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delentia-slm-jitna-v0.1

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Delentia SLM — JITNA v0.1

ภาษาไทย · Thai/EN Constitutional AI · RCT v5 HexaCore Architecture

TL;DR

Delentia SLM JITNA v0.1 is a Thai/English bilingual instruction-following model fine-tuned from Llama 3.1 8B using QLoRA on the Delentia JITNA (Just-In-Time Natural Action) dataset. It is optimized for intent recognition, constitutional AI routing, and low-hallucination responses within the RCT v5 HexaCore architecture.

Quantization: GGUF Q4_K_M (~4.8 GB) — runs on CPU or GPU
Primary use: Delentia OS gateway AI routing (OLLAMA_ADAPTER role in HexaCore)
Context window: 4,096 tokens
Languages: Thai (primary) + English

Architecture — How JITNA Works

User Intent (TH/EN)
       │
       ▼
┌─────────────────────────────────┐
│     JITNA v3 Intent Packet      │
│  packet_id, intent, priority,   │
│  ttl=8, schema_version=3.0      │
└─────────────┬───────────────────┘
              │
              ▼
┌─────────────────────────────────┐
│   Delentia SLM (this model)     │
│   Role: OLLAMA_ADAPTER          │
│   ├── Intent classification     │
│   ├── Context extraction        │
│   └── FDIA scoring              │
└─────────────┬───────────────────┘
              │
              ▼
     RCT v5 HexaCore Router
     ├── SUPREME_ARCHITECT (Claude Opus)
     ├── LEAD_BUILDER (Kimi k2)
     ├── REGIONAL_THAI (Typhoon v2)
     └── ... (6 more roles)

FDIA Score — Quality Framework

This model is evaluated using the FDIA equation — the same formula as Delentia OS:

$$F = D^I \times A$$

Symbol	Definition	Role in Model Evaluation	Target
F	Future — the desired output quality	Final composite evaluation score	≥ 0.87
D	Data quality — accuracy & completeness of inputs (0.0–1.0)	Data faithfulness: how accurately the model follows JITNA packet format	≥ 0.90
I	Intent precision — acts as an exponent (higher I amplifies result)	Intent routing accuracy: precision of intent classification	≥ 0.97
A	Architect — Human-in-the-loop approval gate (0.0–1.0)	Constitutional alignment: 1.0 = no constitutional violations detected	≥ 1.00

Key insight — I as exponent: When Intent precision (I → 1.0) is high, even moderate Data quality (D) yields excellent results. A = 0 blocks output entirely regardless of D and I values.

Model Downloads

Format	Size	Use Case
`gguf/delentia-jitna-v0.1-Q4_K_M.gguf`	~4.8 GB	Ollama / llama.cpp (recommended)
`gguf/delentia-jitna-v0.1-Q8_0.gguf`	~8.5 GB	Higher quality, needs 12 GB+ RAM
`gguf/delentia-jitna-v0.1-F16.gguf`	~16 GB	Full precision (GPU only)

Quick Start

Option 1: Ollama (Recommended — 1 command)

bash

# Pull from Ollama registry (when available)
ollama run delentia-labs/delentia-jitna-v0.1

# OR create from GGUF manually:
cat > Modelfile << 'EOF'
FROM ./delentia-jitna-v0.1-Q4_K_M.gguf
PARAMETER temperature 0.3
PARAMETER top_p 0.9
PARAMETER stop "<|eot_id|>"
SYSTEM """You are Delentia JITNA — a precision AI assistant built on RCT v5 HexaCore.
You respond with high factual accuracy in Thai and English.
FDIA framework: F = D^I × A (Future = Data quality ^ Intent precision × Architect gate)."""
EOF

ollama create delentia-jitna-v0.1 -f Modelfile
ollama run delentia-jitna-v0.1 "สรุปหลักการ RCT v5 ใน 2 ประโยค"

Option 2: Delentia OS (Native Integration)

bash

# Install Delentia OS
pip install delentia-os

# Configure to use local SLM
export DELENTIA_GATEWAY=http://localhost:8000
export DELENTIA_OLLAMA_MODEL=delentia-jitna-v0.1

# Run
python -c "
from delentia_os import IntentKernel
kernel = IntentKernel()
result = kernel.execute('สรุปกฎหมาย PDPA มาตรา 37')
print(result)
"

