The LLM training infrastructure space has seen rapid growth, with various platforms emerging to help teams fine-tune and train large language models. However, a critical divide exists: open source vs. closed source. In this post, we explain why Twinkle chose the open-source path and what it means for enterprise adoption.
Reinforcement Learning from Human Feedback (RLHF) and its variants have become essential for aligning LLMs. Two excellent open-source frameworks in this space are veRL (from ByteDance Seed team) and Twinkle (from ModelScope). Both are production-ready and support diverse training scenarios. In this post, we compare their architectural philosophies and help you choose the right tool for your needs.
We’re excited to announce that Twinkle Training-as-a-Service (TaaS) is now available on ModelScope! Developers can experience Twinkle’s training API for free—no GPU cluster required.