--- pipeline_tag: text2text-generation --- # Further instruct Tuning stanford alpaca based on tloen/alpaca-lora-7b on Hong Kong 2023 Consumption Voucher Scheme Frequently Asked Questions ## How to use it ```python from transformers import LlamaForCausalLM, LlamaTokenizer,GenerationConfig from peft import PeftModel device_map = "auto" tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf") model = LlamaForCausalLM.from_pretrained( "decapoda-research/llama-7b-hf", load_in_8bit=True, device_map="auto", ) ### load model after fine tuned on alpaca datasets model = PeftModel.from_pretrained(model, "Nelsonlin0321/alpaca-lora-7b-tuned-on-hk-cvs-fqa") tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf") tokenizer.pad_token_id = 0 def generate_prompt_eval(instruction): template = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response:""" return template eval_generation_config = GenerationConfig( temperature=0.1, top_p=0.75, num_beams=4, ) def generate_answer(instruction): prompt = generate_prompt_eval(instruction) inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, generation_config=eval_generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=256 ) for s in generation_output.sequences: output = tokenizer.decode(s) # print(output) print("Response:", output.split("### Response:")[1].strip()) question = "Who are eligible to be disbursed with the first-instalment voucher of $1,500 on 16 April?" generate_answer(question) >> Response: All eligible people who have successfully registered under 2022 CVS and met the relevant eligibility criteria will be disbursed with the first-instalment voucher of $1,500 on 16 April. ```