Common

API 참조

Classes

RBLNDecoderOnlyModelForCausalLM

Bases: RBLNDecoderOnlyModel, RBLNDecoderOnlyGenerationMixin

A base class for decoder-only transformer models optimized for causal language modeling tasks on RBLN devices. This class serves as the foundation for various decoder-only architectures like GPT, LLaMA, etc.

The class provides core functionality for:

1. Converting pre-trained transformer models to RBLN-optimized format
2. Handling the compilation process for RBLN devices
3. Managing inference operations for causal language modeling

This class inherits from RBLNModel and implements specific methods required for decoder-only architectures and causal language modeling tasks.

Note

• This class is designed to be subclassed by specific model implementations (e.g., RBLNLlamaForCausalLM, RBLNGPT2LMHeadModel)
• Subclasses should implement model-specific conversion logic.
• The class handles RBLN-specific optimizations automatically during compilation

Functions

generate(input_ids, attention_mask=None, max_length=None, **kwargs)

The generate function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to generate text from the model.

Parameters:

Name	Type	Description	Default
input_ids	LongTensor	The input ids to the model.	required
attention_mask	Optional[LongTensor]	The attention mask to the model.	None
max_length	Optional[int]	The maximum length of the generated text.	None
kwargs		Additional arguments passed to the generate function. See the HuggingFace transformers documentation for more details.	{}

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name	Type	Description	Default
model_id	Union[str, Path]	The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.	required
export	Optional[bool]	A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.	None
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, Path]	Directory where to save the model file.	required
push_to_hub	bool	Whether or not to push your model to the HuggingFace model hub after saving it.	False

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.	required
config	Optional[PretrainedConfig]	The configuration object associated with the model.	None
rbln_config	Optional[Union[RBLNModelConfig, Dict]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

The method performs the following steps:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. Saves the compiled model and configurations

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

Classes

RBLNDecoderOnlyModelForCausalLMConfig

Bases: RBLNDecoderOnlyModelConfig

Configuration class for RBLN decoder-only models for Causal Language Modeling.

This class extends RBLNModelConfig with parameters specific to decoder-only transformer architectures optimized for RBLN devices. It controls aspects like attention implementation, KV cache management, and batching for inference.

Functions

__init__(batch_size=None, max_seq_len=None, use_inputs_embeds=None, use_attention_mask=None, use_position_ids=None, attn_impl=None, kvcache_partition_len=None, kvcache_block_size=None, quantization=None, prefill_chunk_size=None, kvcache_num_blocks=None, decoder_batch_sizes=None, cache_impl=None, sliding_window=None, sliding_window_layers=None, phases=None, logits_to_keep=None, **kwargs)

Parameters:

