77 lines
3.0 KiB
Markdown
77 lines
3.0 KiB
Markdown
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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-ITA
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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** is a *Large Language Model (LLM)* that is an italian-adapted **LLaMA 2**.
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This model aims to provide Italian NLP researchers with a base model for natural language generation tasks.
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The model was trained using *QLora* and using as training data [clean_mc4_it medium](https://huggingface.co/datasets/gsarti/clean_mc4_it/viewer/medium).
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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:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
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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-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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prompt = "Scrivi qui un possibile prompt"
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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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## 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/)
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