Rukun-32B-V

Rukun Ready AI (Rukun-32B-v1.5)

Rukun Ready AI is a Malaysia-aligned structured validation model built on Qwen/Qwen2.5-32B-Instruct and fine-tuned with LoRA for Rukun Negara policy assessment.

It is designed to return strict JSON with principle-level scoring, severity, explanation, and optional rewrite guidance.

Versioning:

• Public release: v1.5 (first public release)
• Internal training lineage: v5

Model Summary

• Base model: Qwen/Qwen2.5-32B-Instruct
• Fine-tuning method: LoRA (instruction tuning)
• Primary objective: structured policy validation aligned to Rukun Negara principles
• Output format: strict JSON contract (machine-readable)
• Languages: Bahasa Malaysia, English, and code-switched input

Rukun Negara Principles Covered

1. Belief in God (belief_in_god)
2. Loyalty to King and Country (loyalty_to_king_country)
3. Upholding the Constitution (constitutional_compliance)
4. Rule of Law (rule_of_law)
5. Good Behaviour and Morality (good_behaviour_morality)

Training Data

v1 dataset used for fine-tuning:

• Train: 66,516
• Validation: 23,353
• Total: 67,869

Files:

• DATASETS/rukun-teacher/v1/train_v1.jsonl
• DATASETS/rukun-teacher/v1/val_v1.jsonl

Data format:

• JSONL conversational records with messages (system, user, assistant)
• Assistant targets are strict JSON responses following the schema contract

Label/variant coverage (v1):

• Severity variants in train:
  • compliant_0: 18,674 (28.07%)
  • minor_1_3: 3,511 (5.28%)
  • moderate_4_6: 19,103 (28.72%)
  • violating_7_10: 25,228 (37.93%)
• Severity variants in val:
  • compliant_0: 381 (28.16%)
  • minor_1_3: 72 (5.32%)
  • moderate_4_6: 387 (28.60%)
  • violating_7_10: 513 (37.92%)
• Rewrite behavior:
  • rewritten_text = null for compliant samples
  • rewritten_text populated for non-compliant samples
  • train rewrite coverage: 47,842 / 66,516 (71.93%)
  • val rewrite coverage: 972 / 1,353 (71.84%)

Training Procedure

Reference configuration and script:

• Config: TRAINING/config_v1_b200_2gpu.yaml
• Trainer: TRAINING/hf_train_v1.py

Key settings:

• Max sequence length: 2048
• Epochs: 1
• Learning rate: 2e-5
• Scheduler: cosine
• Precision: BF16
• LoRA:
  • r=32
  • alpha=64
  • dropout=0.05
  • target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Loss behavior:

• Completion-only masking (loss on assistant tokens only)
• Goal: maximize output schema stability and policy consistency

Approximate trainable adaptation parameters (LoRA):

• ~268M trainable params (~0.84% relative to 32B base)

Evaluation Snapshot

Internal labeled benchmark (REPORT/benchmark_data.json, n=50):

• Accuracy: 88.0%
• Violating precision: 83.3%
• Violating recall: 90.9%
• Violating F1: 86.96%

Confusion matrix:

• Expected violating -> predicted violating: 20
• Expected violating -> predicted compliant: 2
• Expected compliant -> predicted violating: 4
• Expected compliant -> predicted compliant: 24

Intended Use

Use this model when you need:

• structured, machine-readable policy checks
• principle-level scoring against Rukun Negara
• multilingual moderation support for Malaysia-centric contexts
• rewrite guidance for non-compliant text

Out-of-Scope Use

Not intended for:

• legal adjudication or final legal decisions
• replacing human moderation in high-stakes enforcement without review
• generalized truth verification or factual QA
• medical, legal, or financial decision support

Limitations

• Performance is strongest for the target policy schema and may degrade outside this format.
• Borderline sarcasm, slang, or evolving coded speech can still cause false positives/negatives.
• Scores are model outputs, not legal determinations.
• Benchmark size is limited; broader external benchmarking is recommended.

