What is TIEG.SystemOne.LLamaSharp?

A high-performance, deterministic routing engine for AI models built for LLamaSharp by The Intelligent Enterprise Group, implementing a System One architectural pattern.

How does it decide without generating text?

It extracts raw logits from a single parallel batch pass to determine the probability of specific categories, bypassing the token-by-token generation that takes seconds.

What do the universal thresholds do?

The Margin and Dominance Ratios evaluate the model's internal hesitation so the thresholds stay mathematically safe across 2-option and 10-option questions.

What is the performance claim?

Sub-100ms deterministic classification with specialized smaller models, while saving VRAM for larger fallback generative models — a claim from the README with no methodology in the digest.

It is published on NuGet as TIEG.SystemOne.LLamaSharp per the README badges; the digest does not capture the install command or supported versions.

Is it connected to Jev or TypeSafe?

Not in the captured text: it independently implements the System One pattern for .NET, and the README excerpt does not mention Jev or TypeSafe.

What license and status does it have?

MIT licensed, C#, 1 star and 1 fork, created 2026-09-28 and last pushed 2026-10-04, per the digest's GitHub metadata.

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TIEG.SystemOne.LLamaSharp

A .NET extension for LLamaSharp implementing a System One pattern: it extracts raw logits from a single parallel batch pass to classify in under 100 ms, bypassing autoregressive generation and reserving VRAM for larger fallback models.

See highlights Open github.com

Stars: 1
Language: C#
License: MIT
Reading time: 2 min
Published by: Community
Author: TheIntelligentEnterpriseGroup
Last verified: Oct 2026

TheIntelligentEnterpriseGroup/SystemOne.LLamaSharp

User repository on GitHub

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Sub-100ms deterministic decision routing and VRAM management for LLamaSharp. Developed by The Intelligent Enterprise Group (TIEG), this extension bypasses autoregressive token generation in favor of logit-based Softmax evaluation for instant classification.

Forks: 1
Last pushed: Updated Oct 2026

Repo stats from the GitHub API, cached Oct 2026.

Tagscommunity github dotnet llamasharp nuget

Works forJev for Classification Jev for Routing

Used inJev for Software & DevTools

Languages: C# / .NET
Capabilities: Open sourceRuns locally

Highlights

Sub-100ms deterministic decision routing for LLamaSharp by reading raw logits from one parallel batch pass.
Bypasses autoregressive token generation for instant classification, saving VRAM for larger fallback generative models.
Universal thresholds use Margin and Dominance Ratios so 2-option and 10-option questions stay mathematically safe.
Thread-locking is listed for VRAM safety; the package is published on NuGet per the README badges.
GitHub metadata: 1 star, 1 fork, C#, MIT, created 2026-09-28 and pushed 2026-10-04.

Watch out

README and GitHub metadata reviewed 2026-10-17; the digest stops inside the VRAM Safety feature bullet, so the API surface, install details and benchmark methodology are not captured. The sub-100ms figure is the project's own claim.

Questions about this entry

Short answers drawn from the linked project or page.

From the Jev guides

Original write-ups on what this entry is for.

The Open Reimplementations: Local Models That Speak System One

Sep 22, 2026

Closed weights have not stopped the ecosystem from rebuilding the shape in the open. API-compatible servers, retrained heads on small models, and inference-time tricks that skip generation entirely.

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