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Models and Training

Language models, fine-tune checkpoints, reward models and the training infrastructure behind them.

NewmodelAGPL-3.0

tensor-roll

Recursive CUDA-Q model quantizer: compress a 30B-class teacher into an ~8B student under a hard parameter budget.

pythoncuda-q
modelApache-2.0

hilbert-4b-baseline-GGUF

Hilbert 4B decision baseline as Q8_0 and Q4_K_M GGUF, with row-level decision agreement against BF16 (Q8_0: 99.3%).

ggufllama.cpp
modelTri-License

sovereign-mimo-4b

4B parameter code reward model pruned from MiMo-7B. FSM pipeline, ERE gates, WORM sealing. Runs local on RTX 3080.

pythoncudagguf
modelTri-License

sovereign-qra

Deterministic routing tensor replacing softmax in MoE dispatch. Zero entropy, proved bijective. Lean 4 zero-sorry.

lean4veriloghaskellcuda
modelTri-License

sovlm

Sovereign statistical language model. N-grams, Kneser-Ney smoothing, BM25 retrieval, QRNG sampling.

modelAGPL-3.0

summon

Pip-installable framework for sovereign fine-tuning. QLoRA, functional API, layered corpus builder, WORM chain sealing.

modelAGPL-3.0

symbolic-morphology

From-scratch symbolic learner for Latin verb morphology: hand-derived gradients, no ML framework, implemented and benchmarked across 9 runtimes down to NAND gates.

rustf#pythonapl

Commercial license

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