Name	Type	Description	Default
batch_size	Optional[int]	The batch size for inference. Defaults to 1.	None
max_seq_len	Optional[int]	The maximum sequence length supported by the model. If not provided, it attempts to infer from the model's configuration (max_position_embeddings or n_positions). Must be specified if not available in the model config.	None
use_inputs_embeds	Optional[bool]	Whether to use input embeddings (inputs_embeds) directly instead of input_ids. Defaults to False. Requires the model to be compiled with this option enabled.	None
use_attention_mask	Optional[bool]	Whether the model requires attention masks during inference. This is typically determined based on the target device and model architecture. Defaults are often set automatically based on the model and RBLN NPU.	None
use_position_ids	Optional[bool]	Whether to use position IDs. Defaults to False.	None
attn_impl	Optional[str]	Specifies the attention implementation to use. See the "Attention Implementation (attn_impl)" section below for details.	None
kvcache_partition_len	Optional[int]	Defines the partition length for the KV cache when using "flash_attn". See the "KV Cache Partition Length (kvcache_partition_len)" section below for details.	None
kvcache_block_size	Optional[int]	Sets the size (in number of tokens) of each block in the PagedAttention KV cache. See the "KV Cache Block Size (kvcache_block_size)" section below for details.	None
prefill_chunk_size	Optional[int]	The chunk size used during the prefill phase for processing input sequences. Defaults to 128. Must be a positive integer divisible by 64. Affects prefill performance and memory usage.	None
kvcache_num_blocks	Optional[int]	The total number of blocks to allocate for the PagedAttention KV cache. See the "KV Cache Number of Blocks (kvcache_num_blocks)" section below for details.	None
decoder_batch_sizes	Optional[List[int]]	A list of batch sizes for which separate decoder models will be compiled. This allows the model to handle varying batch sizes efficiently during generation. If not specified, defaults to a list containing only the model's main batch size. When specifying multiple batch sizes: 1) All values must be less than or equal to the main batch size. 2) The list will be sorted in descending order (larger batch sizes first). 3) If using multiple decoders, at least one batch size should match the main batch size.	None
cache_impl	Optional[CacheImplType]	Specifies the KV cache implementation strategy. Defaults to "static". - "static": Uses a fixed-size global KV cache for all layers, suitable for standard attention patterns. - "sliding_window": Implements a sliding window KV cache, where each layer maintains a local cache of recent tokens. - "hybrid": Combines both static and sliding window approaches, allowing different layers to use different cache strategies. The choice affects memory usage and attention patterns. When using "sliding_window" or "hybrid", you must specify the sliding_window size and optionally sliding_window_layers for hybrid mode.	None
sliding_window	Optional[int]	The size of the sliding window. Defaults to None.	None
sliding_window_layers	Optional[List[int]]	The layers to use for the sliding window used in the hybrid model. Defaults to None.	None
phases	Optional[List[PhaseType]]	The phases to compile the model for. Defaults to ["prefill"] if DecoderOnlyModel is used, ["prefill", "decode"] if DecoderOnlyModelForCausalLM is used.	None
logits_to_keep	Optional[int]	The number of logits to keep for the decoder. If set to 0, the decoder will keep all logits. Defaults to 0 if DecoderOnlyModel is used, 1 if DecoderOnlyModelForCausalLM is used.	None
kwargs		Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If batch_size is not a positive integer.
ValueError	If prefill_chunk_size is not a positive integer divisible by 64.
ValueError	If max_seq_len cannot be determined and is required.
ValueError	If attention parameter constraints are violated (e.g., max_seq_len vs kvcache_partition_len for flash attention).

Attention Implementation

attn_impl determines the underlying attention mechanism used by the model.

• "eager" (Default if kvcache_partition_len is not set): Uses the standard PyTorch attention implementation. Suitable for sequences up to a certain limit (e.g., 32,768 tokens).
• "flash_attn": Utilizes an optimized Flash Attention implementation, beneficial for longer sequences and potentially faster execution. Requires max_seq_len to be at least 8,192. If kvcache_partition_len is specified, attn_impl automatically defaults to "flash_attn". When using "flash_attn", kvcache_block_size must equal kvcache_partition_len.

The choice impacts performance and memory usage, especially for long sequences. Constraints related to max_seq_len and kvcache_partition_len apply when using "flash_attn".

KV Cache Partition Length

kvcache_partition_len is relevant only when attn_impl is "flash_attn".

• It defines the length (number of tokens) of each partition within the Key-Value (KV) cache.
• Must be between 4,096 and 32,768 (inclusive).
• When using "flash_attn", max_seq_len must be a multiple of kvcache_partition_len and at least twice its value (max_seq_len >= 2 * kvcache_partition_len).
• If attn_impl is "flash_attn" and kvcache_partition_len is None, it defaults to 16,384.

KV Cache Number of Blocks

kvcache_num_blocks controls the total number of memory blocks allocated for the PagedAttention KV cache. Each block holds kvcache_block_size tokens of Key and Value states.

• Automatic Estimation (Default): If kvcache_num_blocks is None, the system estimates the maximum number of blocks that can fit into the available RBLN device memory. This calculation considers the model size (kernel memory), required buffer memory, the number of layers and heads, kvcache_block_size, tensor parallelism, and available RBLN NPU DRAM. This aims to maximize cache capacity for potentially better performance with long sequences or larger batches without manual tuning.
• Manual Setting: You can explicitly set the number of blocks. This provides finer control but requires careful consideration of memory limits. Setting it too high may lead to compilation errors if it exceeds available memory. The system will issue warnings if your setting exceeds the estimated maximum.
• Performance Impact: A larger number of blocks reduces the likelihood of cache eviction, which is beneficial for tasks involving many long sequences or large batch sizes, enabling higher throughput. However, allocating more blocks consumes more memory.
• Minimum Requirement: The system requires a minimum number of blocks to function, calculated based on max_seq_len, kvcache_block_size, and batch_size. The number of allocated blocks must be sufficient to hold at least one full sequence length per item in the batch concurrently. The system will log warnings or raise errors if constraints are violated (e.g., if kvcache_num_blocks is less than batch_size when using Flash Attention).

