THChou1220/gemma-4-e2b-kinetics54K-SQ_FFT
THChou1220/gemma-4-e2b-kinetics54K_FFT is a full fine-tune of the Google Gemma-4-e2b-it model, specifically adapted for processing AI-generated video data. This model specializes in understanding and generating content related to video instructions, leveraging a dataset derived from Kinetics. It is optimized for tasks involving video analysis and interpretation, making it suitable for applications requiring comprehension of dynamic visual information.
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Model Overview
THChou1220/gemma-4-e2b-kinetics54K_FFT is a specialized model based on the google/gemma-4-e2b-it architecture. It has undergone a full fine-tuning process, rather than LoRA, to enhance its capabilities in understanding and processing AI-generated video data.
Key Characteristics
- Video Data Specialization: Fine-tuned on a unique dataset of 54,618 AI-generated video instruction examples derived from Kinetics.
- Training Methodology: Utilizes full fine-tuning with bfloat16 precision over 1 epoch, employing DeepSpeed ZeRO-3 for efficient training across 4 GPUs.
- Optimized for Video Instructions: The training focused on video instruction examples, making it particularly adept at tasks requiring comprehension of dynamic visual sequences.
- Technical Configuration: Features a max sequence length of 3072, AdamW optimizer with a learning rate of 5e-6, and gradient checkpointing enabled.
Ideal Use Cases
This model is particularly well-suited for applications that involve:
- Video Content Analysis: Interpreting and understanding AI-generated video instructions.
- Video-based AI Systems: Developing systems that need to process and respond to dynamic visual information.
- Research in Video Understanding: Exploring the capabilities of language models on specialized video datasets.