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[Model] Add LlamaEmbeddingModel as an embedding Implementation of LlamaModel #9806
[Model] Add LlamaEmbeddingModel as an embedding Implementation of LlamaModel #9806
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👋 Hi! Thank you for contributing to the vLLM project. Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging. To run CI, PR reviewers can do one of these:
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Sounds reasonable, thanks for extending this!
…maModel (vllm-project#9806) Signed-off-by: Randall Smith <[email protected]>
…maModel (vllm-project#9806) Signed-off-by: NickLucche <[email protected]>
…maModel (vllm-project#9806) Signed-off-by: NickLucche <[email protected]>
…maModel (vllm-project#9806) Signed-off-by: Linkun Chen <[email protected]>
…maModel (vllm-project#9806) Signed-off-by: Loc Huynh <[email protected]>
This PR adds
LlamaEmbeddingModel
as an embedding implementation ofLlamaModel
in the model registry.LlamaEmbeddingModel
has already been introduced as the implementation forMistralModel
, and this addition leverages the existing implementation.Background:
At my company, we are developing an embedding model based on
LlamaModel
and wish to run it with vLLM. Internal tests confirm that it works as expected within our environment. However, due to company policies, we are not yet able to share further details publicly. Adding this change would be extremely helpful, as it would allow us to use our model in a more accessible way and assist in eventually making it public. Thank you for considering this request.FIX #xxxx (link existing issues this PR will resolve)
BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE
PR Checklist (Click to Expand)
Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.
PR Title and Classification
Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:
[Bugfix]
for bug fixes.[CI/Build]
for build or continuous integration improvements.[Doc]
for documentation fixes and improvements.[Model]
for adding a new model or improving an existing model. Model name should appear in the title.[Frontend]
For changes on the vLLM frontend (e.g., OpenAI API server,LLM
class, etc.)[Kernel]
for changes affecting CUDA kernels or other compute kernels.[Core]
for changes in the core vLLM logic (e.g.,LLMEngine
,AsyncLLMEngine
,Scheduler
, etc.)[Hardware][Vendor]
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for PRs that do not fit the above categories. Please use this sparingly.Note: If the PR spans more than one category, please include all relevant prefixes.
Code Quality
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format.sh
to format your code.docs/source/
if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.Adding or changing kernels
Each custom kernel needs a schema and one or more implementations to be registered with PyTorch.
Tensors
require meta-functions. Meta-functions should be implemented and registered in python so that dynamic dims can be handled automatically. See above documents for a description of meta-functions.torch.libary.opcheck()
to test the function registration and meta-function for any registered ops. Seetests/kernels
for examples.Notes for Large Changes
Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with
rfc-required
and might not go through the PR.What to Expect for the Reviews
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action-required
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