Hermes 3 - Llama-3.1 70B
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Hermes 3 Llama 3.1 70B
Overview :
Hermes 3, developed by Nous Research, is the latest large language model (LLM) in the Hermes series. Compared to Hermes 2, it shows significant improvements in agent capabilities, role-playing, reasoning, multi-turn dialogue, and long text coherence. The core philosophy of the Hermes series is to align the LLM with the user, providing end-users with strong guidance capabilities and control. Building on Hermes 2, Hermes 3 further enhances function calling and structured output capabilities, improving its general assistant capabilities and code generation skills.
Target Users :
The target audience includes developers, data scientists, and AI researchers. Hermes 3 is particularly suitable for professionals who need to handle complex language tasks and build applications based on language models due to its powerful language understanding and generation capabilities.
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Use Cases
- Use Hermes 3 to generate technical documentation or tutorials.
- Utilize the model for fundamental stock data analysis.
- Create interactive stories or games using the model's role-playing functionality.
Features
- Advanced agent capabilities: Hermes 3 excels in agent tasks, capable of understanding and executing complex instructions.
- Role-playing and multi-turn dialogue: The model has significantly improved in role-playing and maintaining the coherence of multi-turn conversations.
- Long text coherence: Hermes 3 performs better in maintaining contextual coherence when handling lengthy texts.
- Function calling: Hermes 3 enhances practical usability by supporting function calls through specific system prompts.
- Structured output: The model generates structured outputs that conform to specific JSON schemas, facilitating integration and usage by developers.
- Code generation: Hermes 3 has improved in code generation, assisting developers in writing code more efficiently.
How to Use
1. Visit the Hugging Face website and search for the 'Hermes 3 - Llama-3.1 70B' model.
2. Read the model card to understand its details and technical report.
3. Choose the appropriate prompt format, such as function calls or JSON schema, based on the required functionalities.
4. Prepare the input data, formatting it according to the model requirements.
5. Use the code examples provided by Hugging Face for model inference to generate the desired output.
6. Integrate the model output into your own applications or services for automation and intelligent features.
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