The optimal value depends on the specific model, task, hardware, and desired trade-off between performance and memory usage. The automatic estimation provides a robust starting point.

load(path, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Parameters:

Name	Type	Description	Default
path	str	Path to the RBLNModelConfig file or directory containing the config file.	required
kwargs	Any	Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.	{}

Returns:

Name	Type	Description
RBLNModelConfig	RBLNModelConfig	The loaded configuration instance.

Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.

This file defines generic base classes for various RBLN models, such as Question Answering, Image Classification, Audio Classification, Sequence Classification, and Masked Language Modeling. These classes implement common functionalities and configurations to be used across different model architectures.

Classes

RBLNTransformerEncoder

Bases: RBLNModel

Functions

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.	required
config	Optional[PretrainedConfig]	The configuration object associated with the model.	None
rbln_config	Optional[Union[RBLNModelConfig, Dict]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

The method performs the following steps:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. Saves the compiled model and configurations

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name	Type	Description	Default
model_id	Union[str, Path]	The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.	required
export	Optional[bool]	A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.	None
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, Path]	Directory where to save the model file.	required
push_to_hub	bool	Whether or not to push your model to the HuggingFace model hub after saving it.	False

forward(*args, return_dict=None, **kwargs)

Defines the forward pass of RBLNModel. The interface mirrors HuggingFace conventions so it can act as a drop-in replacement in many cases.

This method executes the compiled RBLN model on RBLN NPU devices while remaining fully compatible with Hugging Face Transformers and Diffusers APIs. In practice, RBLNModel can replace models built on torch.nn.Module — including transformers.PreTrainedModel implementations and Diffusers components based on diffusers.ModelMixin — enabling seamless integration into existing workflows.

Parameters:

Name	Type	Description	Default
args	Any	Variable length argument list containing model inputs. The format matches the original HuggingFace model's forward method signature (e.g., input_ids, attention_mask for transformers models, or sample, timestep for diffusers models).	()
return_dict	Optional[bool]	Whether to return outputs as a dictionary-like object or as a tuple. When None: - For transformers models: Uses self.config.use_return_dict (typically True) - For diffusers models: Defaults to True	None
kwargs	Any	Arbitrary keyword arguments containing additional model inputs and parameters, matching the original HuggingFace model's interface.	{}

Returns:

Type	Description
Any	Model outputs in the same format as the original HuggingFace model.
Any	If return_dict=True, Returns a dictionary-like object (e.g., BaseModelOutput, CausalLMOutput) with named fields such as logits, hidden_states, etc.
Any	If return_dict=False, Returns a tuple containing the raw model outputs.

Note

• This method maintains the exact same interface as the original HuggingFace model's forward method
• The compiled model runs on RBLN NPU hardware for accelerated inference
• All HuggingFace model features (generation, attention patterns, etc.) are preserved
• Can be used directly in HuggingFace pipelines, transformers.Trainer, and other workflows

RBLNTransformerEncoderForFeatureExtraction

Bases: RBLNTransformerEncoder

Functions

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name	Type	Description	Default
model_id	Union[str, Path]	The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.	required
export	Optional[bool]	A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.	None
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, Path]	Directory where to save the model file.	required
push_to_hub	bool	Whether or not to push your model to the HuggingFace model hub after saving it.	False

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.	required
config	Optional[PretrainedConfig]	The configuration object associated with the model.	None
rbln_config	Optional[Union[RBLNModelConfig, Dict]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

The method performs the following steps:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. Saves the compiled model and configurations

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

forward(*args, return_dict=None, **kwargs)

Defines the forward pass of RBLNModel. The interface mirrors HuggingFace conventions so it can act as a drop-in replacement in many cases.

This method executes the compiled RBLN model on RBLN NPU devices while remaining fully compatible with Hugging Face Transformers and Diffusers APIs. In practice, RBLNModel can replace models built on torch.nn.Module — including transformers.PreTrainedModel implementations and Diffusers components based on diffusers.ModelMixin — enabling seamless integration into existing workflows.

