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# Quantization LLM Template

class quark.torch.quantization.config.template.QuantizationScheme(config: QLayerConfig)

  Abstract base class for quantization schemes.

class quark.torch.quantization.config.template.Int4WeightOnlyScheme(group_size: int)

  Scheme for INT4 weight-only quantization.

class quark.torch.quantization.config.template.Int4WeightOnlyPerChannelScheme

  Scheme for INT4 weight-only per-channel quantization.

class quark.torch.quantization.config.template.Uint4WeightOnlyScheme(group_size: int)

  Scheme for UINT4 weight-only quantization.

class quark.torch.quantization.config.template.Uint4WeightOnlyPerChannelScheme

  Scheme for UINT4 weight-only per-channel quantization.

class quark.torch.quantization.config.template.Int8Scheme

  Scheme for INT8 weight and activation input quantization.

class quark.torch.quantization.config.template.FP8Scheme

  Scheme for FP8 quantization (e4m3 format).

class quark.torch.quantization.config.template.MXFP4Scheme

  Scheme for MXFP4 quantization.

class quark.torch.quantization.config.template.MXFP6E3M2Scheme

  Scheme for MXFP6E3M2 quantization.

class quark.torch.quantization.config.template.MXFP6E2M3Scheme

  Scheme for MXFP6E2M3 quantization.

class quark.torch.quantization.config.template.MXFP4_MXFP6E2M3Scheme

  Scheme for MXFP4 weight and MXFP6E2M3 activation input quantization.

class quark.torch.quantization.config.template.MX6Scheme

  Scheme for MX6 quantization.

class quark.torch.quantization.config.template.BFP16Scheme

  Scheme for BFP16 quantization.

class quark.torch.quantization.config.template.MXFP4_FP8Scheme

  Scheme for MXFP4 weight and FP8 activation input quantization.

class quark.torch.quantization.config.template.PTPCFP8Scheme

  Scheme for PTPC FP8 quantization (Dynamic activation per-token quantization, weight quantization per-channel).

  Uses FP8 Per-Channel Static for weights and FP8 Per-Token Dynamic for activations.

class quark.torch.quantization.config.template.QuantizationSchemeCollection

  Collection for quantization schemes.

  register_scheme(scheme_name: str, scheme: QuantizationScheme) → None

    Register a quantization scheme.

  unregister_scheme(scheme_name: str) → None

    Unregister a quantization scheme.

  get_supported_schemes() → list[str]

    Get list of supported quantization schemes.

  get_scheme(scheme_name: str) → QuantizationScheme

    Get a quantization scheme by name.

class quark.torch.quantization.config.template.LLMTemplate(model_type: str, kv_layers_name: list[str] | None = None, q_layer_name: str | list[str] | None = None, exclude_layers_name: list[str] = [], awq_config: AWQConfig | None = None, gptq_config: GPTQConfig | None = None, gptaq_config: GPTAQConfig | None = None, qronos_config: QronosConfig | None = None, smoothquant_config: SmoothQuantConfig | None = None, autosmoothquant_config: AutoSmoothQuantConfig | None = None, rotation_config: RotationConfig | None = None)

  A configuration template that defines how to quantize specific types of LLM models.

  Each LLM architecture (like llama, qwen, deepseek, etc.) has its own unique structure and naming patterns for layers. This template allows specifying those patterns and quantization settings in a reusable way.

  Parameters:

    - model_type (str) – Type of the LLM model.

    - kv_layers_name (List[str]) – List of k_proj and v_proj layer name patterns to match. Default is `None`.

    - q_layer_name (Union[str, List[str]]) – q_proj layer name pattern to match. Default is `None`.

    - exclude_layers_name (List[str]) – List of layer name patterns to exclude from quantization. Default is `[]`.

    - awq_config (AWQConfig) – Configuration for AWQ algorithm. Default is `None`.

    - gptq_config (GPTQConfig) – Configuration for GPTQ algorithm. Default is `None`.

    - smoothquant_config (SmoothQuantConfig) – Configuration for SmoothQuant algorithm. Default is `None`.

    - autosmoothquant_config (AutoSmoothQuantConfig) – Configuration for AutoSmoothQuant algorithm. Default is `None`.

    - rotation_config (RotationConfig) – Configuration for Rotation algorithm. Default is `None`.

  Note:
    - The quantization schemes supported by the template are:

        - fp8

        - ptpc_fp8

        - int4_wo_32

        - int4_wo_64

        - int4_wo_128

        - int4_wo_per_channel

        - uint4_wo_32

        - uint4_wo_64

        - uint4_wo_128

        - uint4_wo_per_channel

        - int8

        - mxfp4

        - mxfp6_e3m2

        - mxfp6_e2m3

        - mx6

        - bfp16

    - The quantization algorithms supported by the template are:

        - awq

        - gptq

        - smoothquant

        - autosmoothquant

        - rotation

    - The KV cache schemes supported by the template are:

        - fp8

    - The attention schemes supported by the template are:

        - fp8

  Creating a Custom Template:

  To create a custom template for a new model type, you can define layer name patterns and algorithm configurations specific to your model architecture. Take moonshotai/Kimi-K2-Instruct as an example:

      from quark.torch import LLMTemplate

      # Create a new template
      template = LLMTemplate(
          model_type="kimi_k2",
          kv_layers_name=["*kv_b_proj"],
          exclude_layers_name=["lm_head"]
      )

      # Register the template to LLMTemplate class (optional, if you want to use the template in other places)
      LLMTemplate.register_template(template)

  classmethod list_available() → list[str]

    List all available model names of registered templates.

