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A developer is creating a method to visually fingerprint AI models by analyzing weights and tensors using Random Matrix Theory, with data from QWEN2.5 models, and is interested in open-sourcing the tool.
A technical blog post presenting a reproducible pipeline to fingerprint whether an LLM was trained from scratch or derived from existing open weights, using architecture, tokenizer, and weight comparisons.
User @qqqqqf_ shared fingerprint test results for Fable5 and other models, finding that Fable5 has low similarity with most Anthropic models but very high similarity with kimi k2.7, qwen3-7max, glm5.2, etc., speculating possible data leakage or distillation. At the same time, they released an open source project ai-model-fingerprint for model identification and detection.