Auto and Base model classes in AutoAWQ

View the documentation of the main classes of AutoAWQ models below.

awq.models.auto.AutoAWQForCausalLM

AutoAWQForCausalLM()

Source code in awq/models/auto.py

def __init__(self):
    raise EnvironmentError(
        "You must instantiate AutoAWQForCausalLM with\n"
        "AutoAWQForCausalLM.from_quantized or AutoAWQForCausalLM.from_pretrained"
    )

from_pretrained classmethod

from_pretrained(model_path, trust_remote_code=True, safetensors=True, device_map='auto', download_kwargs=None, **model_init_kwargs)

PARAMETER	DESCRIPTION
model_path
trust_remote_code	DEFAULT: True
safetensors	DEFAULT: True
device_map	DEFAULT: 'auto'
download_kwargs	DEFAULT: None
**model_init_kwargs	DEFAULT: {}

Source code in awq/models/auto.py

@classmethod
def from_pretrained(
    self,
    model_path,
    trust_remote_code=True,
    safetensors=True,
    device_map="auto",
    download_kwargs=None,
    **model_init_kwargs,
) -> BaseAWQForCausalLM:
    model_type = check_and_get_model_type(
        model_path, trust_remote_code, **model_init_kwargs
    )

    return AWQ_CAUSAL_LM_MODEL_MAP[model_type].from_pretrained(
        model_path,
        model_type,
        trust_remote_code=trust_remote_code,
        safetensors=safetensors,
        device_map=device_map,
        download_kwargs=download_kwargs,
        **model_init_kwargs,
    )

from_quantized classmethod

from_quantized(quant_path, quant_filename='', max_seq_len=2048, trust_remote_code=True, fuse_layers=True, use_exllama=False, use_exllama_v2=False, use_ipex=False, batch_size=1, safetensors=True, device_map='balanced', max_memory=None, offload_folder=None, download_kwargs=None, **config_kwargs)

PARAMETER	DESCRIPTION
quant_path
quant_filename	DEFAULT: ''
max_seq_len	DEFAULT: 2048
trust_remote_code	DEFAULT: True
fuse_layers	DEFAULT: True
use_exllama	DEFAULT: False
use_exllama_v2	DEFAULT: False
use_ipex	DEFAULT: False
batch_size	DEFAULT: 1
safetensors	DEFAULT: True
device_map	DEFAULT: 'balanced'
max_memory	DEFAULT: None
offload_folder	DEFAULT: None
download_kwargs	DEFAULT: None
**config_kwargs	DEFAULT: {}

Source code in awq/models/auto.py

@classmethod
def from_quantized(
    self,
    quant_path,
    quant_filename="",
    max_seq_len=2048,
    trust_remote_code=True,
    fuse_layers=True,
    use_exllama=False,
    use_exllama_v2=False,
    use_ipex=False,
    batch_size=1,
    safetensors=True,
    device_map="balanced",
    max_memory=None,
    offload_folder=None,
    download_kwargs=None,
    **config_kwargs,
) -> BaseAWQForCausalLM:
    os.environ["AWQ_BATCH_SIZE"] = str(batch_size)
    model_type = check_and_get_model_type(quant_path, trust_remote_code)

    if config_kwargs.get("max_new_tokens") is not None:
        max_seq_len = config_kwargs["max_new_tokens"]
        logging.warning(
            "max_new_tokens argument is deprecated... gracefully "
            "setting max_seq_len=max_new_tokens."
        )

    return AWQ_CAUSAL_LM_MODEL_MAP[model_type].from_quantized(
        quant_path,
        model_type,
        quant_filename,
        max_seq_len,
        trust_remote_code=trust_remote_code,
        fuse_layers=fuse_layers,
        use_exllama=use_exllama,
        use_exllama_v2=use_exllama_v2,
        use_ipex=use_ipex,
        safetensors=safetensors,
        device_map=device_map,
        max_memory=max_memory,
        offload_folder=offload_folder,
        download_kwargs=download_kwargs,
        **config_kwargs,
    )

awq.models.base.BaseAWQForCausalLM

BaseAWQForCausalLM(model, model_type, is_quantized, config, quant_config, processor)

Bases: Module

The base model for all AutoAWQ models.

