ml-explore / ml-explore/mlx-examples

[FEATURE REQUEST] Support for LLM2VEC Encoder-Decoder LLMs

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Dominant language
Python
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Description

I would like to request support for LLM2VEC, which turns Decoder-only LLM's into bidirectional Encoder-Decoder models.

https://github.com/McGill-NLP/llm2vec

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reading the linked LLM2VEC project to understand the requested encoder-decoder behavior and its integration requirements. Then inspect the MLX examples repository for the relevant language-model entry points and existing model support. Done means defining the required scope and adding working LLM2VEC support with validation for the documented behavior.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
machine-learning
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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