Vayne-V2

Vayne-V2 is a compact, efficient, and high-performance enterprise LLM optimized for AI agent frameworks, MCP-based tool orchestration, Retrieval-Augmented Generation (RAG) pipelines, and secure on-premise deployment.

• ✅ Lightweight architecture for fast inference and low resource usage
• ⚙️ Seamless integration with modern AI agent frameworks
• 🔗 Built-in compatibility for MCP-based multi-tool orchestration
• 🔍 Optimized for enterprise-grade RAG systems
• 🛡️ Secure deployment in private or regulated environments

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Key Design Principles

Feature	Description
🔐 Private AI Ready	Deploy fully on-premise or in air-gapped secure environments
⚡ Lightweight Inference	Single-GPU optimized architecture for fast and cost-efficient deployment
🧠 Enterprise Reasoning	Structured output and instruction-following for business automation
🔧 Agent & MCP Native	Built for AI agent frameworks and MCP-based tool orchestration
🔍 RAG Enhanced	Optimized for retrieval workflows with vector DBs (FAISS, Milvus, pgvector, etc.)

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Model Architecture & Training

Specification	Details
🧬 Base Model	GPT-OSS-Safeguard-20B
🔢 Parameters	21B (Active: 3.6B)
🎯 Precision	BF16 / FP16
🧱 Architecture	Decoder-only Transformer
🛡️ Safety Architecture	Chain-of-Thought Reasoning
📏 Context Length	4K tokens
⚡ Inference	Single-GPU (16GB VRAM) / Multi-GPU

Training Data

Fine-tuned using supervised instruction tuning (SFT) on:

• Enterprise QA datasets
• Task reasoning + tool usage instructions
• RAG-style retrieval prompts
• Business reports & structured communication
• Korean–English bilingual QA and translation
• Safety reasoning with Chain-of-Thought (CoT) supervision
• Policy-based content classification datasets

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Safety & Reasoning Features

Vayne-V2 inherits advanced safety reasoning capabilities from gpt-oss-safeguard-20b:

Feature	Description
🧠 Chain-of-Thought Safety	Transparent reasoning process for content safety decisions
📋 Bring Your Own Policy	Custom policy interpretation and application
⚖️ Configurable Reasoning	Adjustable reasoning effort (Low/Medium/High)
🔬 Explainable Outputs	Full CoT traces for safety decision auditing

Reasoning Effort Levels

Level	Use Case	Trade-off
Low	Fast filtering, real-time applications	Speed-optimized, lower latency
Medium	Balanced production use	Balanced accuracy and speed
High	Critical content review	Maximum accuracy, higher latency

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Secure On-Premise Deployment

Vayne-V2 is built for enterprise AI inside your firewall.

✅ No external API dependency
✅ Compatible with offline environments
✅ Proven for secure deployments

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MCP (Model Context Protocol) Integration

Vayne-V2 supports MCP-based agent tooling, making it easy to integrate tool-use AI.

Works seamlessly with:

• Claude MCP-compatible agent systems
• Local agent runtimes
• JSON structured execution

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RAG Compatibility

Designed for hybrid reasoning + retrieval.

✅ Works with FAISS, Chroma, Elasticsearch
✅ Handles long-context document QA
✅ Ideal for enterprise knowledge bases

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Quick Start

pip install transformers peft accelerate bitsandbytes

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "PoSTMEDIA/Vayne-V2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "Explain the benefits of private AI for enterprise security."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Use Cases

✅ Internal enterprise AI assistant
✅ Private AI document analysis
✅ Business writing (reports, proposals, strategy)
✅ AI automation agents
✅ Secure RAG search systems

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Safety & Limitations

• Not intended for medical, legal, or financial decision-making
• May occasionally generate hallucinations
• Use human validation for critical outputs
• Recommended: enable output guardrails for production

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Citation

@misc{vayne2025,
  title={Vayne-V2: Safety-Enhanced Enterprise LLM with Chain-of-Thought Reasoning},
  author={PoSTMEDIA AI Lab},
  year={2025},
  publisher={Hugging Face}
}

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Contact

PoSTMEDIA AI Lab
📧 dev.postmedia@gmail.com
🌐 https://postmedia.ai
🌐 https://postmedia.co.kr

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