PARAMETER	DESCRIPTION
model	The pretrained or quantized model. TYPE: PreTrainedModel
model_type	The model type, found in config.json. TYPE: str
is_quantized	Indicates if the current model is quantized. TYPE: bool
config	The config of the model. TYPE: PretrainedConfig
quant_config	The quantization config of the model. TYPE: AwqConfig
processor	An optional processor, e.g. for vision models. TYPE: BaseImageProcessor

Source code in awq/models/base.py

def __init__(
    self,
    model: Annotated[PreTrainedModel, Doc("The pretrained or quantized model.")],
    model_type: Annotated[str, Doc("The model type, found in config.json.")],
    is_quantized: Annotated[
        bool, Doc("Indicates if the current model is quantized.")
    ],
    config: Annotated[PretrainedConfig, Doc("The config of the model.")],
    quant_config: Annotated[
        AwqConfig, Doc("The quantization config of the model.")
    ],
    processor: Annotated[
        BaseImageProcessor, Doc("An optional processor, e.g. for vision models.")
    ],
):
    """The base model for all AutoAWQ models."""
    super().__init__()
    self.model: PreTrainedModel = model
    self.model_type: str = model_type
    self.is_quantized: bool = is_quantized
    self.search_result = None
    self.config: PretrainedConfig = config
    self.quant_config: AwqConfig = quant_config
    self.processor: BaseImageProcessor = processor

model instance-attribute

model = model

model_type instance-attribute

model_type = model_type

is_quantized instance-attribute

is_quantized = is_quantized

search_result instance-attribute

search_result = None

config instance-attribute

config = config

quant_config instance-attribute

quant_config = quant_config

processor instance-attribute

processor = processor

to

to(device)

A utility function for moving the model to a device.

PARAMETER	DESCRIPTION
device	The device to move your model to. TYPE: str

Source code in awq/models/base.py

def to(self, device: Annotated[str, Doc("The device to move your model to.")]):
    """A utility function for moving the model to a device."""
    return self.model.to(device)

forward

forward(*args, **kwargs)

A forward function that mimics the torch forward.

PARAMETER	DESCRIPTION
*args	DEFAULT: ()
**kwargs	DEFAULT: {}

Source code in awq/models/base.py

def forward(self, *args, **kwargs):
    """A forward function that mimics the torch forward."""
    return self.model(*args, **kwargs)

generate

generate(*args, **kwargs)

A generate function that mimics the HF generate function.

PARAMETER	DESCRIPTION
*args	DEFAULT: ()
**kwargs	DEFAULT: {}

Source code in awq/models/base.py

def generate(self, *args, **kwargs):
    """A generate function that mimics the HF generate function."""
    with torch.inference_mode():
        return self.model.generate(*args, **kwargs)

quantize

quantize(tokenizer=None, quant_config={}, calib_data='pileval', split='train', text_column='text', duo_scaling=True, export_compatible=False, apply_clip=True, n_parallel_calib_samples=None, max_calib_samples=128, max_calib_seq_len=512, max_chunk_memory=1024 * 1024 * 1024, quantizer_cls=AwqQuantizer, **kwargs)

The main quantization function that you can use to quantize your model.

Example:

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

model_path = "..."
model = AutoAWQForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" }
model.quantize(tokenizer, quant_config)

PARAMETER	DESCRIPTION
tokenizer	The tokenizer to use for quantization. TYPE: PreTrainedTokenizer DEFAULT: None
quant_config	The quantization config you want to use. TYPE: Dict DEFAULT: {}
calib_data	The calibration dataset. Either a string pointing to Huggingface or a list of preloaded examples. TYPE: Union[str, List[str]] DEFAULT: 'pileval'
split	The split of calib_data. TYPE: str DEFAULT: 'train'
text_column	The text column of calib_data. TYPE: str DEFAULT: 'text'
duo_scaling	Whether to scale using both w/x or just x. TYPE: bool DEFAULT: True
export_compatible	This argument avoids real quantization by only applying the scales without quantizing down to FP16. TYPE: bool DEFAULT: False
apply_clip	Whether to apply clipping to the model during quantization. Some models may perform better with this set to False. TYPE: bool DEFAULT: True
n_parallel_calib_samples	The number of parallel samples to run through the model. A high number of parallel samples can result in OOM during quantization if max_calib_samples is high enough. If None, runs through all samples at the same time. You can set this to a low number for more memory efficient quantization. TYPE: int DEFAULT: None
max_calib_samples	The maximum number of samples to run through the model. TYPE: int DEFAULT: 128
max_calib_seq_len	The maximum sequence length of the calibration dataset. Discard samples greater than max_calib_seq_len. TYPE: int DEFAULT: 512
max_chunk_memory	The loss computation and per-channel mean is optimized into chunked computations. Adjust this parameter to increase or decrease memory usage for these computations. Default is 1GB (1024 * 1024 * 1024). TYPE: int DEFAULT: 1024 * 1024 * 1024
quantizer_cls	If you want to customize the quantization class, you can use AwqQuantizer as a base class. TYPE: AwqQuantizer DEFAULT: AwqQuantizer
**kwargs	DEFAULT: {}

