Vayne-V1 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

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.)

Model Architecture & Training

Specification	Details
🧬 Base Model	GPT-OSS-20B
🔢 Parameters	~20B
🎯 Precision	FP16 / BF16
🧱 Architecture	Decoder-only Transformer
📏 Context Length	4K tokens
⚡ Inference	Single / Multi-GPU compatible

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
Synthetic instructions with safety curation

Secure On-Premise Deployment

Vayne-V1 is built for enterprise AI inside your firewall.

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

MCP (Model Context Protocol) Integration

Vayne-V1 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

RAG Compatibility

Designed for hybrid reasoning + retrieval.

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

Quick Start

pip install transformers peft accelerate bitsandbytes

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "PoSTMEDIA/Vayne-V1"
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))

Use Cases

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

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

Citation

bibtex

@misc{vayne2025,
  title={Vayne-V1: Private On-Premise LLM Optimized for Agents and RAG},
  author={PoSTMEDIA AI Lab},
  year={2025},
  publisher={Hugging Face}
}

Contact

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