PoSTMEDIA
/

Vayne-V1

@@ -1,199 +1,188 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+base_model: openai/gpt-oss-20b
+base_model_relation: merge
 library_name: transformers
+pipeline_tag: text-generation
+tags:
+- sft
+- transformers
+- trl
+license: apache-2.0
+language:
+- en
+- ko
 ---
+# Vayne-V1
+**Vayne-V1** is a **high-performance enterprise LLM** engineered for **AI agent frameworks**, **MCP-based tool orchestration**, **RAG (Retrieval-Augmented Generation) pipelines**, and **secure on-premise deployment**.
+---
+## 1. Model Overview
+Vayne-V1 enables enterprises to build **internal AI assistants, automation agents, and retrieval-based knowledge systems** securely within their own infrastructure. It supports **local GPU environments**, making it ideal for **compliance-sensitive industries** such as finance, manufacturing, telecom, defense, and healthcare.
+### Key Design Principles
+- **Private AI Ready** – Deployable fully **on-premise** or **air-gapped**
+- **Single-GPU Inference** – Optimized lightweight architecture
+- **Enterprise Reasoning** – Structured responses for real business use cases
+- **Agent & MCP Optimized** – Plug-and-play AI agent compatibility
+- **RAG Enhanced** – Built to work with vector databases for knowledge retrieval
+---
+## 2. Key Capabilities
+| Capability | Description |
+|------------|-------------|
+| Local Deployment | Runs securely within enterprise infrastructure |
+| MCP Integration | Compatible with **Model Context Protocol** for tool-use AI |
+| Agent Ready | Works with LangChain, CrewAI, AutoGen, TaskWeaver |
+| RAG Optimized | Designed for document retrieval pipelines |
+| Structured Output | JSON, function-style responses for automation |
+| Bilingual | English + Korean hybrid enterprise workflow support |
+---
+## 3. Why Vayne-V1?
+Unlike cloud-restricted LLMs, Vayne-V1 enables **complete AI independence**:
+✅ Full data sovereignty – no data leaves your environment
+✅ Runs on NVIDIA 3090, 4090, A100, L40S, etc.
+✅ Ideal for secure enterprise AI stacks
+✅ Ready for internal AI copilots and automation agents
+✅ No vendor lock-in
+---
+## 4. Model Architecture & Training
+| Specification | Details |
+|---------------|---------|
+| Base Model | GPT-OSS-20B |
+| Parameters | ~20B |
+| Precision | FP16/BF16 |
+| Context Length | 4K tokens |
+| Type | Decoder-only Transformer |
 ### 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
+---
+## 5. Secure On-Premise Deployment
+Vayne-V1 is built for **enterprise AI inside your firewall**.
+✅ No external API dependency
+✅ Compatible with **offline environments**
+✅ Supports air-gapped GPU clusters
+✅ Proven for secure deployments
+---
+## 6. MCP (Model Context Protocol) Integration
+Vayne-V1 supports **MCP-based agent tooling**, making it easy to integrate tool-use AI.
+Example function-style output:
+```json
+{
+  "tool": "search_documents",
+  "arguments": { "query": "AI strategy in manufacturing industry" }
+}
+````
+Works seamlessly with:
+* Claude MCP-compatible agent systems
+* Local agent runtimes
+* JSON structured execution
+---
+## 7. RAG Compatibility
+Designed for **hybrid reasoning + retrieval**.
+✅ Works with FAISS, Chroma, Elasticsearch
+✅ Handles long-context document QA
+✅ Ideal for enterprise knowledge bases
+---
+## 8. Quick Start
+```bash
+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))
+```
+---
+## 9. Use Cases
+✅ Internal enterprise AI assistant
+✅ Private AI document analysis
+✅ Business writing (reports, proposals, strategy)
+✅ AI automation agents
+✅ Secure RAG search systems
+---
+## 10. 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](mailto:dev.postmedia@gmail.com)
+🌐 [https://postmedia.ai](https://postmedia.ai)
+🌐 [https://postmedia.co.kr](https://postmedia.co.kr)
+---