Parameters

• weights (str, optional, defaults to "int8") — The target dtype for the weights after quantization. Supported values are (“float8”,“int8”,“int4”,“int2”)
• activations (str, optional) — The target dtype for the activations after quantization. Supported values are (None,“int8”,“float8”)
• modules_to_not_convert (list, optional, default to None) — The list of modules to not quantize, useful for quantizing models that explicitly require to have some modules left in their original precision (e.g. Whisper encoder, Llava encoder, Mixtral gate layers).

Parameters

• in_group_size (int, optional, defaults to 8) — The group size along the input dimension.
• out_group_size (int, optional, defaults to 1) — The group size along the output dimension. It’s recommended to always use 1.
• num_codebooks (int, optional, defaults to 1) — Number of codebooks for the Additive Quantization procedure.
• nbits_per_codebook (int, optional, defaults to 16) — Number of bits encoding a single codebook vector. Codebooks size is 2**nbits_per_codebook.
• linear_weights_not_to_quantize (Optional[List[str]], optional) — List of full paths of nn.Linear weight parameters that shall not be quantized.
• kwargs (Dict[str, Any], optional) — Additional parameters from which to initialize the configuration object.

Parameters

• enable_proxy_error (bool, optional, defaults to False) — calculate proxy error for each layer
• config_for_layers (Dict, optional, defaults to {}) — quantization params for each layer
• shared_layer_config (Dict, optional, defaults to {}) — shared quantization params among layers
• modules_to_not_convert (list, optional, default to None) — The list of modules to not quantize, useful for quantizing models that explicitly require to have some modules left in their original precision (e.g. Whisper encoder, Llava encoder, Mixtral gate layers).
• kwargs (Dict[str, Any], optional) — Additional parameters from which to initialize the configuration object.

Parameters

• bits (int, optional, defaults to 4) — The number of bits to quantize to.
• group_size (int, optional, defaults to 128) — The group size to use for quantization. Recommended value is 128 and -1 uses per-column quantization.
• zero_point (bool, optional, defaults to True) — Whether to use zero point quantization.
• version (AWQLinearVersion, optional, defaults to AWQLinearVersion.GEMM) — The version of the quantization algorithm to use. GEMM is better for big batch_size (e.g. >= 8) otherwise, GEMV is better (e.g. < 8 ). GEMM models are compatible with Exllama kernels.
• backend (AwqBackendPackingMethod, optional, defaults to AwqBackendPackingMethod.AUTOAWQ) — The quantization backend. Some models might be quantized using llm-awq backend. This is useful for users that quantize their own models using llm-awq library.
• do_fuse (bool, optional, defaults to False) — Whether to fuse attention and mlp layers together for faster inference
• fuse_max_seq_len (int, optional) — The Maximum sequence length to generate when using fusing.
• modules_to_fuse (dict, optional, default to None) — Overwrite the natively supported fusing scheme with the one specified by the users.
• modules_to_not_convert (list, optional, default to None) — The list of modules to not quantize, useful for quantizing models that explicitly require to have some modules left in their original precision (e.g. Whisper encoder, Llava encoder, Mixtral gate layers). Note you cannot quantize directly with transformers, please refer to AutoAWQ documentation for quantizing HF models.
• exllama_config (Dict[str, Any], optional) — You can specify the version of the exllama kernel through the version key, the maximum sequence length through the max_input_len key, and the maximum batch size through the max_batch_size key. Defaults to {"version": 2, "max_input_len": 2048, "max_batch_size": 8} if unset.

Parameters

• weights (str, optional, defaults to "int8") — The target dtype for the weights. Supported value is only “int8”
• modules_to_not_convert (list, optional, default to None) — The list of modules to not quantize, useful for quantizing models that explicitly require to have some modules left in their original precision.

