v0.5.0
unslothai/unslothv0.5.0Nov 18, 2025by min-oumi
AI Summary
Oumi v0.5.0 adds hyperparameter tuning capabilities, a data synthesis module, AWS Bedrock integration, and support for the GKD trainer and NERSC Perlmutter HPC cluster.
Key Highlights
- New `oumi synth` module for data generation
- New `oumi tune` module for hyperparameter optimization
- AWS Bedrock Inference Engine support
- GKD (Generalized Knowledge Distillation) trainer support
- NERSC Perlmutter HPC cluster support
New Features
- Data synthesis module with template-based generation
- Hyperparameter tuning module
- AWS Bedrock integration
- GKD trainer support
- NERSC Perlmutter HPC support
- Enhanced logging with job log trailing
- Lazy cloud initialization for faster startup
Full Release Notes
# **Oumi v0.5.0 Release Notes** We're excited to announce Oumi v0.5.0, featuring hyperparameter tuning capabilities, expanded inference options, and enhanced launcher functionality. ## **🚀 Major Features** ### **Data Synthesis Module** * Introducing `oumi synth` - a powerful data synthesis module for automatically generating high-quality training datasets using LLMs (#1965) * **Template-based Generation**: Control attributes like difficulty, style, and domain for diverse dataset creation * **Domain-specific Datasets**: Generate data for specialized fields (legal, medical, technical, etc.) * **Data Augmentation**: Expand existing small datasets by generating variations * **Multiple Formats**: Support for instruction-following, QA, and conversational datasets ### **Hyperparameter Tuning Module** * Introducing `oumi tune` - a new hyperparameter search and optimization module for efficient model tuning (#1998, #1991). Thank you @gbladislau-aumo! ### **Inference & Training Enhancements** * **Bedrock Integration**: Added AWS Bedrock Inference Engine support for scalable model deployment (#1983) - Thank you @aniruddh-alt! * **GKD Trainer Support**: New Generalized Knowledge Distillation trainer for model compression workflows (#2000) * **OpenEnv RL Training**: Demo notebook showcasing reinforcement learning training with reward visualization (#1996, #2012) ### **HPC & Launcher Improvements** * **NERSC Perlmutter Support**: Oumi launcher now supports the NERSC Perlmutter HPC cluster (#1959) * **Enhanced Logging**: Added job log trailing and dedicated logs command for better debugging (#1951, #1964) * **Lazy Cloud Initialization**: Improved launcher startup performance (#1985) ## **✨ Improvements** **Model Configuration** * Added Qwen3 VL 4B model configurations (#1992, #1993) * Exposed `chat_template_kwargs` parameter in ModelParams for fine-grained control (#1997) **Developer Experience** * Updated BaseConfig to support non-primitive field types (#1684) * Optional stdout_file parameter in SLURM client (#1974) ## **🐛 Bug Fixes** * Fixed NaN values in dataset analyzer for single-conversation datasets (#1961) * Resolved SLURM environment variable issues (PMI_RANK → SLURM_PROCID) (#2010) (Thank you @AliliRayane !) * Fixed non-primitive field saving in base config (#2005) * Updated uv pip install commands to include --system flag (#1979) * Unique inference scratch filenames via hashing (#1986) ## **📦 Dependency Updates** * Upgraded transformers: 4.56 → 4.57 (#1966, #1990) * Upgraded TRL: 0.24.0 → 0.25 (#1995, #2011) * Pinned uvicorn version for SkyPilot compatibility (#1978) ## **🎉 New Contributors** Welcome to our new contributors! * @gbladislau * @oumiandy * @AliliRayane ## **📖 Full Changelog** For a complete list of changes, see the [full changelog](https://github.com/oumi-ai/oumi/compare/v0.4.0...v0.5.0)