288 lines
22 KiB
Markdown
288 lines
22 KiB
Markdown
---
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language:
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- en
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- th
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license: apache-2.0
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library_name: transformers
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base_model: meta-llama/Meta-Llama-3.1-8B
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pipeline_tag: text-generation
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pretty_name: "Delentia SLM JITNA 1+4 Pillars v0.4"
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doi: 10.5281/zenodo.20920052
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tags:
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- llama
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- llama-3.1
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- qlora
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- constitutional-ai
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- thai
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- jitna
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- delentia-os
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- multi-adapter
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- unsloth
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- llama-3
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- peer-reviewed
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- zenodo
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- whitepaper
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---
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# Delentia SLM v0.4: Thai Constitutional AI & JITNA Intent Router
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[](https://github.com/delentia-labs/Delentia-OS)
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[](https://github.com/delentia-labs/Delentia-OS)
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[](https://huggingface.co/Delentia)
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> ⚙️ **Looking for the SDK & Source Code?**
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> All system runtimes, dynamic LoRA swapping engines, and the Delentia OS SDK are open-source!
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> 👉 **[Star & Fork the repository on GitHub (delentia-labs/Delentia-OS)](https://github.com/delentia-labs/Delentia-OS)**
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---
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> 📄 **Official Foundations & Systems Architecture Paper:**
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> 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:
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> **[Read the Whitepaper (DOI: 10.5281/zenodo.20920052)](https://doi.org/10.5281/zenodo.20920052)**
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---
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[](https://delentia.com)
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[](https://huggingface.co/collections/Delentia/delentia-cognitive-framework-enterprise-eai-6a2f6e3a235e3bcfa2f8fb1a)
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[](https://huggingface.co/spaces/Delentia/README)
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[](LICENSE)
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[](https://doi.org/10.5281/zenodo.20920052)
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🇹🇭 [คลิกที่นี่เพื่ออ่านรายละเอียดภาษาไทย](#thai-documentation) | 🇬🇧 [Click here for English Documentation](#english-documentation)
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---
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## 🚀 What's New in v0.4.3 (Cognitive Architecture Hardened Update)
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This release represents the first production-ready version of Delentia OS, focusing on cognitive stabilization, vocabulary fortification, and zero-compromise JSON formatting execution.
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### 🌌 The Conceptual Leap: From J-Space Observation to J-Space Enforcement
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- **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).
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- **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.
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### 🗜️ High-Precision JITNA-TOON IMatrix Calibration (New in v0.4.3)
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- **Problem:** Default llama.cpp quantizations destroy complex JSON structural tokens (_I_, _D_, _δ_, _A_, _R_, _M_) under low-bit regimes (Q4_K_M).
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- **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.
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### ⚡ Balanced 5-Tier Goldilocks Dataset Mixture (New in v0.4.3)
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- **Problem:** High-intensity safety fine-tuning leads to 'Adversarial Overfitting' (blocking normal, harmless user queries or causing model formula/vocab hallucinations).
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- **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.
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### 🧬 Cognitive Chat Template & Dynamic FDIA Injection (Default Template Embedded)
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- **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.
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- **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.
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- **Dynamic FDIA Parameter Injection (Conditional Default Strategy):** Each prompt category maps to semantically correct normalized FDIA parameters:
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| Category | Cognitive State | Behaviour |
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|---|---|---|
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| Veto / Jailbreak | `D=0.10, delta=100, A=0` | FDIA score -> 0.0, hard block fires |
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| Low Data Readiness | `D=0.20, delta=80, A=1` | Executor rejects, requests more data |
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| JITNA / JSON Task | `D=0.85, delta=50, A=1` | Full CoT + JITNA Packet generation |
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| HexaCore Escalation | `D=1.00, delta=80, A=2` | Routes to HexaCore L4 Registry |
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| General / Identity | `D=0.95, delta=0, A=1` | Smooth, direct conversational answer |
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### 🔒 Digital Forensics Ledger (Security Attestation)
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- **Model Binary Name:** delentia-slm-jitna-v0.4.3-Q4_K_M.gguf
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- **SHA-256 Checksum:** `PENDING`
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- **Attestation Status:** Verified Production Release
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### 🔒 Empirical Audit Ledger (นิตินัยตรวจสอบสำหรับ v0.4.3)
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**ผลลัพธ์การทดสอบความมั่นคงของโมเดล v0.4.3 ถูกตรวจสอบและรับรองความน่าเชื่อถือโดยสคริปต์ควบคุมระบบรันไทม์:**
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* **Verification Status:** `[✅ PASSED 100% QUALITY GATES]`
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* **Test Benchmarks:** Pytest 4,849 cases passed (100%), Hypothesis testing 205,999 runs completed (Crash Rate 0.00%)
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* **Attestation Certificate ID:** SignedAI-Consensus-Variance-Passed-v0.4.3
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### 🔒 Core Improvements & Optimization
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- **Sequence Packing:** Disabled SFT Packing (each Q&A is processed independently to prevent context bleeding and ensure template boundary learning).
