---
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
tags:
- llama-cpp
license: mit
datasets:
- SKT-NRS/SKT-OMNI-CORPUS-146T-V1
language:
- en
- hi
pipeline_tag: text-generation
library_name: transformers
---
# 🚀 SKT-OM (TIGER-OM) - Agentic RAG System

**Advanced 13B Agentic RAG with Think Mode + Dynamic Plugins + LangGraph**

Built for **AMD Developer Hackathon 2026** on AMD Developer Cloud.

---

## 🌟 Project Overview

**SKT-OM** (also known as **TIGER-OM**) is a powerful **13B parameter fully agentic Retrieval-Augmented Generation (RAG)** system. It goes far beyond traditional RAG by integrating:

- **Think Mode** — Advanced multi-step reasoning engine
- **Dynamic Plugin Architecture** — Intelligent tool selection & execution
- **LangGraph Multi-Agent Workflow** — Stateful agent collaboration
- **SKT RAG** — High-performance retrieval pipeline

The system takes natural language queries and returns intelligent, reasoned, and accurate responses with tool usage and verification.

---

## 📊 Model Details

- **Model Name**: TIGER-OM (SKT-OM)
- **Parameters**: 13 Billion
- **Base Model**: Custom trained on AMD hardware
- **Quantization**: **Q4_K_M** (Excellent balance between quality and size)
- **GGUF Format**: Optimized for CPU + GPU inference
- **Training Hardware**: AMD Developer Cloud GPUs ($100 credits)
- **Inference**: ROCm 7.0 + vLLM (Full FP16) + GGUF (Q4_K_M)

**Q4_K_M Version** provides near FP16 level reasoning quality while being much more memory efficient and faster on consumer/pro hardware.

---

## ✨ Key Features

- **Think Mode Engine**: Chain-of-Thought, Self-Reflection, Verification Loops, and Self-Critique
- **Plugin Ecosystem**: Code Runner, Math Solver, Web Search, Data Analyzer, Document Parser + Custom Plugins
- **Advanced RAG**: SKT RAG with query rewriting, multi-hop retrieval, reranking & contextual compression
- **Multi-Agent System**: LangGraph powered stateful workflow
- **Memory**: Persistent conversation state
- **Tool Use**: Dynamic plugin routing based on query intent

---

## 🔗 Important Links

- **Live Demo**: [https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/SKT-OM](https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/SKT-OM)
- **Main Model Repo**: [Shrijanagain/TIGER-OM](https://huggingface.co/Shrijanagain/TIGER-OM)
- **GGUF Quantized Models (Q4_K_M)**: [Shrijanagain/TIGER-GGUF](https://huggingface.co/Shrijanagain/TIGER-GGUF)
- **GitHub Repository (RAG + ADK)**: [https://github.com/SHRIJANAGAIN/SKT-AMD-FILES](https://github.com/SHRIJANAGAIN/SKT-AMD-FILES)

---

## How It Works

```mermaid
graph TD
    A[User Query] --> B[Think Mode]
    B --> C[Decomposition & Planning]
    C --> D[Plugin Router]
    C --> E[SKT RAG Retrieval]
    D --> F[Execute Plugins]
    E --> G[Context Processing]
    F & G --> H[Verification Loop]
    H --> I[LangGraph Synthesis]
    I --> J[Final Response]
```

---

## 🛠️ Technologies Used

- **LLM**: 13B TIGER-OM (Q4_K_M GGUF)
- **RAG Framework**: SKT RAG + ADK Kit
- **Agent Framework**: LangGraph
- **GPU Stack**: ROCm 7.0 + AMD ADK Kit
- **Inference**: vLLM (FP16) + llama.cpp (GGUF Q4_K_M)
- **Hardware**: AMD MI300X
- **Cloud**: AMD Developer Cloud

---

## 🚀 Quick Start - GGUF Q4_K_M

```bash
# Using llama.cpp
./llama-cli \
  -m tiger-om-q4_k_m.gguf \
  -p "Your complex query here..." \
  -n 1024 \
  -t 8 \
  --temp 0.7
```

**Python Example (llama-cpp-python)**

```python
from llama_cpp import Llama

llm = Llama(
    model_path="tiger-om-q4_k_m.gguf",
    n_gpu_layers=-1,      # Use all GPU layers
    n_ctx=8192,
    verbose=False
)

response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Explain..."}],
    temperature=0.7,
    max_tokens=1024
)

print(response['choices'][0]['message']['content'])
```

---

## 📁 Repository Structure

- `/skt_ai_labs` — Core ADK + RAG integration
- `/plugins` — Plugin system
- `/agents` — LangGraph workflows
- `/examples` — Ready-to-use examples
- `/docs` — Architecture & guides

---

## 🏆 Hackathon Information

- **Event**: AMD Developer Hackathon 2026
- **Trained on**: AMD Developer Cloud ($100 credits)
- **Built in Public**: Regular technical updates shared
- **Goal**: Showcasing powerful agentic AI on AMD ROCm ecosystem

---

## 📄 License

*MIT*