bridge.models.llama.llama_bridge#
Module Contents#
Classes#
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.MegatronModelBridgeMegatron 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,
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,
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#