{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Tunix is a JAX-based library for post-training large language models with support for supervised fine-tuning, reinforcement learning, and agentic RL workflows, optimized for TPU execution.","skillfed_tags":["llm-training","jax-ecosystem","tpu-optimized"],"use_cases":["Fine-tune open-source LLMs on custom instruction or domain-specific data using supervised fine-tuning.","Run preference optimization workflows (DPO, ORPO) to align model outputs with human preferences.","Train models with PPO or GRPO on reward signals for complex reasoning or task-specific behavior optimization.","Build agentic RL systems where models learn to use tools and interact with environments over multiple turns.","Integrate with JAX-based modeling frameworks and inference engines to streamline end-to-end post-training pipelines."],"what_it_does":"Tunix is a JAX-native post-training framework designed to streamline fine-tuning and reinforcement learning workflows for large language models. It sits as an intermediate layer in the JAX training stack, integrating with Flax, Orbax, and high-performance inference engines like vLLM and SGLang-JAX for rollout generation. The library provides modular, composable components for supervised fine-tuning (full-weight and parameter-efficient variants), preference optimization (DPO, ORPO), reinforcement learning algorithms (PPO, GRPO, GSPO-Token, DAPO, Dr.GRPO), and agentic RL with multi-turn tool use and asynchronous trajectory collection.\n\nThe package is optimized for TPU execution with native integration into XLA and Pathways infrastructure, supporting seamless multi-host distributed training at scale. It includes micro-batching for efficient component-level execution, checkpointing and fault tolerance, and support for a growing list of model families (Gemma, Llama, Qwen). Tunix is under active development (v2 release, alpha status) with ongoing expansion of capabilities.","worth_installing":"Yes, if you are post-training LLMs on TPU infrastructure and want a modular, actively-maintained JAX-native framework. The low install friction and permissive license are favorable. However, the 26 runtime dependencies and alpha status mean you should verify that optional dependencies match your use case and accept that the API may evolve."},"id":"google-tunix","links":{"html":"https://skillfed.io/packages/google-tunix","md":"https://skillfed.io/packages/google-tunix.md","pypi":"https://pypi.org/project/google-tunix/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-11","license_spdx":null,"license_treatment":"permissive","name":"google-tunix","python_support":"supports_current","summary":"A lightweight JAX-native LLM post-training framework."},"popularity":{"monthly_downloads":177005,"position":10227,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.7"}