Source code in awq/models/base.py

@torch.no_grad()
def quantize(
    self,
    tokenizer: Annotated[
        PreTrainedTokenizer, Doc("The tokenizer to use for quantization.")
    ] = None,
    quant_config: Annotated[
        Dict, Doc("The quantization config you want to use.")
    ] = {},
    calib_data: Annotated[
        Union[str, List[str]],
        Doc(
            "The calibration dataset. Either a string pointing to Huggingface or a list of preloaded examples."
        ),
    ] = "pileval",
    split: Annotated[str, Doc("The split of calib_data.")] = "train",
    text_column: Annotated[str, Doc("The text column of calib_data.")] = "text",
    duo_scaling: Annotated[
        bool, Doc("Whether to scale using both w/x or just x.")
    ] = True,
    export_compatible: Annotated[
        bool,
        Doc(
            "This argument avoids real quantization by only applying the scales without quantizing down to FP16."
        ),
    ] = False,
    apply_clip: Annotated[
        bool,
        Doc(
            "Whether to apply clipping to the model during quantization. Some models may perform better with this set to False."
        ),
    ] = True,
    n_parallel_calib_samples: Annotated[
        int,
        Doc(
            "The number of parallel samples to run through the model. "
            "A high number of parallel samples can result in OOM during quantization if max_calib_samples is high enough. "
            "If None, runs through all samples at the same time. "
            "You can set this to a low number for more memory efficient quantization."
        ),
    ] = None,
    max_calib_samples: Annotated[
        int, Doc("The maximum number of samples to run through the model.")
    ] = 128,
    max_calib_seq_len: Annotated[
        int,
        Doc(
            "The maximum sequence length of the calibration dataset. Discard samples greater than max_calib_seq_len."
        ),
    ] = 512,
    max_chunk_memory: Annotated[
        int,
        Doc(
            "The loss computation and per-channel mean is optimized into chunked computations."
            " Adjust this parameter to increase or decrease memory usage for these computations."
            " Default is 1GB (1024 * 1024 * 1024)."
        ),
    ] = 1024
    * 1024
    * 1024,
    quantizer_cls: Annotated[
        AwqQuantizer,
        Doc("If you want to customize the quantization class, you can use AwqQuantizer as a base class.")
    ] = AwqQuantizer,
    **kwargs,
):
    """
    The main quantization function that you can use to quantize your model.

    Example:

    ```python
    from awq import AutoAWQForCausalLM
    from transformers import AutoTokenizer

    model_path = "..."
    model = AutoAWQForCausalLM.from_pretrained(model_path)
    tokenizer = AutoTokenizer.from_pretrained(model_path)

    quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" }
    model.quantize(tokenizer, quant_config)
    ```
    """
    self.quant_config: AwqConfig = AwqConfig.from_dict(quant_config)

    if hasattr(self, "modules_to_not_convert"):
        self.quant_config.modules_to_not_convert = self.modules_to_not_convert

    self.quantizer = quantizer_cls(
        self,
        self.model,
        tokenizer,
        self.quant_config.w_bit,
        self.quant_config.q_group_size,
        self.quant_config.zero_point,
        self.quant_config.version,
        calib_data,
        split,
        text_column,
        duo_scaling,
        modules_to_not_convert=self.quant_config.modules_to_not_convert,
        export_compatible=export_compatible,
        apply_clip=apply_clip,
        n_parallel_calib_samples=n_parallel_calib_samples,
        max_calib_samples=max_calib_samples,
        max_calib_seq_len=max_calib_seq_len,
        max_chunk_memory=max_chunk_memory,
        **kwargs,
    )
    self.quantizer.quantize()

    self.is_quantized = True

pack

pack()

A utility function for the following scenario. Note that save_quantized will overwrite existing weights if you use the same quant_path.

