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Model: swap-uniba/LLaMAntino-2-7b-hf-ITA Source: Original Platform
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README.md
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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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"LlamaForCausalLM"
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],
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}
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||||
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|
||||
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|
||||
24
special_tokens_map.json
Normal file
24
special_tokens_map.json
Normal file
@@ -0,0 +1,24 @@
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
93391
tokenizer.json
Normal file
93391
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
33
tokenizer_config.json
Normal file
33
tokenizer_config.json
Normal file
@@ -0,0 +1,33 @@
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user