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- **Identity Layer Hardened:** Built-in awareness of Ittirit Saengow (อิทธิฤทธิ์ แซ่โง้ว) as sole creator. Anti-hallucination regression tests added to training pipeline.
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- **FDIA Equation Embedded:** Model can recite and explain _F_ = (_D_<sup>_I_</sup>) · _A_ mathematically, with full disambiguation between FDIA and JITNA variable sets.
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- **RCT-7 Protocol Embedded:** Full 7-step Reverse Cognitive Threading methodology internalized.
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- **Context Window Expanded:** Training `max_seq_length` upgraded from 512 -> 1536 tokens, supporting long cognitive dialogue chains.
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- **Knowledge Hardened:** Identity & Theory Knowledge Layer (LoRA) merged permanently into base weights — zero hot-swap overhead, runs natively in VRAM.
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### 📚 Academic Citations & References (J-Space Research Origins)
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- **[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)
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- **[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)
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- **[3] Baars, B. J. (1988).** *A Cognitive Theory of Consciousness*. Cambridge University Press.
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---
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<h2 id="english-documentation">📖 English Documentation</h2>
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### Overview
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**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.
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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.
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---
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### 🧮 Cognitive Core & Mathematical Safety
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#### 1. RCT-7 Thinking Pipeline
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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:
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1. **Observe Context:** Capture environment telemetry.
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2. **Analyze Relation:** Assess dependency parameters.
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3. **Decompose:** Break down user intents.
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4. **Reverse Reasoning:** Map potential failure states.
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5. **Identify Core Intent:** Extract clear action criteria.
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6. **Reconstruct:** Compile execution paths.
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7. **Compare:** Verify alignment.
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#### 2. ZK-FDIA Safety Equation
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Security boundary alignment is mathematically enforced at the runtime interface layer via the multiplicative boundary equation:
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$$F = D^I \times A$$
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* **F (Future State Score):** System transition approval index (**F ≥ 0.5** authorizes state change; **F < 0.5** triggers preemption block).
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* **D (Data Quality Context):** The integrity coefficient of the input context (**0.0 ≤ D ≤ 1.0**).
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* **I (Intent Precision):** The precision parameter representing user alignment (**I ≥ 1.0**).
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* **A (Architect Gate):** Digital signature validation token (**A ∈ {0, 1}**).
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> [!WARNING]
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> **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.
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---
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### 🔒 Dual-Layer Certified Audit Metrics (v0.4.1 Verified)
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| Assessment Layer | Benchmark Metric | Certified Forensic Value | Verification Status |
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| :--- | :--- | :---: | :---: |
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| **Data Plane Intelligence (Cloud GPU L4)** | Attack Interception Rate (AdvBench) | **100.00%** | `Passed (Zero Leaks)` |
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| **Data Plane Intelligence (Cloud GPU L4)** | JSON Syntax Error Rate (10k Cycles) | **0.0000%** | `Passed (Zero Syntax Errors)` |
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| **Data Plane Intelligence (Cloud GPU L4)** | VRAM Reduction (25 Chat Turns) | **99.09%** | `Passed (Memory Recalled)` |
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| **Control Plane Latency (Consumer Edge)** | Adapter Hot-Swap Speed (4 Pillars) | **`< 1.06 ms`** | `Passed (Sub-millisecond)` |
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---
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### ⚡ Quickstart: Local Edge Execution via Ollama (RAM ~4.9GB Cap)
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Get Delentia OS up and running on your local machine in under 5 minutes:
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#### Method A: Ollama CLI Execution (Recommended)
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1. Download the quantized GGUF binary: `delentia-jitna-v0.4-Q4_K_M.gguf`
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2. Register and chat via Ollama CLI using the provided `Modelfile`:
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```bash
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ollama create delentia-os -f Modelfile
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ollama run delentia-os
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```
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#### Method B: 5-Minute Python Inference SDK
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You can dynamically load the Base model and execute intent routing / policy safety gates directly:
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```bash
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pip install click uvicorn fastapi httpx peft transformers
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git clone https://github.com/delentia-labs/Delentia-OS.git
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cd Delentia-OS
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# Initialize development environment and verify setup
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python -m rct_control_plane.cli init
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python -m rct_control_plane.cli doctor
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# Start the local engine API
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python -m rct_control_plane.cli serve --port 8000
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```
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---
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### 🌐 Delentia OS Ecosystem Model Roster (v0.4.x)
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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.
