| --- |
| 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* |