Example:

model.quantize(
    tokenizer,
    quant_config=quant_config,
    export_compatible=True
)
model.save_quantized(...)  # produces GGUF/other compat weights
model.pack(...) # makes the model CUDA compat
model.save_quantized(...)  # produces CUDA compat weights

Source code in awq/models/base.py

@torch.no_grad()
def pack(self):
    """
    A utility function for the following scenario. Note that save_quantized will
    overwrite existing weights if you use the same quant_path.

    Example:

    ```python
    model.quantize(
        tokenizer,
        quant_config=quant_config,
        export_compatible=True
    )
    model.save_quantized(...)  # produces GGUF/other compat weights
    model.pack(...) # makes the model CUDA compat
    model.save_quantized(...)  # produces CUDA compat weights
    ```
    """
    self.quantizer.pack()

fuse_layers staticmethod

fuse_layers(model)

PARAMETER	DESCRIPTION
model

Source code in awq/models/base.py

@staticmethod
def fuse_layers(model):
    pass

save_quantized

save_quantized(save_dir, safetensors=True, shard_size='5GB')

PARAMETER	DESCRIPTION
save_dir	The directory to save your model to. TYPE: str
safetensors	Whether to save the model as safetensors or torch files. TYPE: bool DEFAULT: True
shard_size	The shard size for sharding large models into multiple chunks. TYPE: str DEFAULT: '5GB'

Source code in awq/models/base.py

def save_quantized(
    self,
    save_dir: Annotated[str, Doc("The directory to save your model to.")],
    safetensors: Annotated[
        bool, Doc("Whether to save the model as safetensors or torch files.")
    ] = True,
    shard_size: Annotated[
        str, Doc("The shard size for sharding large models into multiple chunks.")
    ] = "5GB",
):
    save_dir = save_dir[:-1] if save_dir[-1] == "/" else save_dir

    # Save model
    class EmptyModule(nn.Module):
        def __init__(self):
            super(EmptyModule, self).__init__()

        def forward(self, x):
            return x

    # Save model and config files with empty state dict
    self.model.config.quantization_config = self.quant_config.to_transformers_dict()
    self.model.generation_config.do_sample = True
    self.model.save_pretrained(save_dir, state_dict=EmptyModule().state_dict())

    # Vision transformers have a processor
    if self.processor is not None:
        self.processor.save_pretrained(save_dir)

    # Remove empty state dict
    default_paths = [
        f"{save_dir}/model.safetensors",
        f"{save_dir}/pytorch_model.bin",
    ]
    for path in default_paths:
        if os.path.exists(path):
            os.remove(path)

    save_torch_state_dict(
        state_dict=self.model.state_dict(),
        save_directory=save_dir,
        max_shard_size=shard_size,
        safe_serialization=safetensors,
        force_contiguous=True,
    )

from_pretrained classmethod

from_pretrained(model_path, model_type, torch_dtype=float16, trust_remote_code=True, safetensors=True, device_map='auto', download_kwargs=None, **model_init_kwargs)

A method for initialization of pretrained models, usually in FP16.

PARAMETER	DESCRIPTION
model_path	A Huggingface path or local path to a model. TYPE: str
model_type	The model type, loaded from config.json. TYPE: str
torch_dtype	The dtype to load the model as. May not work with other values than float16. TYPE: dtype DEFAULT: float16
trust_remote_code	Useful for Huggingface repositories that have not been integrated into transformers yet. TYPE: bool DEFAULT: True
safetensors	Whether to download/load safetensors instead of torch weights. TYPE: bool DEFAULT: True
device_map	A device map that will be passed onto the model loading method from transformers. TYPE: Union[str, Dict] DEFAULT: 'auto'
download_kwargs	Used for configure download model TYPE: Dict DEFAULT: None
**model_init_kwargs	Additional kwargs that are passed to the model during initialization. TYPE: Dict DEFAULT: {}