Parameters:

Name	Type	Description	Default
args	Any	Variable length argument list containing model inputs. The format matches the original HuggingFace model's forward method signature (e.g., input_ids, attention_mask for transformers models, or sample, timestep for diffusers models).	()
return_dict	Optional[bool]	Whether to return outputs as a dictionary-like object or as a tuple. When None: - For transformers models: Uses self.config.use_return_dict (typically True) - For diffusers models: Defaults to True	None
kwargs	Any	Arbitrary keyword arguments containing additional model inputs and parameters, matching the original HuggingFace model's interface.	{}

Returns:

Type	Description
Any	Model outputs in the same format as the original HuggingFace model.
Any	If return_dict=True, Returns a dictionary-like object (e.g., BaseModelOutput, CausalLMOutput) with named fields such as logits, hidden_states, etc.
Any	If return_dict=False, Returns a tuple containing the raw model outputs.

Note

• This method maintains the exact same interface as the original HuggingFace model's forward method
• The compiled model runs on RBLN NPU hardware for accelerated inference
• All HuggingFace model features (generation, attention patterns, etc.) are preserved
• Can be used directly in HuggingFace pipelines, transformers.Trainer, and other workflows

RBLNModelForQuestionAnswering

Bases: RBLNTransformerEncoder

Functions

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name	Type	Description	Default
model_id	Union[str, Path]	The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.	required
export	Optional[bool]	A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.	None
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, Path]	Directory where to save the model file.	required
push_to_hub	bool	Whether or not to push your model to the HuggingFace model hub after saving it.	False

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.	required
config	Optional[PretrainedConfig]	The configuration object associated with the model.	None
rbln_config	Optional[Union[RBLNModelConfig, Dict]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

The method performs the following steps:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. Saves the compiled model and configurations

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

forward(*args, return_dict=None, **kwargs)

Defines the forward pass of RBLNModel. The interface mirrors HuggingFace conventions so it can act as a drop-in replacement in many cases.

This method executes the compiled RBLN model on RBLN NPU devices while remaining fully compatible with Hugging Face Transformers and Diffusers APIs. In practice, RBLNModel can replace models built on torch.nn.Module — including transformers.PreTrainedModel implementations and Diffusers components based on diffusers.ModelMixin — enabling seamless integration into existing workflows.

Parameters:

Name	Type	Description	Default
args	Any	Variable length argument list containing model inputs. The format matches the original HuggingFace model's forward method signature (e.g., input_ids, attention_mask for transformers models, or sample, timestep for diffusers models).	()
return_dict	Optional[bool]	Whether to return outputs as a dictionary-like object or as a tuple. When None: - For transformers models: Uses self.config.use_return_dict (typically True) - For diffusers models: Defaults to True	None
kwargs	Any	Arbitrary keyword arguments containing additional model inputs and parameters, matching the original HuggingFace model's interface.	{}

Returns:

Type	Description
Any	Model outputs in the same format as the original HuggingFace model.
Any	If return_dict=True, Returns a dictionary-like object (e.g., BaseModelOutput, CausalLMOutput) with named fields such as logits, hidden_states, etc.
Any	If return_dict=False, Returns a tuple containing the raw model outputs.

Note

• This method maintains the exact same interface as the original HuggingFace model's forward method
• The compiled model runs on RBLN NPU hardware for accelerated inference
• All HuggingFace model features (generation, attention patterns, etc.) are preserved
• Can be used directly in HuggingFace pipelines, transformers.Trainer, and other workflows

RBLNModelForSequenceClassification

Bases: RBLNTransformerEncoder

Functions

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name	Type	Description	Default
model_id	Union[str, Path]	The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.	required
export	Optional[bool]	A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.	None
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, Path]	Directory where to save the model file.	required
push_to_hub	bool	Whether or not to push your model to the HuggingFace model hub after saving it.	False

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.	required
config	Optional[PretrainedConfig]	The configuration object associated with the model.	None
rbln_config	Optional[Union[RBLNModelConfig, Dict]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

The method performs the following steps:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. Saves the compiled model and configurations

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

forward(*args, return_dict=None, **kwargs)

Defines the forward pass of RBLNModel. The interface mirrors HuggingFace conventions so it can act as a drop-in replacement in many cases.

This method executes the compiled RBLN model on RBLN NPU devices while remaining fully compatible with Hugging Face Transformers and Diffusers APIs. In practice, RBLNModel can replace models built on torch.nn.Module — including transformers.PreTrainedModel implementations and Diffusers components based on diffusers.ModelMixin — enabling seamless integration into existing workflows.