Parameters

• bits (int) — The number of bits to quantize to, supported numbers are (2, 3, 4, 8).
• tokenizer (str or PreTrainedTokenizerBase, optional) — The tokenizer used to process the dataset. You can pass either:
  • A custom tokenizer object.
  • A string, the model id of a predefined tokenizer hosted inside a model repo on huggingface.co.
  • A path to a directory containing vocabulary files required by the tokenizer, for instance saved using the save_pretrained() method, e.g., ./my_model_directory/.
• dataset (Union[List[str]], optional) — The dataset used for quantization. You can provide your own dataset in a list of string or just use the original datasets used in GPTQ paper [‘wikitext2’,‘c4’,‘c4-new’]
• group_size (int, optional, defaults to 128) — The group size to use for quantization. Recommended value is 128 and -1 uses per-column quantization.
• damp_percent (float, optional, defaults to 0.1) — The percent of the average Hessian diagonal to use for dampening. Recommended value is 0.1.
• desc_act (bool, optional, defaults to False) — Whether to quantize columns in order of decreasing activation size. Setting it to False can significantly speed up inference but the perplexity may become slightly worse. Also known as act-order.
• sym (bool, optional, defaults to True) — Whether to use symetric quantization.
• true_sequential (bool, optional, defaults to True) — Whether to perform sequential quantization even within a single Transformer block. Instead of quantizing the entire block at once, we perform layer-wise quantization. As a result, each layer undergoes quantization using inputs that have passed through the previously quantized layers.
• use_cuda_fp16 (bool, optional, defaults to False) — Whether or not to use optimized cuda kernel for fp16 model. Need to have model in fp16.
• model_seqlen (int, optional) — The maximum sequence length that the model can take.
• block_name_to_quantize (str, optional) — The transformers block name to quantize. If None, we will infer the block name using common patterns (e.g. model.layers)
• module_name_preceding_first_block (List[str], optional) — The layers that are preceding the first Transformer block.
• batch_size (int, optional, defaults to 1) — The batch size used when processing the dataset
• pad_token_id (int, optional) — The pad token id. Needed to prepare the dataset when batch_size > 1.
• use_exllama (bool, optional) — Whether to use exllama backend. Defaults to True if unset. Only works with bits = 4.
• max_input_length (int, optional) — The maximum input length. This is needed to initialize a buffer that depends on the maximum expected input length. It is specific to the exllama backend with act-order.
• exllama_config (Dict[str, Any], optional) — The exllama config. You can specify the version of the exllama kernel through the version key. Defaults to {"version": 1} if unset.
• cache_block_outputs (bool, optional, defaults to True) — Whether to cache block outputs to reuse as inputs for the succeeding block.
• modules_in_block_to_quantize (List[List[str]], optional) — List of list of module names to quantize in the specified block. This argument is useful to exclude certain linear modules from being quantized. The block to quantize can be specified by setting block_name_to_quantize. We will quantize each list sequentially. If not set, we will quantize all linear layers. Example: modules_in_block_to_quantize =[["self_attn.k_proj", "self_attn.v_proj", "self_attn.q_proj"], ["self_attn.o_proj"]]. In this example, we will first quantize the q,k,v layers simultaneously since they are independent. Then, we will quantize self_attn.o_proj layer with the q,k,v layers quantized. This way, we will get better results since it reflects the real input self_attn.o_proj will get when the model is quantized.

Parameters

• load_in_8bit (bool, optional, defaults to False) — This flag is used to enable 8-bit quantization with LLM.int8().
• load_in_4bit (bool, optional, defaults to False) — This flag is used to enable 4-bit quantization by replacing the Linear layers with FP4/NF4 layers from bitsandbytes.
• llm_int8_threshold (float, optional, defaults to 6.0) — This corresponds to the outlier threshold for outlier detection as described in LLM.int8() : 8-bit Matrix Multiplication for Transformers at Scale paper: https://arxiv.org/abs/2208.07339 Any hidden states value that is above this threshold will be considered an outlier and the operation on those values will be done in fp16. Values are usually normally distributed, that is, most values are in the range [-3.5, 3.5], but there are some exceptional systematic outliers that are very differently distributed for large models. These outliers are often in the interval [-60, -6] or [6, 60]. Int8 quantization works well for values of magnitude ~5, but beyond that, there is a significant performance penalty. A good default threshold is 6, but a lower threshold might be needed for more unstable models (small models, fine-tuning).
• llm_int8_skip_modules (List[str], optional) — An explicit list of the modules that we do not want to convert in 8-bit. This is useful for models such as Jukebox that has several heads in different places and not necessarily at the last position. For example for CausalLM models, the last lm_head is kept in its original dtype.
• llm_int8_enable_fp32_cpu_offload (bool, optional, defaults to False) — This flag is used for advanced use cases and users that are aware of this feature. If you want to split your model in different parts and run some parts in int8 on GPU and some parts in fp32 on CPU, you can use this flag. This is useful for offloading large models such as google/flan-t5-xxl. Note that the int8 operations will not be run on CPU.
• llm_int8_has_fp16_weight (bool, optional, defaults to False) — This flag runs LLM.int8() with 16-bit main weights. This is useful for fine-tuning as the weights do not have to be converted back and forth for the backward pass.
• bnb_4bit_compute_dtype (torch.dtype or str, optional, defaults to torch.float32) — This sets the computational type which might be different than the input type. For example, inputs might be fp32, but computation can be set to bf16 for speedups.
• bnb_4bit_quant_type (str, optional, defaults to "fp4") — This sets the quantization data type in the bnb.nn.Linear4Bit layers. Options are FP4 and NF4 data types which are specified by fp4 or nf4.
• bnb_4bit_use_double_quant (bool, optional, defaults to False) — This flag is used for nested quantization where the quantization constants from the first quantization are quantized again.
• bnb_4bit_quant_storage (torch.dtype or str, optional, defaults to torch.uint8) — This sets the storage type to pack the quanitzed 4-bit prarams.
• kwargs (Dict[str, Any], optional) — Additional parameters from which to initialize the configuration object.

