--- library_name: transformers base_model: - meta-llama/Llama-3.2-1B-Instruct license: llama3.2 language: - en - it tags: - translation - text-generation --- # LlaMaestra - A tiny Llama model tuned for text translation ```html _ _ ___ ___ _ | | | | | \/ | | | | | | | __ _| . . | __ _ ___ ___| |_ _ __ __ _ | | | |/ _` | |\/| |/ _` |/ _ \/ __| __| '__/ _` | | |___| | (_| | | | | (_| | __/\__ \ |_| | | (_| | \_____/_|\__,_\_| |_/\__,_|\___||___/\__|_| \__,_| ``` ## Model Card This model was finetuned with roughly 300.000 examples of translations from English to Italian and Italian to English. The model was finetuned in a way to more directly provide a translation without much explanation. Finetuning took about 10 hours on an A10G Nvidia GPU. Due to its size, the model runs very well on CPUs. ![A very italian Llama model](llamaestro-sm-bg.png) ## Usage ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "LeonardPuettmann/LlaMaestra-3.2-1B-Instruct-v0.1" model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_id, add_bos_token=True, trust_remote_code=True) row_json = [ {"role": "system", "content": "Your job is to return translations for sentences or words from either Italian to English or English to Italian."}, {"role": "user", "content": "Do you sell tickets for the bus?"}, ] prompt = tokenizer.apply_chat_template(row_json, tokenize=False) model_input = tokenizer(prompt, return_tensors="pt").to("cuda") with torch.no_grad(): print(tokenizer.decode(model.generate(**model_input, max_new_tokens=1024)[0])) ``` ## Data used The source for the data were sentence pairs from tatoeba.com. The data can be downloaded from here: https://tatoeba.org/downloads ## Credits Base model: `meta-llama/Llama-3.2-1B-Instruct` Finetuned by: Leonard PĆ¼ttmann https://www.linkedin.com/in/leonard-p%C3%BCttmann-4648231a9/