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Artificial Intelligence is revolutionizing the way we create and consume content. In 2025, we're witnessing unprecedented advances in AI that are transforming the content creation landscape.
We present TKLLM Gradient-Surgical, a gradient-informed dynamic parameter freezing framework designed for efficient fine-tuning of TikTok-specific large language models (LLMs).
That’s where TKLLM comes in — the world’s first TikTok-specific small model training platform.With just one click, you can train your own proprietary TikTok AI model, capable of generating viral scripts, captions, and storyboards that perfectly match your content style.
Fine-tuning large language and vision models is a cornerstone of modern AI adaptation, yet traditional approaches often suffer from high computational cost, redundancy, and instability.
This paper introduces a new fine-tuning framework inspired by DeepSeek-OCR’s Optical Context Compression (OCC). By integrating an Optical Context Encoder (OCE), Mixture-of-Experts (MoE) Style Module, and an Optical Forgetting Mechanism, combined with Gradient Surgical Fine-Tuning (GSPF), the framework achieves efficient multimodal alignment and dynamic memory control.
The goal is to enhance TKLLM with TikTok-style content virality intelligence. Instead of only generating stylistically consistent scripts, TKLLM becomes an algorithm-aware content generator capable of producing content with high virality potential.
