you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to `from_pretrained`.

Sure, to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map to `from_pretrained` in CSDN ChatGPT, you can use the following code:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model_name = "gpt2"
model_path = "path/to/pretrained/model"

# Initialize the tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(
    model_path,
    load_in_8bit_fp32_cpu_offload=True,
    device_map={"cuda:0": "cuda:1"}  # Custom device map
)

In the code above, `model_path` should be the path to the directory containing the pretrained model. The `load_in_8bit_fp32_cpu_offload` parameter is set to `True` to enable loading the model in 8-bit floating-point format with CPU offloading. The `device_map` parameter is used to specify a custom mapping of devices, in this case mapping `cuda:0` to `cuda:1`.

You can adjust the values of `model_name`, `model_path`, and the custom `device_map` according to your requirements.