Showing 3 open source projects for "video making"

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    CogVLM2

    CogVLM2

    GPT4V-level open-source multi-modal model based on Llama3-8B

    ...Built on Meta-Llama-3-8B-Instruct, CogVLM2 significantly improves over its predecessor by providing stronger performance across multimodal benchmarks such as TextVQA, DocVQA, and ChartQA, while introducing extended context length support of up to 8K tokens and high-resolution image input up to 1344×1344. The series includes models for both image understanding and video understanding, with CogVLM2-Video supporting up to 1-minute videos by analyzing keyframes. It supports bilingual interaction (Chinese and English) and has open-source versions optimized for dialogue and video comprehension. Notably, the Int4 quantized version allows efficient inference on GPUs with only 16GB of memory. The repository offers demos, API servers, fine-tuning examples, and integration with OpenAI API-compatible endpoints, making it accessible for both researchers and developers.
    Downloads: 0 This Week
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    LLM Colosseum

    LLM Colosseum

    Benchmark LLMs by fighting in Street Fighter 3

    LLM-Colosseum is an experimental benchmarking framework designed to evaluate the capabilities of large language models through gameplay interactions rather than traditional text-based benchmarks. The system places language models inside the environment of the classic video game Street Fighter III, where they must interpret the game state and decide which actions to perform during combat. This setup creates a dynamic environment that tests reasoning, situational awareness, and decision-making abilities in real time. Instead of relying purely on reward signals as in reinforcement learning agents, the models analyze contextual information and generate strategic actions based on the game environment. ...
    Downloads: 0 This Week
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  • 3
    Cradle framework

    Cradle framework

    The Cradle framework is a first attempt at General Computer Control

    Cradle is an open-source framework designed to enable AI agents to perform complex computer tasks by interacting with software environments in a way similar to human users. The system introduces the concept of General Computer Control, where AI agents receive screenshots as input and perform actions through simulated keyboard and mouse operations. This approach allows agents to interact with any software interface without relying on specialized APIs or predefined automation scripts. The...
    Downloads: 4 This Week
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