update model card

README.md

@@ -1,3 +1,92 @@
 ---
 license: llama2
+language:
+- it
+tags:
+- text-generation-inference
 ---
+# Model Card for LLaMAntino-2-13b-evalita
+
+## Model description
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**LLaMAntino-2-13b-evalita** is a *Large Language Model (LLM)* that is an instruction-tuned version of **LLaMAntino-2-13b** (an italian-adapted **LLaMA 2**). 
+This model aims to provide Italian NLP researchers with a tool to tackle tasks such as *sentiment analysis* and *text categorization*.
+
+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. 
+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](https://www.hpc.cineca.it/systems/hardware/leonardo/) supercomputer
+- **Model type:** Large Language Model (LLM)
+- **Language(s) (NLP):** Italian
+- **License:** Llama 2 Community License 
+- **Finetuned from model:** [swap-uniba/LLaMAntino-2-13b-hf-ITA](https://huggingface.co/swap-uniba/LLaMAntino-2-13b-hf-ITA)
+
+## Prompt Format
+
+This prompt format based on the Alpaca model was used for fine-tuning:
+
+```python
+"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}"
+```
+
+We recommend using this 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:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "swap-uniba/LLaMAntino-2-13b-hf-evalita-ITA"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+instruction_text = "Categorizza le emozioni espresse nel testo fornito in input o determina l'assenza di emozioni. " \
+                   "Puoi classificare il testo come neutrale o identificare una o più delle seguenti emozioni: " \
+                   "rabbia, anticipazione, disgusto, paura, gioia, tristezza, sorpresa, fiducia, amore."
+input_text = "Non me lo aspettavo proprio, ma oggi è stata una bellissima giornata, sono contentissimo!"
+
+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(), skip_special_tokens=True)[0][len(prompt):])
+```
+
+If you are facing issues when loading the model, you can try to load it quantized:
+
+```python
+model = AutoModelForCausalLM.from_pretrained(model_id, token=token, load_in_8bit=True)
+```
+
+*Note*: The model loading strategy above requires the [*bitsandbytes*](https://pypi.org/project/bitsandbytes/) and [*accelerate*](https://pypi.org/project/accelerate/) libraries
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+*Coming soon*!
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+If you use this model in your research, please cite the following:
+
+*Coming soon*!