bridge.models.llama.llama_bridge#

Module Contents#

Classes#

LlamaBridge

Megatron Bridge for Llama Causal LM.

Data#

API#

bridge.models.llama.llama_bridge.logger#

‘getLogger(…)’

class bridge.models.llama.llama_bridge.LlamaBridge#

Bases: megatron.bridge.models.conversion.model_bridge.MegatronModelBridge

Megatron Bridge for Llama Causal LM.

As a user you would not use this bridge directly, but through AutoBridge.

.. rubric:: Example

from megatron.bridge import AutoBridge bridge = AutoBridge.from_hf_pretrained(“meta-llama/Llama-3.1-8B-Instruct”) provider = bridge.to_megatron_provider()

provider_bridge(
hf_pretrained: megatron.bridge.models.hf_pretrained.causal_lm.PreTrainedCausalLM,
) megatron.bridge.models.gpt_provider.GPTModelProvider#

Convert HuggingFace Llama config to Megatron GPTModelProvider.

Uses base class implementation for common conversion, then sets Llama-specific config and enables RoPE scaling for Llama 3.1/3.2 models.

Parameters:

hf_pretrained – HuggingFace PreTrainedCausalLM containing the Llama config

Returns:

GPTModelProvider configured for Llama architecture

classmethod megatron_to_hf_config(
provider: megatron.bridge.models.gpt_provider.GPTModelProvider,
) dict#

Convert Megatron GPTModelProvider config to HuggingFace Llama config dict.

Uses base class implementation, then adds RoPE scaling for Llama 3.1/3.2.

Parameters:

provider – GPTModelProvider with Llama configuration

Returns:

Dictionary of HuggingFace LlamaConfig parameters

mapping_registry() megatron.bridge.models.conversion.mapping_registry.MegatronMappingRegistry#