--- library_name: peft license: mit language: - en - vi datasets: - kaitchup/opus-Vietnamese-to-English tags: - translation --- # Model Card for Model ID This is an adapter for Meta's Llama 2 7B fine-tuned for translating Vietnamese text into English. ## Model Details ### Model Description - **Developed by:** [The Kaitchup](https://kaitchup.substack.com/) - **Model type:** LoRA Adapter for Llama 2 7B - **Language(s) (NLP):** Vietnamese, English - **License:** MIT license ## Uses This adapter must be loaded on top of Llama 2 7B. It has been fine-tuned with QLoRA. For optimal results, the base model must be loaded with the exact same configuration used during fine-tuning. You can use the following code to load the model: ``` from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch from peft import PeftModel base_model = "meta-llama/Llama-2-7b-hf" compute_dtype = getattr(torch, "float16") bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=compute_dtype, bnb_4bit_use_double_quant=True, ) model = AutoModelForCausalLM.from_pretrained( original_model_directory, device_map={"": 0}, quantization_config=bnb_config ) tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True) model = PeftModel.from_pretrained(model, "kaitchup/Llama-2-7b-mt-Vietnamese-to-English") ``` Then, run the model as follows: ``` my_text = "" #put your text to translate here prompt = my_text+" ###>" tokenized_input = tokenizer(prompt, return_tensors="pt") input_ids = tokenized_input["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, num_beams=10, return_dict_in_generate=True, output_scores=True, max_new_tokens=130 ) for seq in generation_output.sequences: output = tokenizer.decode(seq, skip_special_tokens=True) print(output.split("###>")[1].strip()) ``` ## Model Card Contact [The Kaitchup](https://kaitchup.substack.com/)