Source code in awq/models/base.py

@classmethod
def from_pretrained(
    self,
    model_path: Annotated[str, Doc("A Huggingface path or local path to a model.")],
    model_type: Annotated[str, Doc("The model type, loaded from config.json.")],
    torch_dtype: Annotated[
        torch.dtype,
        Doc(
            "The dtype to load the model as. May not work with other values than float16."
        ),
    ] = torch.float16,
    trust_remote_code: Annotated[
        bool,
        Doc(
            "Useful for Huggingface repositories that have not been integrated into transformers yet."
        ),
    ] = True,
    safetensors: Annotated[
        bool, Doc("Whether to download/load safetensors instead of torch weights.")
    ] = True,
    device_map: Annotated[
        Union[str, Dict],
        Doc(
            "A device map that will be passed onto the model loading method from transformers."
        ),
    ] = "auto",
    download_kwargs: Annotated[
        Dict,
        Doc("Used for configure download model"),
    ] = None,
    **model_init_kwargs: Annotated[
        Dict,
        Doc(
            "Additional kwargs that are passed to the model during initialization."
        ),
    ],
):
    """A method for initialization of pretrained models, usually in FP16."""
    # Get weights path and quant config
    model_weights_path, config, quant_config = self._load_config(
        self,
        model_path,
        "",
        safetensors,
        trust_remote_code=trust_remote_code,
        download_kwargs=download_kwargs,
    )

    target_cls_name = TRANSFORMERS_AUTO_MAPPING_DICT[config.model_type]
    target_cls = getattr(transformers, target_cls_name)

    processor = None
    if target_cls_name == "AutoModelForVision2Seq":
        processor = AutoProcessor.from_pretrained(model_weights_path)

    # If not quantized, must load with AutoModelForCausalLM
    model = target_cls.from_pretrained(
        model_weights_path,
        trust_remote_code=trust_remote_code,
        torch_dtype=torch_dtype,
        use_safetensors=safetensors,
        device_map=device_map,
        **model_init_kwargs,
    )

    model.eval()

    return self(
        model,
        model_type,
        is_quantized=False,
        config=config,
        quant_config=quant_config,
        processor=processor,
    )

from_quantized classmethod

from_quantized(model_path, model_type, model_filename='', max_seq_len=None, torch_dtype=float16, trust_remote_code=True, safetensors=True, fuse_layers=True, use_exllama=False, use_exllama_v2=False, use_ipex=False, device_map='balanced', max_memory=None, offload_folder=None, download_kwargs=None, **config_kwargs)

A method for initialization of a quantized model, usually in INT4.

PARAMETER	DESCRIPTION
model_path	A Huggingface path or local path to a model. TYPE: str
model_type	The model type, loaded from config.json. TYPE: str
model_filename	Load a specific model's filename by specifying this argument. TYPE: str DEFAULT: ''
max_seq_len	The maximum sequence cached sequence length of the model. Larger values may increase loading time and memory usage. TYPE: int DEFAULT: None
torch_dtype	The dtype to load the model as. May not work with other values than float16. TYPE: dtype DEFAULT: float16
trust_remote_code	Useful for Huggingface repositories that have not been integrated into transformers yet. TYPE: bool DEFAULT: True
safetensors	Whether to download/load safetensors instead of torch weights. TYPE: bool DEFAULT: True
fuse_layers	Whether to use fused/optimized combination of layers for increased speed. TYPE: bool DEFAULT: True
use_exllama	Whether to map the weights to ExLlamaV1 kernels. TYPE: bool DEFAULT: False
use_exllama_v2	Whether to map the weights to ExLlamaV2 kernels. TYPE: bool DEFAULT: False
use_ipex	Whether to map the weights to ipex kernels for CPU and XPU device. TYPE: bool DEFAULT: False
device_map	A device map that will be passed onto the model loading method from transformers. TYPE: Union[str, Dict] DEFAULT: 'balanced'
max_memory	A dictionary device identifier to maximum memory which will be passed onto the model loading method from transformers. For example：{0: "4GB",1: "10GB" TYPE: Dict[Union[int, str], Union[int, str]] DEFAULT: None
offload_folder	The folder ot offload the model to. TYPE: str DEFAULT: None
download_kwargs	Used for configure download model TYPE: Dict DEFAULT: None
**config_kwargs	Additional kwargs that are passed to the config during initialization. TYPE: Dict DEFAULT: {}