Parameters

• nbits (int, optional, defaults to 4) — Number of bits. Supported values are (8, 4, 3, 2, 1).
• group_size (int, optional, defaults to 64) — Group-size value. Supported values are any value that is divisble by weight.shape[axis]).
• view_as_float (bool, optional, defaults to False) — View the quantized weight as float (used in distributed training) if set to True.
• axis (Optional[int], optional) — Axis along which grouping is performed. Supported values are 0 or 1.
• dynamic_config (dict, optional) — Parameters for dynamic configuration. The key is the name tag of the layer and the value is a quantization config. If set, each layer specified by its id will use its dedicated quantization configuration.
• skip_modules (List[str], optional, defaults to ['lm_head']) — List of nn.Linear layers to skip.
• kwargs (Dict[str, Any], optional) — Additional parameters from which to initialize the configuration object.

Parameters

• activation_scale_ub (float, optional, defaults to 1200.0) — The activation scale upper bound. This is used when quantizing the input activation.
• modules_to_not_convert (list, optional, default to None) — The list of modules to not quantize, useful for quantizing models that explicitly require to have some modules left in their original precision.

Parameters

• config_groups (typing.Dict[str, typing.Union[ForwardRef('QuantizationScheme'), typing.List[str]]], optional) — dictionary mapping group name to a quantization scheme definition
• format (str, optional, defaults to "dense") — format the model is represented as. Set run_compressed True to execute model as the compressed format if not dense
• quantization_status (QuantizationStatus, optional, defaults to "initialized") — status of model in the quantization lifecycle, ie ‘initialized’, ‘calibration’, ‘frozen’
• kv_cache_scheme (typing.Union[QuantizationArgs, NoneType], optional) — specifies quantization of the kv cache. If None, kv cache is not quantized.
• global_compression_ratio (typing.Union[float, NoneType], optional) — 0-1 float percentage of model compression
• ignore (typing.Union[typing.List[str], NoneType], optional) — layer names or types to not quantize, supports regex prefixed by ‘re:’
• sparsity_config (typing.Dict[str, typing.Any], optional) — configuration for sparsity compression
• quant_method (str, optional, defaults to "compressed-tensors") — do not override, should be compressed-tensors
• run_compressed (bool, optional, defaults to True) — alter submodules (usually linear) in order to emulate compressed model execution if True, otherwise use default submodule

Parameters

• quant_type (str) — The type of quantization we want to use, currently supporting: int4_weight_only, int8_weight_only and int8_dynamic_activation_int8_weight.
• modules_to_not_convert (list, optional, default to None) — The list of modules to not quantize, useful for quantizing models that explicitly require to have some modules left in their original precision.
• kwargs (Dict[str, Any], optional) — The keyword arguments for the chosen type of quantization, for example, int4_weight_only quantization supports two keyword arguments group_size and inner_k_tiles currently. More API examples and documentation of arguments can be found in https://github.com/pytorch/ao/tree/main/torchao/quantization#other-available-quantization-techniques