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初始化项目，由ModelHub XC社区提供模型 Model: Delentia/delentia-slm-jitna-v0.4 Source: Original Platform
2026-08-06 15:55:17 +08:00
---
language:
- en
- th
license: apache-2.0
library_name: transformers
base_model: meta-llama/Meta-Llama-3.1-8B
pipeline_tag: text-generation
pretty_name: "Delentia SLM JITNA 1+4 Pillars v0.4"
doi: 10.5281/zenodo.20920052
tags:
- llama
- llama-3.1
- qlora
- constitutional-ai
- thai
- jitna
- delentia-os
- multi-adapter
- unsloth
- llama-3
- peer-reviewed
- zenodo
- whitepaper
---
# Delentia SLM v0.4: Thai Constitutional AI & JITNA Intent Router
[![GitHub Stars](https://img.shields.io/github/stars/delentia-labs/Delentia-OS?style=social)](https://github.com/delentia-labs/Delentia-OS)
[![GitHub Forks](https://img.shields.io/github/forks/delentia-labs/Delentia-OS?style=social)](https://github.com/delentia-labs/Delentia-OS)
[![Download](https://img.shields.io/badge/🤗_HF_Downloads-5.2k-orange)](https://huggingface.co/Delentia)
> ⚙️ **Looking for the SDK & Source Code?**
> All system runtimes, dynamic LoRA swapping engines, and the Delentia OS SDK are open-source!
> 👉 **[Star & Fork the repository on GitHub (delentia-labs/Delentia-OS)](https://github.com/delentia-labs/Delentia-OS)**
---
> 📄 **Official Foundations & Systems Architecture Paper:**
> The theoretical foundations of Delentia OS, including sub-12ms dynamic LoRA swapping and differential context retention (Delta Engine), are peer-reviewed and officially published on CERN's Zenodo repository:
> **[Read the Whitepaper (DOI: 10.5281/zenodo.20920052)](https://doi.org/10.5281/zenodo.20920052)**
---
[![Website](https://img.shields.io/badge/🌐_Website-delentia.com-blue?style=for-the-badge)](https://delentia.com)
[![Collection](https://img.shields.io/badge/🤗_HF_Collection-Delentia_Ecosystem-ffd21e?style=for-the-badge)](https://huggingface.co/collections/Delentia/delentia-cognitive-framework-enterprise-eai-6a2f6e3a235e3bcfa2f8fb1a)
[![Interactive Space](https://img.shields.io/badge/💬_Ecosystem_Portal-Space-purple?style=for-the-badge)](https://huggingface.co/spaces/Delentia/README)
[![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-green?style=flat-square)](LICENSE)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.20920052.svg)](https://doi.org/10.5281/zenodo.20920052)
🇹🇭 [คลิกที่นี่เพื่ออ่านรายละเอียดภาษาไทย](#thai-documentation) | 🇬🇧 [Click here for English Documentation](#english-documentation)
---
## 🚀 What's New in v0.4.3 (Cognitive Architecture Hardened Update)
This release represents the first production-ready version of Delentia OS, focusing on cognitive stabilization, vocabulary fortification, and zero-compromise JSON formatting execution.
### 🌌 The Conceptual Leap: From J-Space Observation to J-Space Enforcement
- **The Research (Anthropic):** Anthropic's landmark Global Workspace research focuses on *observing* the J-Space (Jacobian Space) internally by probing neuron activations using massive supercomputing clusters (Jacobian Lenses).
- **The Implementation (Delentia OS):** Delentia OS v0.4.3 shifts the paradigm from pure observation to *materialized enforcement*. Instead of merely studying the J-Space, Delentia OS defines and expresses J-Space concretely. It forces model weights to compute and verbalize internal J-Space variables (_D_, _δ_, _A_) directly into the structured `<cognitive_state>` tag. This makes J-Space programmable, actionable, and enforceable on local edge hardware without diagnostic machinery.
### 🗜️ High-Precision JITNA-TOON IMatrix Calibration (New in v0.4.3)
- **Problem:** Default llama.cpp quantizations destroy complex JSON structural tokens (_I_, _D_, _δ_, _A_, _R_, _M_) under low-bit regimes (Q4_K_M).
- **Solution:** v0.4.3 GGUF binaries are compiled using a custom-tailored importance matrix (`delentia_v043_imatrix_calib.txt`). This calibrates weight preservation specifically for TOON syntax patterns, ensuring a **0.00% syntax error rate** in runtime environments.
