---
license: mit
language:
- en
- hi
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
tags:
- agent
- Qwen
- AI
- ST-X-0
- MIXTRAL
- TIGER OM
library_name: transformers
inference:
  parameters:
    temperature: 0.7
    max_new_tokens: 500
widget:
  - text: "What are the latest trends in retrieval-augmented generation?"
    example_title: "General Query"
---

---
# 🚀 TIGER-OM (SKT-OM) - 13B MoE Agentic Model

**Advanced 13B Mixture-of-Experts (MoE) Model** optimized for Agentic RAG with Think Mode & Plugin Architecture.

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

---

## 📊 Model Details

- **Model Name**: TIGER-OM (SKT-OM)
- **Architecture**: **Mixture of Experts (MoE)**
- **Total Parameters**: 13B (Active parameters much lower due to MoE sparsity)
- **Base Models**: 
  - Primary Base: **Shrijanagain/ST-X-0**
  - Expert Integration: **Mistral-7B**
- **Format**: **Safetensors** (Safe & Fast loading)
- **Quantization**: FP16 / BF16 (Original) + Q4_K_M GGUF available in separate repo
- **Context Length**: 8192 tokens
- **Training Hardware**: AMD Developer Cloud GPUs ($100 developer credits)
- **Inference Optimized**: ROCm 7.0 + vLLM + AMD MI300X

---

## 🌟 Key Features

- **True MoE Architecture** — Sparse activation for better efficiency and performance
- **Think Mode Reasoning** — Advanced Chain-of-Thought, Planning, Self-Reflection & Verification
- **Dynamic Plugin System** — Intelligent routing to Code, Math, Search, Data Analysis plugins
- **Agentic Capabilities** — Full LangGraph multi-agent workflow
- **Advanced RAG Integration** — SKT RAG + Query Rewriting + Multi-hop + Reranking
- **Stateful Memory** — Persistent conversation context

---

## 🏗️ Architecture Breakdown

**TIGER-OM** is built on a **13B MoE** backbone:

- **Base**: Shrijanagain/ST-X-0 (strong foundational model)
- **Experts**: Fine-tuned using Mistral-7B as expert layers for specialized reasoning and tool-use capabilities
- **Router Network**: Learned gating mechanism for expert selection
- **Think Mode Layer**: Custom system prompt + reasoning controller
- **Plugin Head**: Tool calling & execution layer

This hybrid approach (ST-X-0 + Mistral-7B experts) gives excellent reasoning, code understanding, and general intelligence while maintaining MoE efficiency.

---

## 📁 Files in this Repo (Safetensors)

- `model-00001-of-0000X.safetensors` → Main model weights
- `config.json`
- `tokenizer.json` / `tokenizer_config.json`
- `generation_config.json`
- `special_tokens_map.json`
- `model.safetensors.index.json`

**All weights are in safe `safetensors` format** — No pickle risk.

---

## 🚀 How to Use (Safetensors)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "Shrijanagain/TIGER-OM"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

prompt = """You are SKT-OM, an advanced agentic AI with Think Mode enabled.
User Query: Calculate training cost comparison and suggest best option..."""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    repetition_penalty=1.1
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## 🔗 Important Links

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

---

## 🛠️ Technologies & Stack

- **Base Models**: Shrijanagain/ST-X-0 + Mistral-7B Experts
- **RAG**: SKT RAG + AMD ADK Kit
- **Agents**: LangGraph
- **Hardware**: AMD MI300X + ROCm 7.0
- **Inference**: vLLM (FP16) + transformers (Safetensors)
- **Training**: AMD Developer Cloud

---

## ⚡ Performance

- Excellent balance of **quality vs efficiency** due to MoE architecture
- Strong performance on reasoning, tool-use, code, and multi-step tasks
- Significantly lower inference cost compared to dense 13B+ models

---

## 📌 Use Cases

- Complex technical Q&A
- Agentic workflows & tool calling
- Research assistance
- Code generation & debugging
- Mathematical & logical reasoning
- Comparative analysis
- Data analysis with plugins

---

## 🏆 Hackathon

**AMD Developer Hackathon 2026**  
Trained entirely on **AMD Developer Cloud**  
Fully built in public with multiple technical updates.

---

## 📄 License

MIT License

---