) and [*accelerate*](https://pypi.org/project/accelerate/) libraries |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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*Coming soon*! |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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If you use this model in your research, please cite the following: |
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```bibtex |
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@misc{basile2023llamantino, |
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title={LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language}, |
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author={Pierpaolo Basile and Elio Musacchio and Marco Polignano and Lucia Siciliani and Giuseppe Fiameni and Giovanni Semeraro}, |
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year={2023}, |
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eprint={2312.09993}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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*Notice:* Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. [*License*](https://ai.meta.com/llama/license/) |
