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1from peft import AutoPeftModelForCausalLM, LoraConfig, PeftModel
2from transformers import AutoTokenizer, pipeline
3import torch
4
5base_model_name = "NousResearch/Llama-2-7b-chat-hf"
6qlora_model_adapter = "sartajbhuvaji/llama-2-7b-resonate-v1"
7device_map = {"": 0}
8
9base_model = AutoModelForCausalLM.from_pretrained(
10 base_model_name,
11 low_cpu_mem_usage=True,
12 return_dict=True,
13 torch_dtype=torch.float16,
14 device_map=device_map,
15)
16
17model = PeftModel.from_pretrained(base_model, qlora_model_adapter)
18model = model.merge_and_unload()
19
20tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
21tokenizer.pad_token = tokenizer.eos_token
22tokenizer.padding_side = "right"
23
24prompt = "What is a large language model?"
25pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=2000)
26result = pipe(f"<s>[INST] {prompt} [/INST]")
27print(result[0]['generated_text'])