初始化项目，由ModelHub XC社区提供模型
Model: Shrijanagain/TIGER-OM Source: Original Platform
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README.md
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README.md
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---
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license: mit
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language:
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- en
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- hi
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base_model:
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- mistralai/Mistral-7B-Instruct-v0.3
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tags:
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- agent
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- Qwen
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- AI
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- ST-X-0
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- MIXTRAL
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- TIGER OM
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library_name: transformers
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inference:
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parameters:
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temperature: 0.7
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max_new_tokens: 500
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widget:
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- text: "What are the latest trends in retrieval-augmented generation?"
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example_title: "General Query"
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---
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---
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# 🚀 TIGER-OM (SKT-OM) - 13B MoE Agentic Model
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**Advanced 13B Mixture-of-Experts (MoE) Model** optimized for Agentic RAG with Think Mode & Plugin Architecture.
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Built for **AMD Developer Hackathon 2026** using AMD Developer Cloud.
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---
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## 📊 Model Details
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- **Model Name**: TIGER-OM (SKT-OM)
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- **Architecture**: **Mixture of Experts (MoE)**
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- **Total Parameters**: 13B (Active parameters much lower due to MoE sparsity)
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- **Base Models**:
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- Primary Base: **Shrijanagain/ST-X-0**
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- Expert Integration: **Mistral-7B**
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- **Format**: **Safetensors** (Safe & Fast loading)
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- **Quantization**: FP16 / BF16 (Original) + Q4_K_M GGUF available in separate repo
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- **Context Length**: 8192 tokens
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- **Training Hardware**: AMD Developer Cloud GPUs ($100 developer credits)
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- **Inference Optimized**: ROCm 7.0 + vLLM + AMD MI300X
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---
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## 🌟 Key Features
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- **True MoE Architecture** — Sparse activation for better efficiency and performance
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- **Think Mode Reasoning** — Advanced Chain-of-Thought, Planning, Self-Reflection & Verification
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- **Dynamic Plugin System** — Intelligent routing to Code, Math, Search, Data Analysis plugins
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- **Agentic Capabilities** — Full LangGraph multi-agent workflow
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- **Advanced RAG Integration** — SKT RAG + Query Rewriting + Multi-hop + Reranking
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- **Stateful Memory** — Persistent conversation context
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---
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## 🏗️ Architecture Breakdown
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**TIGER-OM** is built on a **13B MoE** backbone:
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- **Base**: Shrijanagain/ST-X-0 (strong foundational model)
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- **Experts**: Fine-tuned using Mistral-7B as expert layers for specialized reasoning and tool-use capabilities
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- **Router Network**: Learned gating mechanism for expert selection
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- **Think Mode Layer**: Custom system prompt + reasoning controller
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- **Plugin Head**: Tool calling & execution layer
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This hybrid approach (ST-X-0 + Mistral-7B experts) gives excellent reasoning, code understanding, and general intelligence while maintaining MoE efficiency.
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---
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## 📁 Files in this Repo (Safetensors)
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- `model-00001-of-0000X.safetensors` → Main model weights
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- `config.json`
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- `tokenizer.json` / `tokenizer_config.json`
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- `generation_config.json`
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- `special_tokens_map.json`
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- `model.safetensors.index.json`
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**All weights are in safe `safetensors` format** — No pickle risk.
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---
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## 🚀 How to Use (Safetensors)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_name = "Shrijanagain/TIGER-OM"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True
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)
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prompt = """You are SKT-OM, an advanced agentic AI with Think Mode enabled.
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User Query: Calculate training cost comparison and suggest best option..."""
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=1024,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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repetition_penalty=1.1
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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## 🔗 Important Links
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- **Live Demo**: [SKT-OM Space](https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/SKT-OM)
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- **GGUF Quantized (Q4_K_M)**: [Shrijanagain/TIGER-GGUF](https://huggingface.co/Shrijanagain/TIGER-GGUF)
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- **GitHub (RAG + ADK Code)**: [SHRIJANAGAIN/SKT-AMD-FILES](https://github.com/SHRIJANAGAIN/SKT-AMD-FILES)
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---
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## 🛠️ Technologies & Stack
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- **Base Models**: Shrijanagain/ST-X-0 + Mistral-7B Experts
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- **RAG**: SKT RAG + AMD ADK Kit
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- **Agents**: LangGraph
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- **Hardware**: AMD MI300X + ROCm 7.0
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- **Inference**: vLLM (FP16) + transformers (Safetensors)
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- **Training**: AMD Developer Cloud
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---
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## ⚡ Performance
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- Excellent balance of **quality vs efficiency** due to MoE architecture
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- Strong performance on reasoning, tool-use, code, and multi-step tasks
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- Significantly lower inference cost compared to dense 13B+ models
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---
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## 📌 Use Cases
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- Complex technical Q&A
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- Agentic workflows & tool calling
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- Research assistance
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- Code generation & debugging
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- Mathematical & logical reasoning
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- Comparative analysis
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- Data analysis with plugins
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---
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## 🏆 Hackathon
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**AMD Developer Hackathon 2026**
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Trained entirely on **AMD Developer Cloud**
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Fully built in public with multiple technical updates.
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---
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## 📄 License
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MIT License
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---
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