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AlicanKiraz0/SenecaLLM-x-QwQ-32B-Q8_Max-Version

Mixed dataset LLM Information Security v1.5 - Incident Response v1.3.1 - Threat Hunting v1.3.2 - Ethical Exploit Development v2.0 - Purple Team Tactics v1.3 - Reverse Engineering v2.0
Run time: 1 second
1710 runs
0

deepseek-ai/DeepSeek-R1-Distill-Qwen-32B

DeepSeek-R1-Distill-Qwen-32B is a distilled version of the DeepSeek-R1 model with 32 billion parameters, leveraging the Qwen architecture to deliver high-quality language understanding and generation. Through knowledge distillation, it retains the strengths of larger models while offering improved efficiency and reduced computational requirements. This model is ideal for large-scale AI applications that demand robust performance with optimized resource utilization.
Run time: 1 second
1198 runs
0

deepseek-ai/DeepSeek-R1-Distill-Qwen-14B

DeepSeek-R1-Distill-Qwen-14B is a distilled version of the DeepSeek-R1 model, featuring 14 billion parameters. Built on the Qwen architecture, it utilizes advanced knowledge distillation techniques to achieve a balance between high performance and computational efficiency. This model is well-suited for a wide range of natural language processing tasks, providing accurate and context-aware responses while optimizing resource consumption for deployment in production environments.
Run time: 1 second
0 runs
0

deepseek-ai/DeepSeek-R1-Distill-Llama-8B

DeepSeek-R1-Distill-Llama-8B is a distilled version of the DeepSeek-R1 model based on the LLaMA architecture, featuring 8 billion parameters. It offers a balance between performance and efficiency by leveraging knowledge distillation techniques to reduce computational costs while maintaining high-quality language processing capabilities. This model is ideal for applications that require powerful text generation and understanding with optimized resource usage.
Run time: 1 second
1230 runs
0

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

DeepSeek-R1-Distill-Qwen-7B is a distilled version of the DeepSeek-R1 model with 7 billion parameters, designed to provide high-quality language understanding while optimizing efficiency. Leveraging advanced knowledge distillation techniques, it retains the core capabilities of larger models with improved speed and lower resource consumption. This model is well-suited for tasks requiring robust natural language processing while maintaining cost-effective deployment.
Run time: 1 second
2264 runs
0

deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

DeepSeek-R1-Distill-Qwen-1.5B is a distilled version of the DeepSeek-R1 model, designed to offer a balance between efficiency and performance. With 1.5 billion parameters, it leverages knowledge distillation techniques to retain the capabilities of larger models while optimizing for faster inference and reduced resource consumption. Ideal for applications requiring high-quality language understanding with lower computational overhead.
Run time: 1 second
2705 runs
0

microsoft/Phi-3.5-mini-instruct

microsoft/Phi-3.5-mini-instruct is a lightweight AI language model optimized for instruction-based tasks, offering efficient performance and high-quality responses with a compact architecture.
Run time: 1 second
2098 runs
0

microsoft/phi-4

PHI-4 by Microsoft is an advanced large language model designed for precise natural language understanding and generation. It offers robust performance in complex conversational AI and NLP tasks, making it well-suited for enterprise and research applications.
Run time: 1 second
1788 runs
0

ZhengPeng7/BiRefNet

BirefNet is a GenAI tool that removes backgrounds with high precision.
Run time: 1 second
1543 runs
0

wangqixun/YamerMIX v8.0

An instruction-based image editing model SDXL.
Run time: 1 second
2918 runs
0

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