metadata
license: apache-2.0
datasets:
- databricks/databricks-dolly-15k
language:
- en
模型介绍
- 使用模型：LLaMA2-7B
- 微调方法：QLoRA
- 数据集：databricks/databricks-dolly-15k
- 显卡：一张RTX4090
- 目标：对模型进行指令微调
使用方法
- 加载数据
from datasets import load_dataset
from random import randrange
# 从hub加载数据集并得到一个样本
dataset = load_dataset("databricks/databricks-dolly-15k", split="train")
sample = dataset[randrange(len(dataset))]
- 模型使用
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name_or_path = "snowfly/llama2-7b-QLoRA-dolly"
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name_or_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_name_or_path,
trust_remote_code=True,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
load_in_4bit=True)
model = model.eval()
prompt = f"""### Instruction:
Use the Input below to create an instruction, which could have been used to generate the input using an LLM.
### Input:
{sample['response']}
### Response:
"""
input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.9)
print(f"Prompt:\n{sample['response']}\n")
print(f"Generated instruction:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}")
print(f"Ground truth:\n{sample['instruction']}")