Train and deploy task-specialized LLMs with PEFT/LoRA and structured inference.¶
LMTask is a configuration-driven framework for dataset preparation, parameter-efficient supervised fine-tuning, structured inference, and deployment of task-specialized language models.
- Overview
- Table of Contents
- Why LMTask?
- Features
- Installation
- Quick start
- Specialize a model with PEFT/LoRA SFT
- Test and benchmark a specialized model
- Python API
- Use an LMTask task in an agentic workflow
- Dataset configuration
- Supported model configurations
- Documentation
- Evidence and reproducibility
- Alternatives
- Community
- Changelog
- License
- Configuration
- Inference
- Training
- API Reference
- zensols.lmtask package
- Submodules
- zensols.lmtask.app module
- zensols.lmtask.benchmark module
- zensols.lmtask.cli module
- zensols.lmtask.dataset module
- zensols.lmtask.gemma4 module
- zensols.lmtask.generate module
- zensols.lmtask.hf module
- zensols.lmtask.instruct module
- zensols.lmtask.metric module
- zensols.lmtask.proto module
- zensols.lmtask.task module
- zensols.lmtask.test module
- zensols.lmtask.torchconfig module
- zensols.lmtask.torchtype module
- zensols.lmtask.train module
- Module contents
- zensols.lmtask package
- Contributing
- License