Any idea how to solve this: Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules in 32-bit,...
quantization_config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True) AutoModelForCausalLM.from_pretrained(path, device_map='auto', quantization_config=quantization_config) If the model does not fit into VRAM, it reports: ...
bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, # llm_int8_enable_fp32_cpu_offload=True ) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, device_map=device_map...
offload_buffers: bool = False, keep_in_fp32_modules: List[str] = None, offload_8bit_bnb: bool = False, strict: bool = False, ): """ Loads a (potentially sharded) checkpoint inside a model, potentially sending weights to a given device as they are loaded. <Tip warning={true}> Onc...