feat: expose attention_type parameter in Llama.__init__#2143
Draft
jamesbiederbeck wants to merge 1 commit intoabetlen:mainfrom
Draft
feat: expose attention_type parameter in Llama.__init__#2143jamesbiederbeck wants to merge 1 commit intoabetlen:mainfrom
jamesbiederbeck wants to merge 1 commit intoabetlen:mainfrom
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
llama_context_paramsalready contains anattention_typefield andllama_cpp.pydefines theLLAMA_ATTENTION_TYPE_*constants, butLlama.__init__does not expose this parameter.This makes it impossible to select non-causal attention from Python,
which is required for embedding models trained with bidirectional
attention (e.g. GTE/Qwen embedding models).
This PR wires the parameter through to
self.context_params.attention_type,mirroring how
pooling_typeis handled.Example usage:
from llama_cpp.llama_cpp import LLAMA_ATTENTION_TYPE_NON_CAUSAL
model = Llama(
model_path="model.gguf",
embedding=True,
attention_type=LLAMA_ATTENTION_TYPE_NON_CAUSAL,
)