| --- |
| 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 |
|
|
| --- |
