delentia-slm-jitna-executor-v0.4/README.md

license, base_model, tags

license	base_model	tags
apache-2.0	Delentia/delentia-slm-jitna-v0.4	gguf llama-cpp text-generation tool-use delentia-os JITNA merged

Delentia SLM — The Pre-Merged Executor v0.4 (slm-jitna-executor-v0.4)

⚙️ 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)

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This is the Pre-Merged GGUF/Safetensors version of The Executor, the tool-calling engine of Delentia OS. The Executor adapter has been merged directly into the base kernel weights for high performance local edge execution via Ollama or llama.cpp without needing external adapter loading.

⚡ Quick Start: Local Edge Execution via Ollama

To run this tool executor model locally in under 5 minutes:

1. Download the GGUF model binary: delentia-slm-jitna-executor-v0.4-Q4_K_M.gguf
2. Create a local Modelfile with the following configuration:

FROM ./delentia-slm-jitna-executor-v0.4-Q4_K_M.gguf

TEMPLATE \"\"\"<|start_header_id|>system<|end_header_id|>
You are the Executor. Translate user intent into valid JSON/TOON format.
<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|end_header_id|>
\"\"\"

3. Register and run the model via Ollama CLI:

ollama create delentia-executor -f Modelfile
ollama run delentia-executor

⚡ Quick Start: Python Transformers

Alternatively, run the merged weights directly using Python Hugging Face Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Delentia/delentia-slm-jitna-executor-v0.4"

# Load the merged weights directly
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

🌐 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	Core cognitive LLM (8B Parameters)	✅
The Router	PEFT LoRA Adapter	Delentia/delentia-lora-router-v0.4	Intention parser & node routing	❌ (PEFT only)
The Executor	PEFT LoRA Adapter	Delentia/delentia-lora-executor-v0.4	JSON tool payload generation	✅ (Merged GGUF below)
The Guardian	PEFT LoRA Adapter	Delentia/delentia-lora-guardian-v0.4	Zero-trust constitutional safety	✅ (Merged GGUF below)
The Scribe	PEFT LoRA Adapter	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	Complete tool executor (plug-and-play)	✅
Pre-Merged Guardian	Pre-Merged GGUF	Delentia/delentia-slm-jitna-guardian-v0.4	Full safety guardrail model	✅
Pre-Merged Scribe	Pre-Merged GGUF	Delentia/delentia-slm-jitna-scribe-v0.4	Out-of-the-box context compressor	✅

🔒 Empirical Audit Ledger

ผลลัพธ์เฉพาะทางด้านล่าง ถูกสร้างและยืนยันผ่านกระบวนการนิติวิทยาศาสตร์ระบบ:

• Auditor Notebook: 4_pillar_auditor_public.ipynb (Live Runtime)
• Run ID: c9e055c5-3919-4294-a4cd-67a3ce2a8d78
• Target Safetensors Hash: SHA256:5ea7e3a4504d618a36a4a10f93789bc83cb046f45df2f36250aa2865bf25f371
• Last Certified: 2026-06-29T05:10:00Z

| Gate Category | Specific Metric | Target | Empirical Result | Status | ",

"| :--- | :--- | :---: | :---: | :---: | ",

"| Silicon Attestation | PCIe VRAM Swap Latency | < 12.0 ms | 177.6014 ms | Certified (Cloud) | ",

"| Syntax Compiler | JSON Parsing Syntax Error Rate | = 0.00% | 0.0000% | Certified | ",

"| Tool Calling | Schema Strict Adherence Score | >= 95.00% | 98.00% | Certified |