Parameters:

Name	Type	Description	Default
args	Any	Variable length argument list containing model inputs. The format matches the original HuggingFace model's forward method signature (e.g., input_ids, attention_mask for transformers models, or sample, timestep for diffusers models).	()
return_dict	Optional[bool]	Whether to return outputs as a dictionary-like object or as a tuple. When None: - For transformers models: Uses self.config.use_return_dict (typically True) - For diffusers models: Defaults to True	None
kwargs	Any	Arbitrary keyword arguments containing additional model inputs and parameters, matching the original HuggingFace model's interface.	{}

Returns:

Type	Description
Any	Model outputs in the same format as the original HuggingFace model.
Any	If return_dict=True, Returns a dictionary-like object (e.g., BaseModelOutput, CausalLMOutput) with named fields such as logits, hidden_states, etc.
Any	If return_dict=False, Returns a tuple containing the raw model outputs.

Note

• This method maintains the exact same interface as the original HuggingFace model's forward method
• The compiled model runs on RBLN NPU hardware for accelerated inference
• All HuggingFace model features (generation, attention patterns, etc.) are preserved
• Can be used directly in HuggingFace pipelines, transformers.Trainer, and other workflows

RBLNModelForMaskedLM

Bases: RBLNTransformerEncoder

Functions

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name	Type	Description	Default
model_id	Union[str, Path]	The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.	required
export	Optional[bool]	A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.	None
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, Path]	Directory where to save the model file.	required
push_to_hub	bool	Whether or not to push your model to the HuggingFace model hub after saving it.	False

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.	required
config	Optional[PretrainedConfig]	The configuration object associated with the model.	None
rbln_config	Optional[Union[RBLNModelConfig, Dict]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

The method performs the following steps:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. Saves the compiled model and configurations

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

forward(*args, return_dict=None, **kwargs)

Defines the forward pass of RBLNModel. The interface mirrors HuggingFace conventions so it can act as a drop-in replacement in many cases.

This method executes the compiled RBLN model on RBLN NPU devices while remaining fully compatible with Hugging Face Transformers and Diffusers APIs. In practice, RBLNModel can replace models built on torch.nn.Module — including transformers.PreTrainedModel implementations and Diffusers components based on diffusers.ModelMixin — enabling seamless integration into existing workflows.

Parameters:

Name	Type	Description	Default
args	Any	Variable length argument list containing model inputs. The format matches the original HuggingFace model's forward method signature (e.g., input_ids, attention_mask for transformers models, or sample, timestep for diffusers models).	()
return_dict	Optional[bool]	Whether to return outputs as a dictionary-like object or as a tuple. When None: - For transformers models: Uses self.config.use_return_dict (typically True) - For diffusers models: Defaults to True	None
kwargs	Any	Arbitrary keyword arguments containing additional model inputs and parameters, matching the original HuggingFace model's interface.	{}

Returns:

Type	Description
Any	Model outputs in the same format as the original HuggingFace model.
Any	If return_dict=True, Returns a dictionary-like object (e.g., BaseModelOutput, CausalLMOutput) with named fields such as logits, hidden_states, etc.
Any	If return_dict=False, Returns a tuple containing the raw model outputs.

Note

• This method maintains the exact same interface as the original HuggingFace model's forward method
• The compiled model runs on RBLN NPU hardware for accelerated inference
• All HuggingFace model features (generation, attention patterns, etc.) are preserved
• Can be used directly in HuggingFace pipelines, transformers.Trainer, and other workflows

RBLNModelForTextEncoding

Bases: RBLNTransformerEncoder

Functions

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name	Type	Description	Default
model_id	Union[str, Path]	The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.	required
export	Optional[bool]	A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.	None
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, Path]	Directory where to save the model file.	required
push_to_hub	bool	Whether or not to push your model to the HuggingFace model hub after saving it.	False

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.	required
config	Optional[PretrainedConfig]	The configuration object associated with the model.	None
rbln_config	Optional[Union[RBLNModelConfig, Dict]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

The method performs the following steps:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. Saves the compiled model and configurations

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

forward(*args, return_dict=None, **kwargs)

Defines the forward pass of RBLNModel. The interface mirrors HuggingFace conventions so it can act as a drop-in replacement in many cases.

