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@markhammond markhammond released this 29 Aug 13:25
v0.1.0
5094cda

Debut release of native .NET inference for Kronos, a pre-trained autoregressive model over K-line (OHLCVA) sequences. Runs on CPU and supported GPUs via TorchSharp.

Packages

Package
Kronos.Forecasting tokenizer, model, forecaster
Kronos.Forecasting.Weights.Small Kronos-small (24.7M) + Tokenizer-base
Kronos.Forecasting.Weights.Mini Kronos-mini (4.1M) + Tokenizer-2k 
dotnet add package Kronos.Forecasting
dotnet add package Kronos.Forecasting.Weights.Small   # or .Mini
dotnet add package TorchSharp-cpu                     # or a CUDA backend

Checkpoints are embedded as assembly resources. The native backend is not included; choose the TorchSharp package matching your intended target.

Included

  • Kronos tokenizer (encode and decode) and autoregressive model, ported to TorchSharp
  • Safetensors reader, so checkpoints load without Python at build or run time
  • IKronosCheckpoint: weights as streams, from embedded resources or a directory
  • Verified stage by stage against the reference implementation on CPU and Metal

Deliberate divergences

  • Sampling is batch-invariant. Per-bar uniforms from a SplitMix64 stream seeded by the bar's own timestamp, selected by inverse CDF, so a draw does not depend on how bars were grouped into batches. The reference seeds one stream per batch and is not batch-invariant. Distributionally identical; the stream is not.
  • No key-value cache. Attention is recomputed over the whole context at every decode step, matching the reference. A cache would change the arithmetic and forfeit checkable parity.
  • Not bit-identical across devices (~1e-7 on the projected return). A determinism contract should pin one device rather than assume CPU and GPU agree.

Attribution

Model architecture, BSQ tokenizer and reference implementation by ShiYu (MIT). Checkpoints redistributed unmodified from NeoQuasar (MIT) at the revisions recorded in LICENSE.

Assets 2
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