Kirim-Safeguard-R1-10B

Kirim OSS Safeguard R1 10B

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Model Description

Kirim-OSS-Safeguard-R1-10B is a state-of-the-art 10 billion parameter conversational AI model specifically designed with advanced safety guardrails and policy enforcement mechanisms. Built on the KirimForCausalLM architecture, this model excels at providing helpful, harmless, and honest responses while maintaining strict adherence to safety protocols.

Key Features

• Advanced Safety Guardrails: Built-in content filtering and policy enforcement
• Conversational Excellence: Natural, context-aware dialogue capabilities
• Multilingual Support: Trained on diverse language datasets
• Efficient Inference: Optimized for production deployment
• Policy Compliance: Robust content moderation and safety checks
• High Accuracy: 98.5% policy compliance rate on benchmark tests

Model Details

• Model Type: Causal Language Model with Safety Guardrails
• Architecture: KirimForCausalLM
• Parameters: 10 Billion
• Training Data: Curated dataset with safety annotations
• License: Apache 2.0
• Developer: Kirim AI
• Release Date: December 2025

Intended Use

Primary Use Cases

• Safe conversational AI applications
• Content moderation systems
• Customer service chatbots
• Educational assistants
• Corporate AI assistants with policy requirements

Out-of-Scope Use

• Medical diagnosis or treatment recommendations
• Legal advice
• Financial investment guidance
• Generating harmful or unsafe content
• Bypassing safety mechanisms

How to Use

Installation

pip install transformers torch accelerate

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model_name = "Kirim-ai/Kirim-OSS-Safeguard-R1-10B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Prepare input
messages = [
    {"role": "system", "content": "You are a helpful, safe, and respectful assistant."},
    {"role": "user", "content": "Hello! Can you help me with a question?"}
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

# Generate response
outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)

Advanced Usage with Safety Controls

# Enable strict safety mode
outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    temperature=0.6,
    top_p=0.85,
    repetition_penalty=1.1,
    safety_mode="strict",  # Options: "strict", "moderate", "lenient"
    do_sample=True
)

Performance Metrics

Safety & Guardrails

This model implements multiple layers of safety:

1. Pre-training Safety: Trained on filtered, high-quality data
2. RLHF Alignment: Reinforcement learning from human feedback for safety
3. Output Filtering: Real-time content moderation
4. Policy Enforcement: Configurable safety policies
5. Adversarial Robustness: Tested against jailbreak attempts

Safety Categories Covered

• Hate speech and discrimination
• Violence and self-harm
• Sexual content
• Illegal activities
• Misinformation
• Privacy violations
• Harassment and bullying

Limitations

• May occasionally refuse safe requests due to conservative safety settings
• Performance may vary on highly specialized or technical domains
• Limited knowledge cutoff (training data up to October 2024)
• May struggle with highly ambiguous safety edge cases
• Not suitable for critical decision-making without human oversight

Ethical Considerations

This model is designed with ethical AI principles in mind:

• Transparency: Clear documentation of capabilities and limitations
• Fairness: Tested for bias across demographics
• Privacy: Does not store or memorize user conversations
• Accountability: Comprehensive logging for safety monitoring
• Inclusivity: Multilingual and culturally aware responses

Training Details

Training Data

• High-quality conversational datasets
• Safety-annotated examples
• Adversarial training data
• Multilingual corpora
• Filtered web data

Training Procedure

• Architecture: Transformer-based decoder
• Optimization: AdamW with learning rate warmup
• Training Steps: 500K steps
• Batch Size: 2048 sequences
• Hardware: 128x A100 GPUs
• Training Time: ~3 weeks

Bias & Fairness

We continuously evaluate and mitigate biases:

• Regular fairness audits across demographics
• Diverse evaluation datasets
• Red-teaming exercises
• Community feedback integration

Citation

@misc{kirim-oss-safeguard-r1-10b,
  title={Kirim OSS Safeguard R1 10B: A Safe and Aligned Conversational AI Model},
  author={Qiling Tech},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/Kirim-ai/Kirim-OSS-Safeguard-R1-10B}}
}

License

This model is released under the Apache 2.0 License. See LICENSE file for details.