library_name: transformers
base_model:
- unsloth/Llama-3.2-1B-Instruct
license: llama3.2
language:
- en
- it
tags:
- translation
A tiny Llama model tuned for text translation
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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.
Due to its size, the model runs very well on CPUs.
Usage
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
base_model_id = "unsloth/Llama-3.2-1B-Instruct"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id, # Mistral, same as before
quantization_config=bnb_config, # Same quantization config as before
device_map="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True)
ft_model = PeftModel.from_pretrained(base_model, "LeonardPuettmann/LlaMaestro-3.2-1B-Instruct-v0.1-4bit")
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": "Scontri a Bologna, la destra lancia l'offensiva contro i centri sociali."}
]
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(ft_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: unsloth/Llama-3.2-1B-Instruct derived from meta-llama/Llama-3.2-1B-Instruct
Finetuned by: Leonard Püttmann https://www.linkedin.com/in/leonard-p%C3%BCttmann-4648231a9/