| import torch |
| from dataclasses import dataclass |
| from peft import LoraConfig, get_peft_model |
| from transformers import LlamaForCausalLM, LlamaTokenizer |
|
|
|
|
| @dataclass |
| class LoraArguments: |
| lora_r: int = 8 |
| lora_alpha: int = 16 |
| lora_dropout: float = 0.05 |
| lora_target_modules = ["q_proj", "v_proj"] |
| lora_weight_path: str = "" |
| bias: str = "none" |
| |
|
|
| if __name__ == "__main__": |
| device = 0 |
| lora_args = LoraArguments |
| base_model = "TheBloke/vicuna-13B-1.1-HF" |
|
|
| tokenizer = LlamaTokenizer.from_pretrained(base_model) |
| model = LlamaForCausalLM.from_pretrained( |
| base_model, load_in_8bit=True, |
| torch_dtype=torch.float16, device_map={"": device} |
| ) |
|
|
| lora_config = LoraConfig( |
| r=lora_args.lora_r, lora_alpha=lora_args.lora_alpha, lora_dropout=lora_args.lora_dropout, |
| target_modules=lora_args.lora_target_modules, bias=lora_args.bias, task_type="CAUSAL_LM", |
| ) |
| model = get_peft_model(model, lora_config) |
|
|
| weight = torch.load("pytorch_model.bin", map_location="cpu") |
| model.load_state_dict(weight) |
|
|
| prompt = ( |
| "A chat between a curious user and an artificial intelligence assistant. " |
| "The assistant gives helpful, detailed, and polite answers to the user's questions. " |
| "USER: You are tasked to demonstrate your writing skills in professional or work settings for the following question.\n" |
| "Can you help me write a speech for a graduation ceremony, inspiring and motivating the graduates to pursue their dreams and make a positive impact on the world?\n" |
| "Output: ASSISTANT: " |
| ) |
|
|
| inputs = tokenizer([prompt], return_tensors="pt") |
| inputs = {k: v.to("cuda:{}".format(device)) for k, v in inputs.items()} |
|
|
| out = model.generate( |
| **inputs, max_new_tokens=500, min_new_tokens=100, early_stopping=True, do_sample=True, top_k=8, temperature=0.75 |
| ) |
| decoded = tokenizer.decode(out[0], skip_special_tokens=True, clean_up_tokenization_spaces=True) |
| print (decoded) |
