📊 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

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🌟 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

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🏗️ 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.

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📁 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.

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🚀 How to Use (Safetensors)

python
Copy code
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))

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🔗 Important Links

• Live Demo: SKT-OM Space
• GGUF Quantized (Q4_K_M): Shrijanagain/TIGER-GGUF
• GitHub (RAG + ADK Code): SHRIJANAGAIN/SKT-AMD-FILES

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🛠️ 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

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⚡ 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

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📌 Use Cases

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

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🏆 Hackathon

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

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📄 License

MIT License

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