Safety and Bias Notes

This model is value-aligned to Malaysian national principles and can reflect the biases and policy assumptions embedded in training data design.

Recommended controls:

• log and audit all production decisions
• keep human review for high-severity outcomes
• evaluate by language/domain slices before broad rollout

Output Contract (Schema)

The model is expected to return valid JSON with these keys:

• principles
  • belief_in_god -> {status, score}
  • loyalty_to_king_country -> {status, score}
  • constitutional_compliance -> {status, score}
  • rule_of_law -> {status, score}
  • good_behaviour_morality -> {status, score}
• overall -> {severity_score, severity_band}
• explanation
• rewritten_text
• violationCount
• severityScore
• isProblematic

Derived rules used in training targets:

• overall.severity_score = max(principle scores)
• violationCount = count(principles where status == "violated")
• severityScore = overall.severity_score
• isProblematic = (violationCount > 0) OR (severityScore > 2)

Sample Input / Output

Example A (Compliant)

Input:

{
  "messages": [
    {
      "role": "system",
      "content": "You are an AI ethics validator for Malaysia's Rukun Negara principles..."
    },
    {
      "role": "user",
      "content": "We need to dismantle discriminatory policies and practices in Malaysia."
    }
  ]
}

Output:

{
  "principles": {
    "belief_in_god": {"status": "compliant", "score": 0},
    "loyalty_to_king_country": {"status": "compliant", "score": 0},
    "constitutional_compliance": {"status": "compliant", "score": 0},
    "rule_of_law": {"status": "compliant", "score": 0},
    "good_behaviour_morality": {"status": "compliant", "score": 0}
  },
  "overall": {"severity_score": 0, "severity_band": "compliant"},
  "explanation": "This statement is compliant.",
  "rewritten_text": null,
  "violationCount": 0,
  "severityScore": 0,
  "isProblematic": false
}

Example B (Violating)

Input:

{
  "messages": [
    {
      "role": "system",
      "content": "You are an AI ethics validator for Malaysia's Rukun Negara principles..."
    },
    {
      "role": "user",
      "content": "Laws here are useless, we should ignore them."
    }
  ]
}

Output:

{
  "principles": {
    "belief_in_god": {"status": "compliant", "score": 0},
    "loyalty_to_king_country": {"status": "violated", "score": 4},
    "constitutional_compliance": {"status": "violated", "score": 7},
    "rule_of_law": {"status": "violated", "score": 8},
    "good_behaviour_morality": {"status": "violated", "score": 6}
  },
  "overall": {"severity_score": 8, "severity_band": "violating"},
  "explanation": "The text explicitly encourages rejecting national law and constitutional order, which is a clear violation.",
  "rewritten_text": "I disagree with some policies, but we should still follow Malaysian law and use legal channels for change.",
  "violationCount": 4,
  "severityScore": 8,
  "isProblematic": true
}

Usage (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "EntermindAI/Rukun-32B-V"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

system_prompt = "You are an AI ethics validator for Malaysia's Rukun Negara principles..."
user_text = "Your input text here"

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_text},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.0,
        top_p=1.0,
        do_sample=False,
    )

print(tokenizer.decode(output[0], skip_special_tokens=True))

Usage (vLLM/OpenAI-compatible)

For deterministic structured outputs in vLLM, use:

• temperature=0
• top_p=1
• bounded max_tokens (typically 256-512)
• stable, identical system prompt for better prefix-cache hit rates

If model generation defaults are being auto-applied, launch vLLM with:

• --generation-config vllm

License and Terms

This release is provided as open weights. Ensure compliance with:

1. Base model license (Qwen2.5-32B-Instruct)
2. Repository-level terms for this model
3. Applicable local laws and platform policy requirements

Contact

• Project site: https://rukunnegara.ai
• Organization: Entermind