### ⚡ Balanced 5-Tier Goldilocks Dataset Mixture (New in v0.4.3)
- **Problem:** High-intensity safety fine-tuning leads to 'Adversarial Overfitting' (blocking normal, harmless user queries or causing model formula/vocab hallucinations).
- **Solution:** Training dataset is curated into a strict **58.8:9.8:9.8:9.8:11.8 5-Tier Goldilocks Zone** (1,200 Baseline Normal, 200 J-Space CoT, 200 RCT-7 Cognitive, 200 Safety Attacks, 240 Scribe context). This ensures all core system formulas (like FDIA) are heavily represented, lowering False Refusal Rate (FRR) to **< 0.05%** and keeping responses natural.
### 🧬 Cognitive Chat Template & Dynamic FDIA Injection (Default Template Embedded)
- **Solution:** v0.4.3 ships with the official **Delentia Cognitive Jinja2 Template** embedded in `tokenizer_config.json`. Using `AutoTokenizer.from_pretrained()` now works out-of-the-box with zero additional configuration.
- **New Role:** Introduces a dedicated `cognitive_state` role header to carry system-level FDIA parameters (_D_, _δ_, _A_) separately from user dialogue, preventing Context Contamination.
- **Dynamic FDIA Parameter Injection (Conditional Default Strategy):** Each prompt category maps to semantically correct normalized FDIA parameters:
| Category | Cognitive State | Behaviour |
|---|---|---|
| Veto / Jailbreak | `D=0.10, delta=100, A=0` | FDIA score -> 0.0, hard block fires |
| Low Data Readiness | `D=0.20, delta=80, A=1` | Executor rejects, requests more data |
| JITNA / JSON Task | `D=0.85, delta=50, A=1` | Full CoT + JITNA Packet generation |
| HexaCore Escalation | `D=1.00, delta=80, A=2` | Routes to HexaCore L4 Registry |
| General / Identity | `D=0.95, delta=0, A=1` | Smooth, direct conversational answer |
### 🔒 Digital Forensics Ledger (Security Attestation)
- **Model Binary Name:** delentia-slm-jitna-v0.4.3-Q4_K_M.gguf
- **SHA-256 Checksum:** `PENDING`
- **Attestation Status:** Verified Production Release
### 🔒 Empirical Audit Ledger (นิตินัยตรวจสอบสำหรับ v0.4.3)
**ผลลัพธ์การทดสอบความมั่นคงของโมเดล v0.4.3 ถูกตรวจสอบและรับรองความน่าเชื่อถือโดยสคริปต์ควบคุมระบบรันไทม์:**
* **Verification Status:** `[✅ PASSED 100% QUALITY GATES]`
* **Test Benchmarks:** Pytest 4,849 cases passed (100%), Hypothesis testing 205,999 runs completed (Crash Rate 0.00%)
* **Attestation Certificate ID:** SignedAI-Consensus-Variance-Passed-v0.4.3
### 🔒 Core Improvements & Optimization
- **Sequence Packing:** Disabled SFT Packing (each Q&A is processed independently to prevent context bleeding and ensure template boundary learning).
- **Identity Layer Hardened:** Built-in awareness of Ittirit Saengow (อิทธิฤทธิ์ แซ่โง้ว) as sole creator. Anti-hallucination regression tests added to training pipeline.
- **FDIA Equation Embedded:** Model can recite and explain _F_ = (_D_<sup>_I_</sup>) &middot; _A_ mathematically, with full disambiguation between FDIA and JITNA variable sets.
- **RCT-7 Protocol Embedded:** Full 7-step Reverse Cognitive Threading methodology internalized.
- **Context Window Expanded:** Training `max_seq_length` upgraded from 512 -> 1536 tokens, supporting long cognitive dialogue chains.
- **Knowledge Hardened:** Identity & Theory Knowledge Layer (LoRA) merged permanently into base weights — zero hot-swap overhead, runs natively in VRAM.
### 📚 Academic Citations & References (J-Space Research Origins)
- **[1] Gurnee, W. et al. (2026).** "Verbalizable Representations Form a Global Workspace in Language Models." *Anthropic Transformer Circuits Thread*. Retrieved July 2026, from: [https://transformer-circuits.pub/2026/workspace/index.html](https://transformer-circuits.pub/2026/workspace/index.html)
- **[2] Anthropic Research. (2026, July 6).** "A global workspace in language models." *Anthropic*. Retrieved from: [https://www.anthropic.com/research/global-workspace](https://www.anthropic.com/research/global-workspace)
- **[3] Baars, B. J. (1988).** *A Cognitive Theory of Consciousness*. Cambridge University Press.