    Returns:

      List of template names.

    Return type:

      List[str]

    Example:

        from quark.torch import LLMTemplate

        templates = LLMTemplate.list_available()
        print(templates)  # ['llama', 'opt', 'gpt2', ...]

  classmethod register_template(template: LLMTemplate) → None

    Register a template.

    Parameters:

      template (LLMTemplate) – The template to register.

    Example:

        from quark.torch import LLMTemplate

        # Create template
        template = LLMTemplate(
            model_type="llama",
            kv_layers_name=["*k_proj", "*v_proj"],
            q_layer_name="*q_proj",
            exclude_layers_name=["lm_head"],
        )

        # Register template
        LLMTemplate.register_template(template)

  classmethod get(model_type: str) → LLMTemplate

    Get a template by model type.

    Parameters:

      model_type (str) – Type of the model. It is obtained from the original LLM HuggingFace model’s `model.config.model_type` attribute. When the model_type field is not defined, the `model.config.architecture[0]` is assigned as the model_type..

    Available model types:

    > - chatglm

    > - cohere

    > - dbrx

    > - deepseek

    > - deepseek_v2

    > - deepseek_v3

    > - gemma2

    > - gemma3

    > - gemma3_text

    > - gptj

    > - gpt_oss

    > - grok-1

    > - instella

    > - llama

    > - llama4

    > - mistral

    > - mixtral

    > - mllama

    > - olmo

    > - opt

    > - phi

    > - phi3

    > - qwen

    > - qwen2

    > - qwen2_moe

    > - qwen3

    > - qwen3_next

    > - qwen3_moe

    > - qwen3_vl_moe

    Returns:

      The template object.

    Return type:

      LLMTemplate

    Example:

        from quark.torch import LLMTemplate

        template = LLMTemplate.get("llama")
        print(template)

  classmethod register_scheme(scheme_name: str, config: QLayerConfig) → None

    Register a new quantization scheme for LLMTemplate class.

    Parameters:

      - scheme_name (str) – Name of the scheme.

      - config (QLayerConfig) – Configuration for the scheme.

    Example:

        # Register a new quantization scheme ``int8_wo (int8 weight-only)`` to the template
        from quark.torch import LLMTemplate
        from quark.torch.quantization.config.config import Int8PerTensorSpec, QLayerConfig

        quant_spec = Int8PerTensorSpec(observer_method="min_max", symmetric=True, scale_type="float",
                                       round_method="half_even", is_dynamic=False).to_quantization_spec()
        global_config = QLayerConfig(weight=quant_spec)

        LLMTemplate.register_scheme("int8_wo", config=global_config)

  classmethod unregister_scheme(scheme_name: str) → None

    Unregister a quantization scheme.

    Parameters:

      scheme_name (str) – Name of the scheme to unregister.

    Example:

        from quark.torch import LLMTemplate

        LLMTemplate.unregister_scheme("int8")

  classmethod get_supported_schemes() → list[str]

    Get list of supported quantization schemes.

  get_config(scheme: str, algorithm: str | list[str] | None = None, kv_cache_scheme: str | None = None, min_kv_scale: float = 0.0, attention_scheme: str | None = None, layer_config: dict[str, str] | None = None, layer_type_config: dict[type[Module], str] | None = None, exclude_layers: list[str] | None = None, algo_configs: dict[str, AlgoConfig] | None = None) → Config

    Create a quantization configuration based on the provided parameters.

    Parameters:

      - scheme (str) – Name of the quantization scheme.

      - algorithm (Optional[Union[str, List[str]]]) – Name or list of names of quantization algorithms to apply.

      - kv_cache_scheme (Optional[str]) – Name of the KV cache quantization scheme.

      - min_kv_scale (float) – Minimum value of KV Cache scale.

      - attention_scheme (Optional[str]) – Name of the attention quantization scheme.

      - layer_config (Optional[Dict[str, str]]) – Dictionary of layer name patterns and quantization scheme names.

      - layer_type_config (Optional[Dict[Type[nn.Module], str]]) – Dictionary of layer types and quantization scheme names.

      - exclude_layers (Optional[List[str]]) – List of layer names to exclude from quantization.

      - algo_configs (Optional[Dict[str, AlgoConfig]]) – Dictionary of algorithm names to their configurations.

    Example:

        from quark.torch import LLMTemplate

        template = LLMTemplate.get("llama")
        config = template.get_config(scheme="fp8", kv_cache_scheme="fp8")