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Model · Image to image

stopmotion-consistency-v1-lora

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Built with Qwen. An experimental next-frame image-editing adapter fine-tuned from the original BF16 Qwen-Image-2.1. The adapter was trained on 112 curated stop-motion transitions using an RTX PRO 6000. Quality is limited. Matched tests show improved static-area retention, but motion magnitude, direction and shape consistency remain unreliable. This is a small research pilot, not a proven solution for long, consistent animation. The adapter is stopmotion-consistency-v1.safetensors, 67,141,056 bytes. It contains only attention LoRA updates, not the base model. SHA-256: d68479ed2c246bc7ebea8a62019b10da903772d30854385302b2d66de99df51a. Use DiffSynth-Studio at commit…

Model · Text classification

auto-200m-2-int4

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This is auto-200m-2 with its weights stored as 4-bit integers. The file is 77 MB instead of 299 MB, about a quarter of the size. It gets 2,884/3,000 on the benchmark, with 60 false approvals and 56 false denials. The BF16 model gets 2,890, with 53 and 57. 44 of the 3,000 decisions differ from the BF16 model. auto-200m-2 is a 149.6M-parameter ModernBERT classifier. It reads an AI agent's proposed tool call, the user's request and the agent's history, then answers approve or deny. It takes up to 65,536 tokens of context. This version loads through one small Python file, autoquant.py, in plain PyTorch, without compiled kernels. It was checked on NVIDIA CUDA, the Apple M4 Max CPU and Apple MPS.…

Model · Text classification

auto-200m-2-int8

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This is auto-200m-2 with its weights stored as 8-bit integers. The file is 150 MB instead of 299 MB, and it gets the same benchmark score: 2,890/3,000, with 53 false approvals and 57 false denials. Only 2 of the 3,000 decisions differ from the BF16 model. auto-200m-2 is a 149.6M-parameter ModernBERT classifier. It reads an AI agent's proposed tool call, the user's request and the agent's history, then answers approve or deny. It takes up to 65,536 tokens of context. This version loads through one small Python file, autoquant.py, in plain PyTorch, without compiled kernels. It was checked on NVIDIA CUDA, the Apple M4 Max CPU and Apple MPS. For an even smaller file, see auto-200m-2-int4 (77…