Source code in awq/models/base.py

@classmethod
def from_quantized(
    self,
    model_path: Annotated[str, Doc("A Huggingface path or local path to a model.")],
    model_type: Annotated[str, Doc("The model type, loaded from config.json.")],
    model_filename: Annotated[
        str, Doc("Load a specific model's filename by specifying this argument.")
    ] = "",
    max_seq_len: Annotated[
        int,
        Doc(
            "The maximum sequence cached sequence length of the model. Larger values may increase loading time and memory usage."
        ),
    ] = None,
    torch_dtype: Annotated[
        torch.dtype,
        Doc(
            "The dtype to load the model as. May not work with other values than float16."
        ),
    ] = torch.float16,
    trust_remote_code: Annotated[
        bool,
        Doc(
            "Useful for Huggingface repositories that have not been integrated into transformers yet."
        ),
    ] = True,
    safetensors: Annotated[
        bool, Doc("Whether to download/load safetensors instead of torch weights.")
    ] = True,
    fuse_layers: Annotated[
        bool,
        Doc(
            "Whether to use fused/optimized combination of layers for increased speed."
        ),
    ] = True,
    use_exllama: Annotated[
        bool, Doc("Whether to map the weights to ExLlamaV1 kernels.")
    ] = False,
    use_exllama_v2: Annotated[
        bool, Doc("Whether to map the weights to ExLlamaV2 kernels.")
    ] = False,
    use_ipex: Annotated[
        bool, Doc("Whether to map the weights to ipex kernels for CPU and XPU device.")
    ] = False,
    device_map: Annotated[
        Union[str, Dict],
        Doc(
            "A device map that will be passed onto the model loading method from transformers."
        ),
    ] = "balanced",
    max_memory: Annotated[
        Dict[Union[int, str], Union[int, str]],
        Doc(
            'A dictionary device identifier to maximum memory which will be passed onto the model loading method from transformers. For example：{0: "4GB",1: "10GB"'
        ),
    ] = None,
    offload_folder: Annotated[
        str,
        Doc("The folder ot offload the model to."),
    ] = None,
    download_kwargs: Annotated[
        Dict,
        Doc("Used for configure download model"),
    ] = None,
    **config_kwargs: Annotated[
        Dict,
        Doc(
            "Additional kwargs that are passed to the config during initialization."
        ),
    ],
):
    """A method for initialization of a quantized model, usually in INT4."""
    # [STEP 1-2] Load weights path and configs
    model_weights_path, config, quant_config = self._load_config(
        self,
        model_path,
        model_filename,
        safetensors,
        trust_remote_code,
        max_seq_len=max_seq_len,
        download_kwargs=download_kwargs,
        **config_kwargs,
    )

    target_cls_name = TRANSFORMERS_AUTO_MAPPING_DICT[config.model_type]
    target_cls = getattr(transformers, target_cls_name)

    # [STEP 3] Load model
    with init_empty_weights():
        model = target_cls.from_config(
            config=config,
            torch_dtype=torch_dtype,
            trust_remote_code=trust_remote_code,
        )

    best_device = get_best_device()
    use_ipex = use_ipex or best_device in ["cpu", "xpu:0"]
    if use_ipex and not ipex_available:
        raise ImportError(
            "Please install intel_extension_for_pytorch with "
            "`pip install intel_extension_for_pytorch` for 'ipex' kernel!"
        )
    # Prepare WQLinear layers, replace nn.Linear
    self._load_quantized_modules(
        self,
        model,
        quant_config,
        quant_config.version,
        use_exllama=use_exllama,
        use_exllama_v2=use_exllama_v2,
        use_ipex=use_ipex,
    )

    model.tie_weights()

    # loads the weights into modules and distributes
    # across available devices automatically
    load_checkpoint_and_dispatch(
        model,
        checkpoint=model_weights_path,
        device_map=device_map,
        max_memory=max_memory,
        no_split_module_classes=[self.layer_type],
        offload_folder=offload_folder,
        dtype=torch_dtype,
    )

    # Dispath to devices
    awq_ext, msg = try_import("awq_ext")
    if fuse_layers:
        if best_device in ["mps", "cuda:0"] and awq_ext is None:
            warnings.warn("Skipping fusing modules because AWQ extension is not installed." + msg)
        else:
            self.fuse_layers(model)

    if use_ipex:
        # repack qweight to match the ipex kernel.
        model = ipex_post_init(model)
    elif quant_config.version == "marlin":
        model = marlin_post_init(model)
    elif use_exllama:
        # creates q4 handle
        model = exllama_post_init(model)
    elif use_exllama_v2:
        # creates q4 handle and allocates scratch spaces wrt max_input_len and max_batch_size
        model = exllamav2_post_init(
            model,
            max_input_len=max_seq_len or 2048,
            max_batch_size=int(os.getenv("AWQ_BATCH_SIZE", 1)),
        )

    model.eval()

    return self(
        model,
        model_type,
        is_quantized=True,
        config=config,
        quant_config=quant_config,
        processor=None,
    )