Update README.md

README.md

@@ -13,4 +13,215 @@ tags:
 - transformers
 - safety
 - reasoning
----
+---
+# Vayne-V3
+
+**Vayne-V3** is a **fully fine-tuned, MXFP4-quantized enterprise LLM** built for **AI agent frameworks**, **MCP-based tool orchestration**, **Retrieval-Augmented Generation (RAG) pipelines**, and **secure on-premise deployment**.
+
+Building on the foundation of Vayne-V2, Vayne-V3 delivers deeper model adaptation through **full-parameter Supervised Fine-Tuning (SFT)** combined with **NVIDIA ModelOpt Quantization-Aware Training (QAT)**, resulting in significantly improved instruction-following, identity consistency, and inference efficiency.
+
+- **Full-parameter fine-tuning** for deeper knowledge integration (vs. LoRA in V2)
+- **MXFP4 quantization** via NVIDIA ModelOpt for fast, memory-efficient inference
+- **Enhanced multilingual reasoning** with Korean Chain-of-Thought capabilities
+- Seamless integration with MCP-based multi-tool orchestration
+- Secure deployment in private or regulated environments
+
+---
+
+## What's New in V3
+
+| Feature | V2 | V3 |
+|---------|----|----|
+| Fine-Tuning Method | LoRA (Adapter) | **Full-Parameter SFT** |
+| Quantization | BF16 / FP16 | **MXFP4 (QAT)** |
+| Identity Alignment | Basic | **Enhanced (5x oversampled identity training)** |
+| Multilingual Reasoning | Bilingual QA | **Korean Chain-of-Thought Thinking** |
+| Training Pipeline | Single-step | **3-Step QAT Recipe** |
+
+---
+
+## Key Design Principles
+
+| Feature | Description |
+|---------|-------------|
+| Private AI Ready | Deploy fully **on-premise** or in **air-gapped** secure environments |
+| Efficient Inference | **MXFP4 quantization** enables fast inference on a single GPU |
+| 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 | [openai/gpt-oss-safeguard-20b](https://huggingface.co/openai/gpt-oss-safeguard-20b) |
+| Parameters | 21B (Active: 3.6B) |
+| Training Precision | BF16 |
+| Inference Precision | **MXFP4** (Quantization-Aware Training) |
+| Architecture | Decoder-only Transformer (MoE) |
+| Safety Architecture | Chain-of-Thought Reasoning |
+| Context Length | 4K tokens |
+| Inference | Single-GPU (16GB VRAM) / Multi-GPU |
+
+### Training Pipeline — 3-Step QAT Recipe
+
+Vayne-V3 is trained using a **3-step Quantization-Aware Training (QAT) recipe** powered by NVIDIA ModelOpt:
+
+```
+Step 1: Full-Parameter SFT
+   └─ Standard supervised fine-tuning on BF16 weights (no quantization)
+
+Step 2: Quantization-Aware Training (QAT)
+   └─ Fine-tune with MXFP4_MLP_WEIGHT_ONLY quantization config
+   └─ Lower learning rate (1e-5) for stable convergence
+
+Step 3: MXFP4 Conversion
+   └─ Convert trained model to MXFP4 format via nvidia_convert.py
+   └─ Optimized for production inference
+```
+
+### Training Data
+
+Fine-tuned using full-parameter supervised instruction tuning (SFT) on proprietary and curated datasets covering:
+
+- Model identity and persona alignment
+- Domain-specific knowledge for targeted enterprise verticals
+- Multilingual Chain-of-Thought reasoning (Korean-English)
+
+### Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Learning Rate (SFT) | 2.0e-5 |
+| Learning Rate (QAT) | 1.0e-5 |
+| Batch Size | 2 per device |
+| Epochs | 1.0 |
+| Max Sequence Length | 4,096 |
+| Warmup Ratio | 0.03 |
+| LR Scheduler | Cosine with Min LR (10%) |
+| Gradient Checkpointing | Enabled |
+| Training Infrastructure | NVIDIA H200 x 8 |
+
+---
+
+## Safety & Reasoning Features
+
+Vayne-V3 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 |
+
+---
+
+## Secure On-Premise Deployment
+
+Vayne-V3 is built for **enterprise AI inside your firewall**.
+
+- No external API dependency
+- Compatible with **offline environments**
+- MXFP4 quantization for **resource-efficient deployment**
+- Proven for secure, regulated environments
+
+---
+
+## MCP (Model Context Protocol) Integration
+
+Vayne-V3 supports **MCP-based agent tooling**, making it easy to build tool-use AI agents.
+
+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
+
+```bash
+pip install transformers accelerate
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "PoSTMEDIA/Vayne-V3"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    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_new_tokens=1024)
+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 with MCP tool orchestration
+- Secure RAG search systems
+- Multilingual (Korean-English) reasoning tasks
+
+---
+
+## 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{vayne2026,
+  title={Vayne-V3: Fully Fine-Tuned Enterprise LLM with MXFP4 Quantization-Aware Training},
+  author={PoSTMEDIA AI Lab},
+  year={2026},
+  publisher={Hugging Face}
+}
+```
+
+---
+
+## Contact
+
+**PoSTMEDIA AI Lab**
+- Email: [dev.postmedia@gmail.com](mailto:dev.postmedia@gmail.com)
+- Web: [https://postmedia.ai](https://postmedia.ai)
+- Web: [https://postmedia.co.kr](https://postmedia.co.kr)