some errores

[ahe168]

The argument trust_remote_code is to be used with Auto classes. It has no effect here and is ignored.
The load_in_4bit and load_in_8bit arguments are deprecated and will be removed in the future versions. Please, pass a BitsAndBytesConfig object in quantization_config argument instead.

ImportError Traceback (most recent call last)
Cell In[21], line 6
4 tokenizer = LlamaTokenizerFast.from_pretrained(base_model, trust_remote_code=True)
5 tokenizer.pad_token = tokenizer.eos_token
----> 6 model = LlamaForCausalLM.from_pretrained(base_model, trust_remote_code=True, device_map = "cuda:0", load_in_8bit = True,)
7 model = PeftModel.from_pretrained(model, peft_model)
8 model = model.eval()

File /opt/conda/lib/python3.10/site-packages/transformers/modeling_utils.py:3049, in PreTrainedModel.from_pretrained(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)
3046 hf_quantizer = None
3048 if hf_quantizer is not None:
-> 3049 hf_quantizer.validate_environment(
3050 torch_dtype=torch_dtype, from_tf=from_tf, from_flax=from_flax, device_map=device_map
3051 )
3052 torch_dtype = hf_quantizer.update_torch_dtype(torch_dtype)
3053 device_map = hf_quantizer.update_device_map(device_map)

File /opt/conda/lib/python3.10/site-packages/transformers/quantizers/quantizer_bnb_8bit.py:62, in Bnb8BitHfQuantizer.validate_environment(self, *args, **kwargs)
60 def validate_environment(self, *args, **kwargs):
61 if not (is_accelerate_available() and is_bitsandbytes_available()):
---> 62 raise ImportError(
63 "Using bitsandbytes 8-bit quantization requires Accelerate: pip install accelerate "
64 "and the latest version of bitsandbytes: pip install -i https://pypi.org/simple/ bitsandbytes"
65 )
67 if kwargs.get("from_tf", False) or kwargs.get("from_flax", False):
68 raise ValueError(
69 "Converting into 4-bit or 8-bit weights from tf/flax weights is currently not supported, please make"
70 " sure the weights are in PyTorch format."
71 )

ImportError: Using bitsandbytes 8-bit quantization requires Accelerate: pip install accelerate and the latest version of bitsandbytes: pip install -i https://pypi.org/simple/ bitsandbytes

[liaorihu]

The solution can be easily found on Google.

My proposed solution to this issue is as follows:

• Update the version of 'bitsandbytes'. You can find the suitable version under this project.
• Caerfully review the document on Hugging Face's 'peft' to adjust relevant parameters.

I hope my answer will work for you.

[Siddharth-Latthe-07]

The error message you're encountering indicates that the bitsandbytes 8-bit quantization requires the Accelerate library and the latest version of bitsandbytes. Additionally, the arguments load_in_4bit and load_in_8bit are deprecated, and you should use a BitsAndBytesConfig object with the quantization_config argument instead.
Try out these steps and let me know, if it works:-

1. Install the required libraries
2. Update your code to use BitsAndBytesConfig:
   sample snippet:-

from transformers import LlamaForCausalLM, LlamaTokenizerFast
from transformers import BitsAndBytesConfig

base_model = 'your_model_path_or_name'
peft_model = 'your_peft_model_path_or_name'

tokenizer = LlamaTokenizerFast.from_pretrained(base_model, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

quant_config = BitsAndBytesConfig.load_in_8bit()

model = LlamaForCausalLM.from_pretrained(
    base_model,
    trust_remote_code=True,
    device_map="cuda:0",
    quantization_config=quant_config
)
model = PeftModel.from_pretrained(model, peft_model)
model = model.eval()

and lastly check whether you are using CUDA environment

import torch
print(torch.cuda.is_available())

Hope, this helps
Thanks