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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-chat-13b-UltraChat-ITA |
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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-chat-13b-UltraChat** is a *Large Language Model (LLM)* that is an instruction-tuned version of **LLaMAntino-2-chat-13b** (an italian-adapted **LLaMA 2 chat**). |
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This model aims to provide Italian NLP researchers with an improved model for italian dialogue use cases. |
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The model was trained using *QLora* and using as training data [UltraChat](https://github.com/thunlp/ultrachat) translated to the italian language using [Argos Translate](https://pypi.org/project/argostranslate/1.4.0/). |
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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-chat |
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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-chat-13b-hf-ITA](https://huggingface.co/swap-uniba/LLaMAntino-2-chat-13b-hf-ITA) |
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## Prompt Format |
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This prompt format based on the [LLaMA 2 prompt template](https://gpus.llm-utils.org/llama-2-prompt-template/) adapted to the italian language was used: |
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```python |
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" [INST]<<SYS>>\n" \ |
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"Sei un assistente disponibile, rispettoso e onesto. " \ |
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"Rispondi sempre nel modo piu' utile possibile, pur essendo sicuro. " \ |
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"Le risposte non devono includere contenuti dannosi, non etici, razzisti, sessisti, tossici, pericolosi o illegali. " \ |
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"Assicurati che le tue risposte siano socialmente imparziali e positive. " \ |
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"Se una domanda non ha senso o non e' coerente con i fatti, spiegane il motivo invece di rispondere in modo non corretto. " \ |
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"Se non conosci la risposta a una domanda, non condividere informazioni false.\n" \ |
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"<</SYS>>\n\n" \ |
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f"{user_msg_1}[/INST] {model_answer_1} </s> <s> [INST]{user_msg_2}[/INST] {model_answer_2} </s> ... <s> [INST]{user_msg_N}[/INST] {model_answer_N} </s>" |
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``` |
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We recommend using the 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-chat-13b-hf-UltraChat-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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user_msg = "Ciao! Come stai?" |
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prompt = " [INST]<<SYS>>\n" \ |
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"Sei un assistente disponibile, rispettoso e onesto. " \ |
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"Rispondi sempre nel modo piu' utile possibile, pur essendo sicuro. " \ |
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"Le risposte non devono includere contenuti dannosi, non etici, razzisti, sessisti, tossici, pericolosi o illegali. " \ |
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"Assicurati che le tue risposte siano socialmente imparziali e positive. " \ |
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"Se una domanda non ha senso o non e' coerente con i fatti, spiegane il motivo invece di rispondere in modo non corretto. " \ |
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"Se non conosci la risposta a una domanda, non condividere informazioni false.\n" \ |
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"<</SYS>>\n\n" \ |
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f"{user_msg}[/INST]" |
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pipe = transformers.pipeline( |
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model=model, |
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tokenizer=tokenizer, |
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return_full_text=False, # langchain expects the full text |
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task='text-generation', |
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max_new_tokens=512, # max number of tokens to generate in the output |
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temperature=0.8 #temperature for more or less creative answers |
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) |
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# Method 1 |
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sequences = pipe(text) |
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for seq in sequences: |
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print(f"{seq['generated_text']}") |
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# Method 2 |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids |
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outputs = model.generate(input_ids=input_ids, max_length=512) |
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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*: |
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1) 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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2) The Tokenizer, by default, adds at the beginning of the prompt the '\<BOS\>' token. If that is not the case, add as a starting token the *\<s\>* string. |
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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/) |
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