Alexander Ananyev

This model is served in https://github.com/xelananv/fhe-ssm: it is a 404,715,520 parameter recurrent language model whose every (!) nonlinearity is a low-degree polynomial with trained coeffs, so that the CKKS encrypted evaluation computes precisely the model's own float64 arithmetic without any post-hoc approximation of softmax or GELU. It succesfully generated text for 64 prompts at once under encryption, autoregressively, for 148 consecutive steps (=> 9,472 tokens) at 128 bit classical parameters, matching its own plaintext top-1 choice 99.7 % of the time. Those are properties of the circuit; this card is about the model, which is small and was trained on a few hundred dollars of…

On 2026-09-18 a 405M parameter recurrent language model (xelananv/fhe-ssm-pbd430a) generated text for 64 prompts at once under CKKS fully homomorphic encryption on a rented 2 GPU server, with the keys generated on a laptop & the secret key never leaving it. This dataset is that session, complete: the keys, every ciphertext that went to the server and every ciphertext that came back, the plaintext rows that were encrypted, the rows the laptop decrypted, and the tokens. With it, anyone can check on a laptop, in a few minutes, that the server's replies decrypt to the model's output, that the requests were the embeddings of the recorded tokens, and that the tokens the session produced follow…