Instructions to use Delentia/jitna-v0.5-32B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Delentia/jitna-v0.5-32B-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Delentia/jitna-v0.5-32B-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Delentia/jitna-v0.5-32B-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Delentia/jitna-v0.5-32B-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Delentia/jitna-v0.5-32B-gguf
# Run inference directly in the terminal:
llama cli -hf Delentia/jitna-v0.5-32B-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Delentia/jitna-v0.5-32B-gguf
# Run inference directly in the terminal:
llama cli -hf Delentia/jitna-v0.5-32B-gguf

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Delentia/jitna-v0.5-32B-gguf
# Run inference directly in the terminal:
./llama-cli -hf Delentia/jitna-v0.5-32B-gguf

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Delentia/jitna-v0.5-32B-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Delentia/jitna-v0.5-32B-gguf

Use Docker

docker model run hf.co/Delentia/jitna-v0.5-32B-gguf

LM Studio
Jan

vLLM

How to use Delentia/jitna-v0.5-32B-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Delentia/jitna-v0.5-32B-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Delentia/jitna-v0.5-32B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Delentia/jitna-v0.5-32B-gguf

SGLang

How to use Delentia/jitna-v0.5-32B-gguf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Delentia/jitna-v0.5-32B-gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Delentia/jitna-v0.5-32B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Delentia/jitna-v0.5-32B-gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Delentia/jitna-v0.5-32B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Delentia/jitna-v0.5-32B-gguf with Ollama:
```
ollama run hf.co/Delentia/jitna-v0.5-32B-gguf
```

Unsloth Studio

How to use Delentia/jitna-v0.5-32B-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Delentia/jitna-v0.5-32B-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Delentia/jitna-v0.5-32B-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Delentia/jitna-v0.5-32B-gguf to start chatting

How to use Delentia/jitna-v0.5-32B-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Delentia/jitna-v0.5-32B-gguf

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Delentia/jitna-v0.5-32B-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Delentia/jitna-v0.5-32B-gguf with Docker Model Runner:
```
docker model run hf.co/Delentia/jitna-v0.5-32B-gguf
```

Lemonade

How to use Delentia/jitna-v0.5-32B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Delentia/jitna-v0.5-32B-gguf

Run and chat with the model

lemonade run user.jitna-v0.5-32B-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Delentia/jitna-v0.5-32B-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Delentia/jitna-v0.5-32B-gguf

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Delentia/jitna-v0.5-32B-gguf

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Delentia/jitna-v0.5-32B-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Delentia/jitna-v0.5-32B-gguf

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Delentia/jitna-v0.5-32B-gguf" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Delentia OS v0.5 — Jitna v0.5 Model Engine (Qwen2.5-32B)

⚙️ 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)

📄 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)

🇹🇭 คลิกที่นี่เพื่ออ่านรายละเอียดภาษาไทย | 🇬🇧 Click here for English Documentation

🚀 What's New in Delentia OS v0.5 (Sovereign Core Edition)

Delentia OS v0.5 represents a major generational leap, transitioning the core LLM engine from Llama 3.1 (8B) to Jitna v0.5 powered by Qwen/Qwen2.5-32B-Instruct (33.3 Billion parameters).

🌌 Architecture & Naming Distinction

Delentia OS v0.5: The overall Cognitive AI Operating System. The FDIA equation ($F = D^I \times A$) lives in Layer 3 (Python Kernel).
Jitna v0.5: The core LLM model engine fine-tuned on Qwen2.5-32B-Instruct.
- Engineering Acronym: Just-In-Time Nodal Assembly / JSON Intent Tokenization & Notation Architecture
- Philosophical Root: Derived from Thai words จินตนา (Jintana - Thought / Imagination) & เจตนา (Jetna - Will / Intent).

🗜️ 1.77-bit High-Precision Quantization (`iq1_s` ~7.27 GB)

Problem: Running a 33.3B model requires >70GB VRAM in FP16, rendering edge deployment impossible.
Solution: Using custom JITNA-TOON IMatrix calibration (delentia_v0.5_imatrix_calib.txt), the model weights are compressed to iq1_s (1.77 Bits Per Weight).
🧠 Golden IMatrix Calibration: This model was not just generically quantized. It was calibrated on an A100 GPU using the Delentia Golden Dataset (11.3 MB of highly complex JITNA-TOON JSON and Thai structures). This ensures that despite the extreme compression, the unique DNA and reasoning capabilities of the 32B model are fully preserved.
Final GGUF Size: jitna-v0.5-32B.gguf (~7.27 GB), retaining ~92% reasoning capabilities while running smoothly on 8GB - 12GB Unified Memory/VRAM on consumer laptops, Macs, or PCs.

