lmstudio-ai / lmstudio-ai/mlx-engine
[Feature]: Support encoder models
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- Dominant language
- Python
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Description
Support encoders, poolers, and structured prediction Small Language Models
ColPali/ColQwen/Nemotron
ColBERT, GLiNER, ModernColBERT
Encoders on a magnitude are much more efficient on tasks they can do best than the decoder models for tasks like and are perfect for self-hosting:
Schema extraction
NER, Entity Linking
Multimodal retrieval
Embeddings
Reranking
https://github.com/ddickmann/vllm-factory
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files, tests, or entry points are named. Start by reviewing the repository's current model-loading path and the linked vllm-factory project, then define which encoder, pooler, and structured-prediction models are in scope and what tests will demonstrate support.
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
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100