This method executes the compiled RBLN model on RBLN NPU devices while remaining fully compatible with Hugging Face Transformers and Diffusers APIs. In practice, RBLNModel can replace models built on torch.nn.Module — including transformers.PreTrainedModel implementations and Diffusers components based on diffusers.ModelMixin — enabling seamless integration into existing workflows.

Parameters:

Name	Type	Description	Default
args	Any	Variable length argument list containing model inputs. The format matches the original HuggingFace model's forward method signature (e.g., input_ids, attention_mask for transformers models, or sample, timestep for diffusers models).	()
return_dict	Optional[bool]	Whether to return outputs as a dictionary-like object or as a tuple. When None: - For transformers models: Uses self.config.use_return_dict (typically True) - For diffusers models: Defaults to True	None
kwargs	Any	Arbitrary keyword arguments containing additional model inputs and parameters, matching the original HuggingFace model's interface.	{}

Returns:

Type	Description
Any	Model outputs in the same format as the original HuggingFace model.
Any	If return_dict=True, Returns a dictionary-like object (e.g., BaseModelOutput, CausalLMOutput) with named fields such as logits, hidden_states, etc.
Any	If return_dict=False, Returns a tuple containing the raw model outputs.

Note

• This method maintains the exact same interface as the original HuggingFace model's forward method
• The compiled model runs on RBLN NPU hardware for accelerated inference
• All HuggingFace model features (generation, attention patterns, etc.) are preserved
• Can be used directly in HuggingFace pipelines, transformers.Trainer, and other workflows

Classes

RBLNTransformerEncoderConfig

Bases: RBLNModelConfig

Functions

__init__(max_seq_len=None, batch_size=None, model_input_names=None, model_input_shapes=None, **kwargs)

Parameters:

Name	Type	Description	Default
max_seq_len	Optional[int]	Maximum sequence length supported by the model.	None
batch_size	Optional[int]	The batch size for inference. Defaults to 1.	None
model_input_names	Optional[List[str]]	Names of the input tensors for the model. Defaults to class-specific rbln_model_input_names if not provided.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If batch_size is not a positive integer.

load(path, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Parameters:

Name	Type	Description	Default
path	str	Path to the RBLNModelConfig file or directory containing the config file.	required
kwargs	Any	Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.	{}

Returns:

Name	Type	Description
RBLNModelConfig	RBLNModelConfig	The loaded configuration instance.

Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.

RBLNTransformerEncoderForFeatureExtractionConfig

Bases: RBLNTransformerEncoderConfig

Functions

__init__(max_seq_len=None, batch_size=None, model_input_names=None, model_input_shapes=None, **kwargs)

Parameters:

Name	Type	Description	Default
max_seq_len	Optional[int]	Maximum sequence length supported by the model.	None
batch_size	Optional[int]	The batch size for inference. Defaults to 1.	None
model_input_names	Optional[List[str]]	Names of the input tensors for the model. Defaults to class-specific rbln_model_input_names if not provided.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If batch_size is not a positive integer.

load(path, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Parameters:

Name	Type	Description	Default
path	str	Path to the RBLNModelConfig file or directory containing the config file.	required
kwargs	Any	Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.	{}

Returns:

Name	Type	Description
RBLNModelConfig	RBLNModelConfig	The loaded configuration instance.

Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.

RBLNModelForQuestionAnsweringConfig

Bases: RBLNTransformerEncoderConfig

Functions

__init__(max_seq_len=None, batch_size=None, model_input_names=None, model_input_shapes=None, **kwargs)

Parameters:

Name	Type	Description	Default
max_seq_len	Optional[int]	Maximum sequence length supported by the model.	None
batch_size	Optional[int]	The batch size for inference. Defaults to 1.	None
model_input_names	Optional[List[str]]	Names of the input tensors for the model. Defaults to class-specific rbln_model_input_names if not provided.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If batch_size is not a positive integer.

load(path, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Parameters:

Name	Type	Description	Default
path	str	Path to the RBLNModelConfig file or directory containing the config file.	required
kwargs	Any	Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.	{}

Returns:

Name	Type	Description
RBLNModelConfig	RBLNModelConfig	The loaded configuration instance.

Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.