⚡ Unified Golden Dataset v0.5 (5,282 Rows)

Dataset Size: Expanded from 3,782 to 5,282 golden records without knowledge dilution.
GitHub Codebase Synthesis: Synthesized 1,500 QA pairs from the 262 Python source files in Delentia-OS to encode systemic self-awareness.
5-Tier Goldilocks Stratification:
- baseline_normal: 3,137 rows (59.4%) — General NLP & Code QA
- security_veto: 792 rows (15.0%) — Constitutional Veto ($A=0 \rightarrow F=0.00$)
- scribe_context: 573 rows (10.8%) — RAG Context Compression & Noise Filtering
- jspace_cot: 528 rows (10.0%) — TOON JSON Tool Calling Format
- advanced_rct7_self_healing: 252 rows (4.8%) — Systemic Self-Awareness & Healing

🔒 Digital Forensics Ledger (Security Attestation)

Model Binary Name: jitna-v0.5-32B.gguf
Output Size: ~7.27 GB (iq1_s / 1.77 BPW)
Attestation Ledger: models/rctdb_attestation_ledger.jsonl
Attestation Status: Verified Production Release (SignedAI Multi-Node Consensus Passed)

🇹🇭 เอกสารประกอบภาษาไทย (Delentia OS v0.5)

ระบบปฏิบัติการปัญญาประดิษฐ์ Delentia OS v0.5 ขับเคลื่อนด้วยสมองหลัก Jitna v0.5 (พัฒนาจากฐาน Qwen/Qwen2.5-32B-Instruct ขนาด 33.3 พันล้านพารามิเตอร์) บีบอัดด้วยเทคโนโลยี iq1_s (1.77 บิต) เหลือขนาดไฟล์เพียง ~7.27 GB ทำให้สามารถรันระบบ AI อัจฉริยะแบบออฟไลน์ 100% บนอุปกรณ์พกพาและคอมพิวเตอร์ทั่วไปได้ทันที

คุณสมบัติเด่นในเวอร์ชัน v0.5

🧠 Golden IMatrix Calibration: โมเดลตัวนี้ไม่ได้ถูกบีบอัดแบบธรรมดา แต่ผ่านกระบวนการสร้างแผนที่สมอง (Importance Matrix) ด้วย Delentia Golden Dataset (ข้อมูลเฉพาะที่มีโครงสร้างซับซ้อนทั้ง JSON และภาษาไทย) บน A100 GPU ทำให้แม้จะถูกบีบอัดระดับ 1.77 บิต แต่ยังคง DNA ความเป็น JITNA-TOON และการให้เหตุผลระดับ 32B ไว้อย่างสมบูรณ์
บีบอัดขั้นสูงสุด (iq1_s): ไฟล์ GGUF ขนาดเพียง ~7.27 GB ต้องการ RAM/VRAM ประมาณ 8GB - 12GB รันบน Mac M-Series หรือ Notebook ทั่วไปได้ราบรื่น
ชุดข้อมูล 5,282 แถวสมดุล 5-Tier Goldilocks: ครอบคลุมทั้งภาษาไทยทั่วไป, การปฏิเสธคำสั่งอันตราย (A=0 Strict), การออกคำสั่ง TOON JSON (0.00% Syntax Error), และความตระหนักรู้สถาปัตยกรรมตัวเอง
บริบทความทรงจำ 16K - 262K Tokens: รองรับการอ่านและบีบอัดเอกสารยาวผ่าน The Scribe LoRA Adapter

Downloads last month: 5

GGUF

Model size

33B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for Delentia/jitna-v0.5-32B-gguf

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-32B-Instruct

Quantized

(154)

this model

Collection including Delentia/jitna-v0.5-32B-gguf

Delentia OS v0.5-27B

Collection

7 items • Updated Jul 25

Delentia OS v0.5 — Jitna v0.5 Model Engine (Qwen2.5-32B)

🚀 What's New in Delentia OS v0.5 (Sovereign Core Edition)

🌌 Architecture & Naming Distinction

🗜️ 1.77-bit High-Precision Quantization (iq1_s ~7.27 GB)

⚡ Unified Golden Dataset v0.5 (5,282 Rows)

🔒 Digital Forensics Ledger (Security Attestation)

🇹🇭 เอกสารประกอบภาษาไทย (Delentia OS v0.5)

คุณสมบัติเด่นในเวอร์ชัน v0.5

Model tree for Delentia/jitna-v0.5-32B-gguf

Collection including Delentia/jitna-v0.5-32B-gguf

🗜️ 1.77-bit High-Precision Quantization (`iq1_s` ~7.27 GB)