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v0.5.0

sipcapture/homerv0.5.0Nov 18, 2025by min-oumi

AI Summary

This release introduces major new modules for data synthesis and hyperparameter tuning, expands inference options with AWS Bedrock support, and enhances the launcher for HPC clusters like NERSC Perlmutter.

Key Highlights

  • New `oumi synth` module for data synthesis
  • New `oumi tune` module for hyperparameter tuning
  • AWS Bedrock Inference Engine integration
  • NERSC Perlmutter HPC cluster support
  • Generalized Knowledge Distillation (GKD) trainer support

New Features

  • Data synthesis module with template-based generation
  • Hyperparameter tuning module
  • Bedrock integration for scalable deployment
  • NERSC Perlmutter support
  • GKD Trainer support

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)