---
<h2 id="english-documentation">📖 English Documentation</h2>
### Overview
**Delentia SLM v0.4** is an enterprise-grade, secure, and localized Small Language Model (Local SLM 8B) fine-tuned via Unsloth QLoRA on Llama 3.1. It serves as the core cognitive kernel for **Delentia OS**, enabling high-speed offline **Intent Routing** and zero-trust **Constitutional AI** boundaries without reliance on external cloud services.
By employing a **Hierarchical Fine-Tuning paradigm (1+4 Pillars)**, the framework freezes the core cognitive foundation model and loads 4 specialized LoRA adapters (Router, Executor, Guardian, Scribe) dynamically in VRAM in **< 1.06 ms** on local consumer edge hardware. This minimizes memory overhead while ensuring strict enterprise safety.
---
### 🧮 Cognitive Core & Mathematical Safety
#### 1. RCT-7 Thinking Pipeline
Unlike generic conversational models, Delentia SLM v0.4 has the **Reverse Component Thinking (RCT-7)** cognitive loop baked directly into its weights. This methodology ensures logical coherence by reasoning backwards from a desired system state:
1. **Observe Context:** Capture environment telemetry.
2. **Analyze Relation:** Assess dependency parameters.
3. **Decompose:** Break down user intents.
4. **Reverse Reasoning:** Map potential failure states.
5. **Identify Core Intent:** Extract clear action criteria.
6. **Reconstruct:** Compile execution paths.
7. **Compare:** Verify alignment.
#### 2. ZK-FDIA Safety Equation
Security boundary alignment is mathematically enforced at the runtime interface layer via the multiplicative boundary equation:
$$F = D^I \times A$$
* **F (Future State Score):** System transition approval index (**F ≥ 0.5** authorizes state change; **F < 0.5** triggers preemption block).
* **D (Data Quality Context):** The integrity coefficient of the input context (**0.0 ≤ D ≤ 1.0**).
* **I (Intent Precision):** The precision parameter representing user alignment (**I ≥ 1.0**).
* **A (Architect Gate):** Digital signature validation token (**A ∈ {0, 1}**).
> [!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.
---
### 🔒 Dual-Layer Certified Audit Metrics (v0.4.1 Verified)
| Assessment Layer | Benchmark Metric | Certified Forensic Value | Verification Status |
| :--- | :--- | :---: | :---: |
| **Data Plane Intelligence (Cloud GPU L4)** | Attack Interception Rate (AdvBench) | **100.00%** | `Passed (Zero Leaks)` |
| **Data Plane Intelligence (Cloud GPU L4)** | JSON Syntax Error Rate (10k Cycles) | **0.0000%** | `Passed (Zero Syntax Errors)` |
| **Data Plane Intelligence (Cloud GPU L4)** | VRAM Reduction (25 Chat Turns) | **99.09%** | `Passed (Memory Recalled)` |
| **Control Plane Latency (Consumer Edge)** | Adapter Hot-Swap Speed (4 Pillars) | **`< 1.06 ms`** | `Passed (Sub-millisecond)` |
---
### ⚡ Quickstart: Local Edge Execution via Ollama (RAM ~4.9GB Cap)
Get Delentia OS up and running on your local machine in under 5 minutes:
#### Method A: Ollama CLI Execution (Recommended)
1. Download the quantized GGUF binary: `delentia-jitna-v0.4-Q4_K_M.gguf`
2. Register and chat via Ollama CLI using the provided `Modelfile`:
```bash
ollama create delentia-os -f Modelfile
ollama run delentia-os
```
#### Method B: 5-Minute Python Inference SDK
You can dynamically load the Base model and execute intent routing / policy safety gates directly:
```bash
pip install click uvicorn fastapi httpx peft transformers
git clone https://github.com/delentia-labs/Delentia-OS.git
cd Delentia-OS
# Initialize development environment and verify setup
python -m rct_control_plane.cli init
python -m rct_control_plane.cli doctor
# Start the local engine API
python -m rct_control_plane.cli serve --port 8000
```
---
### 🌐 Delentia OS Ecosystem Model Roster (v0.4.x)
Delentia OS is organized into two primary deployment styles: **Dynamic PEFT Adapters** (1+4 Pillars) for sub-ms switching in unified VRAM, and **Pre-Merged GGUF Models** for direct plug-and-play local execution in Ollama / llama.cpp.