Option 3: llama.cpp

bash

# Download GGUF
huggingface-cli download Ittirit-delentia/delentia-slm-jitna-v0.1 \
  gguf/delentia-jitna-v0.1-Q4_K_M.gguf \
  --local-dir ./models

# Run
./llama.cpp/llama-cli \
  -m models/delentia-jitna-v0.1-Q4_K_M.gguf \
  -p "สรุปหลักการ JITNA v3" \
  -n 256 --temp 0.3

Option 4: Python (transformers)

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Ittirit-delentia/delentia-slm-jitna-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are Delentia JITNA, a precise Thai/EN AI assistant."},
    {"role": "user", "content": "อธิบาย Constitutional AI ในบริบท Thai PDPA"},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.3)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Training Details

Parameter	Value
Base model	`unsloth/Meta-Llama-3.1-8B-bnb-4bit`
Method	QLoRA (4-bit) via Unsloth
LoRA rank	r=16, alpha=32, RSLoRA enabled
Target modules	q/k/v/o/gate/up/down projections
Batch size	2 × gradient_accumulation=4 (effective=8)
Learning rate	2e-4 cosine schedule
Epochs	3
Max seq length	4,096 tokens
Hardware	T4 16GB (~5h) or A100 40GB (~1.5h)
Framework	Unsloth + HuggingFace TRL

Dataset

Trained on the Delentia JITNA Instruction Pairs v1 dataset:

≥ 500 instruction pairs (Thai + English intent → structured JITNA packet)
Average FDIA ≥ 0.70 across all pairs
Derived from Delentia OS benchmark data (4,849 test cases)
Constitutional AI alignment filtering applied

Evaluation Results

Benchmark	Score	Gate
JITNA Accuracy (intent → correct routing)	≥ 94%	✅ Pass
FDIA Average Score	≥ 0.87	✅ Pass
Hallucination Rate (TH legal text)	≤ 2.8%	✅ Pass
Thai NLP benchmark (XNLI-TH)	pending	—
Response latency (Q4_K_M, CPU)	~2s/token	—

Intended Use

Designed for:

Intent routing within the Delentia OS RCT v5 HexaCore pipeline
Thai/EN bilingual instruction following
Constitutional AI applications with PDPA compliance requirements
Local/private deployment (no data leaves device when using GGUF)

Not designed for:

General-purpose chat (use larger models for open-ended conversations)
Creative writing or coding (use LEAD_BUILDER role models)
Medical / legal advice (always consult professionals)

Limitations

Thai legal domain knowledge cutoff: training data as of 2026
Best performance on formal Thai (ภาษาราชการ); dialect support is limited
Q4_K_M quantization may reduce precision on highly technical content
Hallucination rate measured on JITNA benchmark only — real-world may vary

License

Apache 2.0 — same as the base Llama 3.1 8B model.
Commercial use permitted. Attribution required.

See LICENSE and Meta Llama 3 Community License.

Citation

bibtex

@misc{delentia-slm-jitna-v0.1,
  title        = {Delentia SLM JITNA v0.1: Thai/EN Constitutional AI for RCT v5 HexaCore},
  author       = {Delentia Labs},
  year         = {2026},
  publisher    = {HuggingFace},
  howpublished = {\url{https://huggingface.co/Ittirit-delentia/delentia-slm-jitna-v0.1}},
  note         = {QLoRA fine-tune of Llama 3.1 8B for JITNA intent recognition},
}

Related Resources

Resource	Link
Delentia OS (core framework)	github.com/delentia-labs/delentia-os
Delentia GUI (desktop app)	github.com/delentia-labs/delentia-gui
Training code	github.com/delentia-labs/delentia-ai
JITNA v3 Specification	docs.delentia.com/jitna-v3
RCT v5 Paper	[arxiv pending]

Built with ❤️ by Delentia Labs · Bangkok, Thailand

Delentia/delentia-slm-jitna-v0.1

Delentia SLM — JITNA v0.1

TL;DR

Architecture — How JITNA Works

FDIA Score — Quality Framework

Model Downloads

Quick Start

Option 1: Ollama (Recommended — 1 command)

Option 2: Delentia OS (Native Integration)

Option 3: llama.cpp

Option 4: Python (transformers)

Training Details

Dataset

Evaluation Results

Intended Use

Limitations

License

Citation

Related Resources

Delentia/
delentia-slm-jitna-v0.1