# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Pagewood/T-LLaMA")
model = AutoModelForCausalLM.from_pretrained("Pagewood/T-LLaMA")Quick Links
T‑LLaMA: a Tibetan large language model based on LLaMA2
In this study, we built a corpus containing 2.2 billion Tibetan characters and trained Tibetan LLaMA based on LLaMA2 7B. We achieved state-of-the-art performance in the text classification task using the open-source TNCC dataset, with an accuracy of 79.8%. Additionally, we obtained promising results in text generation and text summarization tasks.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Pagewood/T-LLaMA")