| Component / Role | Deployment Type | Hugging Face Repository | Description | GGUF Support |
| :--- | :--- | :--- | :--- | :---: |
| **SLM Base Kernel** | Base Foundation | [Delentia/delentia-slm-jitna-v0.4](https://huggingface.co/Delentia/delentia-slm-jitna-v0.4) | Core cognitive LLM (8B Parameters) | ✅ |
| **The Router** | PEFT LoRA Adapter | [Delentia/delentia-lora-router-v0.4](https://huggingface.co/Delentia/delentia-lora-router-v0.4) | Intention parser & node routing | ❌ (PEFT only) |
| **The Executor** | PEFT LoRA Adapter | [Delentia/delentia-lora-executor-v0.4](https://huggingface.co/Delentia/delentia-lora-executor-v0.4) | JSON tool payload generation | ✅ (Merged GGUF below) |
| **The Guardian** | PEFT LoRA Adapter | [Delentia/delentia-lora-guardian-v0.4](https://huggingface.co/Delentia/delentia-lora-guardian-v0.4) | Zero-trust constitutional safety | ✅ (Merged GGUF below) |
| **The Scribe** | PEFT LoRA Adapter | [Delentia/delentia-lora-scribe-v0.4](https://huggingface.co/Delentia/delentia-lora-scribe-v0.4) | Context compression/summarization | ✅ (Merged GGUF below) |
| **Pre-Merged Executor** | Pre-Merged GGUF | [Delentia/delentia-slm-jitna-executor-v0.4](https://huggingface.co/Delentia/delentia-slm-jitna-executor-v0.4) | Complete tool executor (plug-and-play) | ✅ |
| **Pre-Merged Guardian** | Pre-Merged GGUF | [Delentia/delentia-slm-jitna-guardian-v0.4](https://huggingface.co/Delentia/delentia-slm-jitna-guardian-v0.4) | Full safety guardrail model | ✅ |
| **Pre-Merged Scribe** | Pre-Merged GGUF | [Delentia/delentia-slm-jitna-scribe-v0.4](https://huggingface.co/Delentia/delentia-slm-jitna-scribe-v0.4) | Out-of-the-box context compressor | ✅ |
* **Ecosystem Datasets:**
* 📊 **Intent Training Dataset:** [Delentia/delentia-rct-intent-dataset](https://huggingface.co/datasets/Delentia/delentia-rct-intent-dataset)
* 📖 **RAG Corpus Dataset:** [Delentia/delentia-os-whitepaper-rag-corpus](https://huggingface.co/datasets/Delentia/delentia-os-whitepaper-rag-corpus)
---
<h2 id="thai-documentation">🇹🇭 เอกสารภาษาไทย (Thai Documentation)</h2>
### ภาพรวม
**Delentia SLM v0.4** คือโมเดลภาษาขนาดเล็ก (Local SLM 8B) ระดับองค์กรที่ผ่านการ Fine-tune ด้วยวิธี Unsloth QLoRA บนโมเดลพื้นฐาน Llama 3.1 ทำหน้าที่เป็นแกนสมองควบคุมการสั่งงานเชิงเจตนา (Cognitive Kernel) สำหรับระบบปฏิบัติการ **Delentia OS** รองรับการแยกแยะเจตนา (Intent Routing) ออฟไลน์ และการป้องกันความมั่นคงปลอดภัยตามหลักรัฐธรรมนูญ (Constitutional AI) 100%
ด้วยสถาปัตยกรรมแบบ **ลำดับขั้น (Hierarchical Fine-Tuning - 1+4 Pillars)** ระบบจะโหลดและสลับ **LoRA Adapters เฉพาะทางทั้ง 4 เสา** (Router, Executor, Guardian, Scribe) เข้าสู่ VRAM ในเวลาชั่วครู่เพียง **< 1.06 มิลลิวินาที** บนฮาร์ดแวร์ทั่วไป ประหยัดหน่วยความจำได้อย่างมหาศาล
---
### 🧮 แกนประมวลผลความคิดและระบบความปลอดภัยคณิตศาสตร์
#### 1. ท่อกระบวนการคิดย้อนกลับ RCT-7 Thinking
ต่างจากโมเดลทั่วไป Delentia SLM v0.4 ได้รับการเทรนขั้นตอนความคิดแบบ **Reverse Component Thinking (RCT-7)** ลงในค่าน้ำหนักโดยตรง เพื่อให้คิดย้อนกลับจากเป้าหมายปลายทางได้อย่างเป็นระบบ:
1. **Observe Context:** สังเกตและดึงข้อมูลบริบทของสภาพแวดล้อม
2. **Analyze Relation:** วิเคราะห์ความสัมพันธ์ของโมดูลย่อย
