joinus = """
## Join us :
🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/qdfnvSPcqP) On 🤗Huggingface:[MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [Build Tonic](https://git.tonic-ai.com/contribute)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
"""

title =  """# 🙋🏻‍♂️Welcome to Tonic's 🤖 OpenReasoning-Nemotron-14B Demo 🚀"""

description = """nvidia/🤖OpenReasoning-Nemotron-14B is a reasoning model that is post-trained for reasoning about math, code and science solution generation. It demonstrates exceptional performance across challenging reasoning benchmarks.
"""

presentation1 = """Try this model on [Hugging Face](https://huggingface.co/nvidia/OpenReasoning-Nemotron-14B).

OpenReasoning-Nemotron-14B is a large language model (LLM) which is a derivative of Qwen2.5-14B-Instruct. It is a reasoning model that is post-trained for reasoning about math, code and science solution generation. This model has been evaluated with up to 64K output tokens. The OpenReasoning model is available in the following sizes: 1.5B, 7B, 14B and 32B.

The models demonstrate exceptional performance across a suite of challenging reasoning benchmarks. The 14B model consistently sets new state-of-the-art records for its size class, achieving:
- **AIME24**: 87.8% pass@1
- **AIME25**: 82.0% pass@1  
- **HMMT Feb 25**: 71.2% pass@1
- **LiveCodeBench v6**: 67.9% pass@1
- **GPQA**: 71.6% pass@1
- **MMLU-PRO**: 77.5% pass@1

### License

Creative Commons Attribution 4.0 International License (CC-BY-4.0) with Apache 2.0 License"""

presentation2 = """
###  Model Architecture

🤖OpenReasoning-Nemotron-14B uses a dense decoder-only Transformer architecture based on Qwen2.5-14B-Instruct. It has 14B model parameters and supports up to 64,000 output tokens for extended reasoning chains.

**Architecture Type:** Dense decoder-only Transformer model  
**Network Architecture:** Qwen2.5-14B-Instruct  
**Model Size:** 14B parameters  
**Max Output Tokens:** 64,000 """

customtool = """{
  "name": "custom_tool",
  "description": "A custom tool defined by the user",
  "parameters": {
    "type": "object",
    "properties": {
      "param1": {
        "type": "string",
        "description": "First parameter of the custom tool"
      },
      "param2": {
        "type": "string",
        "description": "Second parameter of the custom tool"
      }
    },
    "required": ["param1"]
  }
}"""

example = """{{
  "name": "get_current_weather",
  "description": "Get the current weather in a given location",
  "parameters": {{
    "type": "object",
    "properties": {{
      "location": {{
        "type": "string",
        "description": "The city and state, e.g. San Francisco, CA"
      }},
      "unit": {{
        "type": "string",
        "enum": ["celsius", "fahrenheit"]
      }}
    }},
    "required": ["location"]
  }}
}}"""