RBLNModelForSequenceClassificationConfig

Bases: RBLNTransformerEncoderConfig

Functions

__init__(max_seq_len=None, batch_size=None, model_input_names=None, model_input_shapes=None, **kwargs)

Parameters:

Name	Type	Description	Default
max_seq_len	Optional[int]	Maximum sequence length supported by the model.	None
batch_size	Optional[int]	The batch size for inference. Defaults to 1.	None
model_input_names	Optional[List[str]]	Names of the input tensors for the model. Defaults to class-specific rbln_model_input_names if not provided.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If batch_size is not a positive integer.

load(path, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Parameters:

Name	Type	Description	Default
path	str	Path to the RBLNModelConfig file or directory containing the config file.	required
kwargs	Any	Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.	{}

Returns:

Name	Type	Description
RBLNModelConfig	RBLNModelConfig	The loaded configuration instance.

Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.

RBLNModelForMaskedLMConfig

Bases: RBLNTransformerEncoderConfig

Functions

__init__(max_seq_len=None, batch_size=None, model_input_names=None, model_input_shapes=None, **kwargs)

Parameters:

Name	Type	Description	Default
max_seq_len	Optional[int]	Maximum sequence length supported by the model.	None
batch_size	Optional[int]	The batch size for inference. Defaults to 1.	None
model_input_names	Optional[List[str]]	Names of the input tensors for the model. Defaults to class-specific rbln_model_input_names if not provided.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If batch_size is not a positive integer.

load(path, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Parameters:

Name	Type	Description	Default
path	str	Path to the RBLNModelConfig file or directory containing the config file.	required
kwargs	Any	Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.	{}

Returns:

Name	Type	Description
RBLNModelConfig	RBLNModelConfig	The loaded configuration instance.

Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.

Classes

RBLNModelForSeq2SeqLM

Bases: RBLNModel, ABC

This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created with the from_pretrained() class method. This model inherits from [RBLNModel]. Check the superclass documentation for the generic methods the library implements for all its models.

A class to convert and run pre-trained transformers based Seq2SeqLM models on RBLN devices. It implements the methods to convert a pre-trained transformers Seq2SeqLM model into a RBLN transformer model by: - transferring the checkpoint weights of the original into an optimized RBLN graph, - compiling the resulting graph using the RBLN compiler.

Currently, this model class only supports the 'bart' and 't5' models from the transformers library. Future updates may include support for additional model types.

Functions

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.	required
config	Optional[PretrainedConfig]	The configuration object associated with the model.	None
rbln_config	Optional[Union[RBLNModelConfig, Dict]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

The method performs the following steps:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. Saves the compiled model and configurations

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name	Type	Description	Default
model_id	Union[str, Path]	The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.	required
export	Optional[bool]	A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.	None
rbln_config	Optional[Union[Dict, RBLNModelConfig]]	Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	None
kwargs	Any	Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.	{}

Returns:

Type	Description
RBLNModel	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name	Type	Description	Default
save_directory	Union[str, Path]	Directory where to save the model file.	required
push_to_hub	bool	Whether or not to push your model to the HuggingFace model hub after saving it.	False

Functions

Classes

RBLNModelForSeq2SeqLMConfig

Bases: RBLNModelConfig

Functions

__init__(batch_size=None, enc_max_seq_len=None, dec_max_seq_len=None, use_attention_mask=None, pad_token_id=None, kvcache_num_blocks=None, kvcache_block_size=None, **kwargs)

Parameters:

Name	Type	Description	Default
batch_size	Optional[int]	The batch size for inference. Defaults to 1.	None
enc_max_seq_len	Optional[int]	Maximum sequence length for the encoder.	None
dec_max_seq_len	Optional[int]	Maximum sequence length for the decoder.	None
use_attention_mask	Optional[bool]	Whether to use attention masks during inference.	None
pad_token_id	Optional[int]	The ID of the padding token in the vocabulary.	None
kvcache_num_blocks	Optional[int]	The total number of blocks to allocate for the PagedAttention KV cache for the SelfAttention. Defaults to batch_size.	None
kvcache_block_size	Optional[int]	Sets the size (in number of tokens) of each block in the PagedAttention KV cache for the SelfAttention. Defaults to dec_max_seq_len.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If batch_size is not a positive integer.

load(path, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Parameters:

Name	Type	Description	Default
path	str	Path to the RBLNModelConfig file or directory containing the config file.	required
kwargs	Any	Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.	{}

Returns:

Name	Type	Description
RBLNModelConfig	RBLNModelConfig	The loaded configuration instance.

Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.

Functions