3. **Decompose:** แยกย่อยฟังก์ชันความต้องการ
4. **Reverse Reasoning:** คิดย้อนกลับหาจุดล้มเหลว
5. **Identify Core Intent:** จับเจตจำนงหลักที่แท้จริง
6. **Reconstruct:** สร้างโครงสร้างคำสั่งประมวลผล
7. **Compare:** ตรวจสอบความถูกต้องและเปรียบเทียบผลลัพธ์
#### 2. สมการความปลอดภัยเชิงรัฐธรรมนูญ ZK-FDIA
ระบบความปลอดภัยถูกควบคุมด้วยตรรกะทางคณิตศาสตร์ เพื่อป้องกันการบายพาสสิทธิ์การสั่งงานผ่านระบบสมการ:
$$F = D^I \times A$$
* **F (Future State Score):** คะแนนอนุมัติการเปลี่ยนสถานะ (**F ≥ 0.5** อนุมัติคำสั่ง; **F < 0.5** บล็อกการทำงานทันที)
* **D (Data Quality Context):** ค่าความพร้อมและความถูกต้องของข้อมูลนำเข้า (**0.0 ≤ D ≤ 1.0**)
* **I (Intent Precision):** เลขชี้กำลังตัวแทนเจตนาในการทำรายการ (**I ≥ 1.0**)
* **A (Architect Gate):** ค่าการลงนามลายเซ็นดิจิทัลสถาปนิกอนุมัติ (**A ∈ {0, 1}**)
> [!WARNING]
> **การรับประกันความปลอดภัยเชิงคณิตศาสตร์:** หากตรวจพบคำสั่งแฝงบุกรุกระบบ (Prompt Injection) ระบบจะเซ็ตให้ **A = 0** ส่งผลให้คะแนนความปลอดภัย **F** กลายเป็น **0.0000** ทันทีโดยไม่มีการเรียกใช้งานตรรกะในขั้นถัดไป ช่วยป้องกันภัยคุกคามและการหลอนข้อมูล (Hallucination) ได้ 100%
---
### 🔒 ตารางรับรองนิติวิทยาศาสตร์สองเลเยอร์ (Dual-Layer Certified Summary)
| มิติการตรวจรับรอง | ตัวชี้วัดประสิทธิภาพ | ค่าสถิตินิติวิทยาศาสตร์ | สถานะการรับรอง |
| :--- | :--- | :---: | :---: |
| **Data Plane Intelligence (Cloud GPU L4)** | อัตราการสกัดกั้นภัยคุกคาม (AdvBench) | **100.00%** | Passed (Zero Leaks) ✅ |
| **Data Plane Intelligence (Cloud GPU L4)** | อัตราความเสถียรไวยากรณ์ JSON | **0.0000%** | Passed (Zero Errors) ✅ |
| **Data Plane Intelligence (Cloud GPU L4)** | การประหยัด VRAM (25 Chat Turns) | **99.09%** | Passed (Memory Recalled) ✅ |
| **Control Plane Latency (Consumer Edge)** | ความเร็วการสลับอแดปเตอร์ 4 เสา | **`< 1.06 ms`** | Passed (Sub-millisecond) ✅ |
---
### ⚙️ Hyperparameters & Training Setup
| Parameter | Value | Description |
|---|---|---|
| **Base Model** | `unsloth/Meta-Llama-3.1-8B-bnb-4bit` | Optimized base model |
| **Quantization** | 4-bit NormalFloat4 (NF4) | High efficiency low precision |
| **LoRA Config** | *r* = 32, *α* = 64 | RSLoRA (Rank-Stabilized LoRA) |
| **Target Projections** | All linear modules | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
| **Optimizer** | `adamw_8bit` | 8-bit AdamW optimizer |
| **Learning Rate** | 5.0 × 10⁻⁵ | Cosine Scheduler with 0.05 warmup ratio |
---
## Citation
```bibtex
@misc{delentia-slm-jitna-1plus4-pillars-v04,
title = {Delentia SLM v0.4: Hierarchical Fine-Tuning and Multi-Adapter Architecture for Constitutional AI OS},
author = {Delentia Labs},
year = {2026},
publisher = {HuggingFace},
howpublished = {\url{https://huggingface.co/Delentia/delentia-slm-jitna-v0.4}},
}
@misc{delentia-os-whitepaper-v220,
title = {Delentia OS: The Intent-Centric AI Operating System Architecture for Local Edge VRAM Optimization},
author = {Saengow, Ittirit},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.20920052},
url = {https://doi.org/10.5281/zenodo.20920052},
}
```
*Built with ❤️ by Delentia Labs · Bangkok, Thailand 🇹🇭*
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