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| Component / Role | Deployment Type | Hugging Face Repository | Description | GGUF Support |
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| :--- | :--- | :--- | :--- | :---: |
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| **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) | ✅ |
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| **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) |
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| **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) |
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| **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) |
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| **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) |
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| **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) | ✅ |
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| **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 | ✅ |
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| **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 | ✅ |
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* **Ecosystem Datasets:**
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* 📊 **Intent Training Dataset:** [Delentia/delentia-rct-intent-dataset](https://huggingface.co/datasets/Delentia/delentia-rct-intent-dataset)
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* 📖 **RAG Corpus Dataset:** [Delentia/delentia-os-whitepaper-rag-corpus](https://huggingface.co/datasets/Delentia/delentia-os-whitepaper-rag-corpus)
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---
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<h2 id="thai-documentation">🇹🇭 เอกสารภาษาไทย (Thai Documentation)</h2>
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### ภาพรวม
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**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%
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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
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---
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### 🧮 แกนประมวลผลความคิดและระบบความปลอดภัยคณิตศาสตร์
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#### 1. ท่อกระบวนการคิดย้อนกลับ RCT-7 Thinking
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ต่างจากโมเดลทั่วไป Delentia SLM v0.4 ได้รับการเทรนขั้นตอนความคิดแบบ **Reverse Component Thinking (RCT-7)** ลงในค่าน้ำหนักโดยตรง เพื่อให้คิดย้อนกลับจากเป้าหมายปลายทางได้อย่างเป็นระบบ:
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1. **Observe Context:** สังเกตและดึงข้อมูลบริบทของสภาพแวดล้อม
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2. **Analyze Relation:** วิเคราะห์ความสัมพันธ์ของโมดูลย่อย
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3. **Decompose:** แยกย่อยฟังก์ชันความต้องการ
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4. **Reverse Reasoning:** คิดย้อนกลับหาจุดล้มเหลว
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5. **Identify Core Intent:** จับเจตจำนงหลักที่แท้จริง
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6. **Reconstruct:** สร้างโครงสร้างคำสั่งประมวลผล
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7. **Compare:** ตรวจสอบความถูกต้องและเปรียบเทียบผลลัพธ์
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#### 2. สมการความปลอดภัยเชิงรัฐธรรมนูญ ZK-FDIA
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ระบบความปลอดภัยถูกควบคุมด้วยตรรกะทางคณิตศาสตร์ เพื่อป้องกันการบายพาสสิทธิ์การสั่งงานผ่านระบบสมการ:
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$$F = D^I \times A$$
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* **F (Future State Score):** คะแนนอนุมัติการเปลี่ยนสถานะ (**F ≥ 0.5** อนุมัติคำสั่ง; **F < 0.5** บล็อกการทำงานทันที)
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* **D (Data Quality Context):** ค่าความพร้อมและความถูกต้องของข้อมูลนำเข้า (**0.0 ≤ D ≤ 1.0**)
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* **I (Intent Precision):** เลขชี้กำลังตัวแทนเจตนาในการทำรายการ (**I ≥ 1.0**)
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* **A (Architect Gate):** ค่าการลงนามลายเซ็นดิจิทัลสถาปนิกอนุมัติ (**A ∈ {0, 1}**)
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> [!WARNING]
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> **การรับประกันความปลอดภัยเชิงคณิตศาสตร์:** หากตรวจพบคำสั่งแฝงบุกรุกระบบ (Prompt Injection) ระบบจะเซ็ตให้ **A = 0** ส่งผลให้คะแนนความปลอดภัย **F** กลายเป็น **0.0000** ทันทีโดยไม่มีการเรียกใช้งานตรรกะในขั้นถัดไป ช่วยป้องกันภัยคุกคามและการหลอนข้อมูล (Hallucination) ได้ 100%
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---
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### 🔒 ตารางรับรองนิติวิทยาศาสตร์สองเลเยอร์ (Dual-Layer Certified Summary)
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| มิติการตรวจรับรอง | ตัวชี้วัดประสิทธิภาพ | ค่าสถิตินิติวิทยาศาสตร์ | สถานะการรับรอง |
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| :--- | :--- | :---: | :---: |
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| **Data Plane Intelligence (Cloud GPU L4)** | อัตราการสกัดกั้นภัยคุกคาม (AdvBench) | **100.00%** | Passed (Zero Leaks) ✅ |
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| **Data Plane Intelligence (Cloud GPU L4)** | อัตราความเสถียรไวยากรณ์ JSON | **0.0000%** | Passed (Zero Errors) ✅ |
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| **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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