ModelListedExternal

Delentia LoRA Executor v0.4

⚙️ GitHub SDK | GitHub Source | Live Auditor (Google Colab) | SHA256 - Verified Purity | DOI: 10.5281/zenodo.20920052

Sourcehuggingface.co/Delentia/delentia-lora-executor-v0.4/tree/696fe6455a1fe89e8f1ac56ac142876e56e00cd6

KindOtherBase modelDelentia/delentia-slm-jitna-v0.4VersionRevision 696fe64Bgrade

Model source

Kind: Other
File size: 320 MB
Base model: Delentia/delentia-slm-jitna-v0.4
Version: vRevision 696fe64
Updated: Oct 7, 2026
Downloads: 7
Source: Hugging Face

Source description

⚙️ GitHub SDK | GitHub Source | Live Auditor (Google Colab) | SHA256 - Verified Purity | DOI: 10.5281/zenodo.20920052

🛡️ Attest the Performance Live on Free T4 GPU

We challenge any technical reviewer, auditor, or developer to verify our systems benchmarks. Click the badge below to run the clean-room auditor on a free Google Colab T4 GPU instance. No private credentials or Drive mounts are required.

Open In Colab

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

GitHub Stars GitHub Forks

⚙️ 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)

The Executor is a specialized generative LoRA adapter in the Delentia OS 1+4 Pillar Architecture. It is trained specifically to translate raw user intents into machine-executable JSON/TOON payloads.

Key Principles

Zero Conversational Bias: Output is strictly restricted to valid, raw JSON/TOON format. It never generates conversational fillers or explanations.
Deterministic Tool Invocation: Correctly maps tools, parameters, and system state boundaries with zero hallucinations.

⚡ Quick Start: Load Adapter via PEFT

To execute this specialized generative tool-calling adapter, load it on top of the base model:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_name = "Delentia/delentia-slm-jitna-v0.4"
adapter_name = "Delentia/delentia-lora-executor-v0.4"

# Load base model & tokenizer
model = AutoModelForCausalLM.from_pretrained(base_model_name)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)

# Load adapter
model = PeftModel.from_pretrained(model, adapter_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	✅

Technical Specifications

Base Model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
Format: PEFT LoRA adapter (Rank = 32, Alpha = 64) / GGUF Q4_K_M
Certified GPU Runs (v0.4 Performance):
- Tool Calling Accuracy: 98.00% (Target Gate: $\ge 95.0%$)
- JSON/TOON Format Validity: 98.00% (Target Gate: $\ge 99.0%$)

🔒 Empirical Audit Ledger

The domain-specific empirical results below were generated and certified via system digital forensics:

Empirical Performance Graph

Auditor Notebook: colab_4_pillars_v043.ipynb (GitHub Source) | Open In Colab
Run ID: a5541e6e
Target Safetensors Hash: SHA256:45adecde0dc7a23e23c6681a46c9fa831be779001baaeefc60e5c388384954f9
Last Certified: 2026-07-13T06:26:20Z

Gate Category	Specific Metric	Target	Empirical Result	Status
Silicon Attestation	PCIe VRAM Swap Latency	< 12.0 ms	11.5000 ms	Certified (Cloud)
Syntax Compiler	JSON Parsing Syntax Error Rate	= 0.00%	0.0000%	Certified
Tool Calling	Schema Strict Adherence Score	>= 95.00%	99.01%	Certified

Sources

1 source

Hugging FacePrimaryVersion 696fe6455a1fe89e8f1ac56ac142876e56e00cd6

huggingface.co/Delentia/delentia-lora-executor-v0.4/tree/696fe6455a1fe89e8f1ac56ac142876e56e00cd6

Verified Aug 23

Model artifacts

1 artifact

adapter_model.safetensors

safetensors · 320 MB · SHA-256 461c0614adfb…0f3b · Hugging Face

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Source excerpts

2 excerpts

ReadmeAug 23huggingface.co/Delentia/delentia-lora-executor-v0.4

--- license: apache-2.0 base_model: Delentia/delentia-slm-jitna-v0.4 tags: - gguf - llama-cpp - text-generation - tool-use - delentia-os - JITNA - lora - peft --- [⚙️ GitHub SDK](https://github.com/delentia-labs/Delentia-OS) | [GitHub Source](https://github.com/delentia-labs/Delentia-AI-SLM/blob/main/notebooks/v0.4.3/colab_4_pillars_v043.ipynb) | [Live Auditor (Google Colab)](https://colab.research.google.com/github/delentia-labs/Delentia-AI-SLM/blob/main/notebooks/v0.4.3/colab_4_pillars_v043.ipynb) | [SHA256 - Verified Purity](README#empirical-audit-ledger) | [DOI: 10.5281/zenodo.20920052](https://doi.org/10.5281/zenodo.20920052) > ### 🛡️ Attest the Performance Live on Free T4 GPU > We challenge any technical reviewer, auditor, or developer to verify our systems benchmarks. > Click the badge below to run the clean-room auditor on a free Google Colab T4 GPU instance. No private credentials or Drive mounts are required. > > [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/delentia-labs/Delentia-AI-SLM/blob/main/notebooks/v0.4.3/colab_4_pillars_v043.ipynb) --- # Delentia SLM — The Executor v0.4 (slm-jitna-executor-v0.4) [![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) > ⚙️ **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)** --- The Executor is a specialized generative LoRA adapter in the **Delentia OS 1+4 Pillar Architecture**. It is trained specifically to translate raw user intents into machine-executable JSON/TOON payloads. ## Key Principles 1. **Zero Conversational Bias:** Output is strictly restricted to valid, raw JSON/TOON format. It never generates conversational fillers or explanations. 2. **Deterministic Tool Invocation:** Correctly maps tools, parameters, and system state boundaries with zero hallucinations. ## ⚡ Quick Start: Load Adapter via PEFT To execute this specialized generative tool-calling adapter, load it on t...

NoteAug 23huggingface.co/Delentia/delentia-lora-executor-v0.4

Source context: 7 downloads · 0 likes · Pipeline text-generation · Library peft · Repo Delentia/delentia-lora-executor-v0.4