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Delentia/jitna-v0.5.1-27B-gguf

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face
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Model Card

Delentia OS v0.5.1 (27B) โ€” Sovereign Multi-Pillar GGUF

<p align="center"> <b>Sovereign AI for Everyone: The Ultimate "Agent A" powered by FDIA (Full Data Internalization Architecture)</b> </p>

Secure & Private | IQ1_S Compressed (~6.7 GB) | Native Vision Support | 100% llama.cpp Compatible

๐ŸŒŸ Highlights & Conceptual Leaps

  • โ€”The "Agent A" Paradigm: Delentia is not a centralized chatbot. It is a sovereign OS controller designed to run locally on your hardware, fully adaptable to your personal or enterprise needs.
  • โ€”J-Space Enforcement: While top-tier labs (like Anthropic) observe Jacobian Space (J-Space) using massive supercomputers, Delentia enforces it. It forces model weights to verbalize internal J-Space variables directly into the structured <cognitive_state> tag, making AI reasoning programmable and completely controllable on Edge hardware.
  • โ€”FDIA (Full Data Internalization Architecture): Your data never leaves your device. Uncompromising privacy combined with state-of-the-art 27B reasoning, enforced by mathematical logic.
  • โ€”The 4 Pillars of Intelligence: Masterfully fine-tuned on the JITNA/TOON protocol encompassing Executor (action), Guardian (security), Router (logic), and Scribe (memory).
  • โ€”Extreme Compression (IQ1_S): ~6.7 GB footprint down from 54 GB. Fits perfectly on mobile devices and everyday laptops without sacrificing complex logic.
  • โ€”Flawless TOON Syntax: Quantized using a custom 13MB IMatrix calibration dataset (delentia_v0.5.1_imatrix_calib.dat) to preserve exact JSON routing and reasoning paths even at 1.56 bits, maintaining a 0.00% syntax error rate.

๐Ÿงฎ Cognitive Architecture & Mathematical Safety

The RCT-7 Thinking Pipeline

Unlike generic conversational models, Delentia OS has the Reverse Component Thinking (RCT-7) cognitive loop baked directly into its weights to ensure logical coherence:

  1. 1.Observe Context: Capture environment telemetry.
  2. 2.Analyze Relation: Assess dependency parameters.
  3. 3.Decompose: Break down user intents.
  4. 4.Reverse Reasoning: Map potential failure states.
  5. 5.Identify Core Intent: Extract clear action criteria.
  6. 6.Reconstruct: Compile execution paths.
  7. 7.Compare: Verify alignment before execution.

ZK-FDIA Safety Equation

Security boundary alignment is mathematically enforced at the runtime interface layer via the multiplicative boundary equation:

F = (D^I) * A

  • โ€”F (Future State Score): System transition approval index (F >= 0.5 authorizes state change).
  • โ€”D (Data Quality Context): The integrity coefficient of the input context.
  • โ€”I (Intent Precision): The precision parameter representing user alignment.
  • โ€”A (Architect Gate): Digital signature validation token.
[!WARNING] Mathematical Preemption Proof: Since A is a direct multiplier, if authorization fails or the input contains adversarial injections (prompt override, jailbreak), the system sets A = 0. This collapses the future safety score F to 0.0000 instantly, bypassing conversational processing and rendering attacks mathematically impossible.

๐Ÿ“Š Model Overview

ItemSpecification
Base ModelQwen3.6-27B (MTP layers safely stripped)
ArchitectureSovereign OS Controller / Multi-Pillar
ProtocolTOON / JITNA (Strict JSON syntax routing)
QuantizationGGUF (IQ1S, Q4K_M) calibrated via Custom IMatrix
VisionMulti-Modal Projector included (F16)

๐Ÿ“‚ Included Files

FileSizeDescription
jitna_v051_f16.gguf~54.0 GBMaster file for custom downstream quantization.
jitna_v051_Q4_K_M.gguf~16.0 GBHigh-fidelity for Desktop & Server environments.
jitna_v051_IQ1_S.gguf~6.7 GBUltra-compressed for Mobile & Edge devices.
delentia_v051_mmproj_f16.gguf~0.9 GBFull-precision Vision Encoder (Eyes of the AI).

๐Ÿš€ Quickstart (llama.cpp)

Text-only Inference (Mac/Linux/Windows):

bash
./llama-cli \
    -m jitna_v051_Q4_K_M.gguf \
    -p "<|im_start|>user\nInitiate system scan.<|im_end|>\n<|im_start|>assistant\n" \
    -n 512 -c 4096 --temp 0.2

Vision-enabled Inference:

bash
./llama-cli \
    -m jitna_v051_Q4_K_M.gguf \
    --mmproj delentia_v051_mmproj_f16.gguf \
    --image "document.png" \
    -p "<|im_start|>user\nExtract text from this image.<|im_end|>\n<|im_start|>assistant\n" \
    -n 512 -c 4096 --temp 0.2

๐Ÿ›ก๏ธ Best Practices & System Prompt

Because Delentia operates on the TOON protocol, a low temperature (e.g., 0.1 - 0.3) is highly recommended to maintain strict structural JSON syntax.

Recommended System Prompt:

text
You are Delentia, a sovereign Agent A operating under the Full Data Internalization Architecture (FDIA). You are bound by the TOON protocol. Execute commands logically, protect user privacy, and route tasks using strict JSON formatting.