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v0.5.0

neuml/txtaiv0.5.0Nov 18, 2025by min-oumi

AI Summary

Announces Oumi v0.5.0, introducing data synthesis and hyperparameter tuning modules, alongside AWS Bedrock integration and NERSC HPC support.

Key Highlights

  • Introduction of `oumi synth` for automated data generation using LLMs
  • Launch of `oumi tune` for hyperparameter search and optimization
  • Added AWS Bedrock Inference Engine support for scalable deployment
  • NERSC Perlmutter HPC cluster support
  • Enhanced logging capabilities with job log trailing

New Features

  • Data Synthesis Module with template-based generation
  • Hyperparameter Tuning Module
  • AWS Bedrock Integration
  • GKD Trainer Support
  • OpenEnv RL Training Demo
  • NERSC Perlmutter Support
  • Enhanced Job Logging

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)