Reddit investigator says numbat checkpoint weights were generated
A Reddit investigator reports that the tested weights in the burner22/numbat checkpoint were deterministically generated rather than learned through training. The investigator says SHA-256 and NumPy PCG64 reconstructed all 633 tested tensors, covering 11,374,080 values, bit for bit. All 373.6 million weight signs matched predicted pseudorandom sequences, and 99.51% of 173 million held-out NVFP4 quantization codes were reconstructed. A 4.73B-parameter truncated model ran but produced repetitive punctuation rather than meaningful language. Some semantic structure was found in the embeddings, which the investigator says does not make the reconstructed weight families genuine trained weights. The analysis does not identify who created the checkpoint or establish whether it has any connection to Anthropic.
I investigated burner22/numbat, a 7.72TB checkpoint claiming to be an internal Anthropic Mythos-class model.
My forensic findings show that the tested weights are deterministically generated, not learned through training. The checkpoint's claim to be a genuine trained model is not supported by this evidence.
Here's the proof:
Conclusion: This is a fake model checkpoint containing demonstrably generated weights. The analysis does not establish who created it or whether it has any connection to Anthropic.
Full forensic research, reconstruction scripts, and evidence:
https://huggingface.co/adityawakharkar/numbat-forensics
All findings are open for independent verification. submitted by /u/Appropriate-Try-1998
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